From Insight to Impact - How Applying What You Read Makes You a Better Leader

Like many aspiring leaders, I once believed that reading business books from cover to cover would somehow make me a better leader. I highlighted key lessons, absorbed powerful insights, and felt a sense of accomplishment just by finishing them. But over time, I realized that simply reading wasn’t enough. Not even close.

The true shift happened when I started asking myself a simple but powerful question during my second read-through: “How will I change my behavior because of this?” That question marked the beginning of a deeper transformation. I started highlighting not just what was interesting, but what resonated with my strengths. I wrote down how I would apply those lessons. That’s when the real work began.

This is where so many well-meaning leaders lose their way. They think that reading a book makes them better. But the truth is: until you apply what you’ve learned, you haven’t even started the journey. Leadership isn’t about collecting ideas; it’s about changing how you show up every day. Knowledge is only the beginning—what you do with it is what defines your growth.

I made a list of changes I was going to make. I shared it with others. I asked for guidance from people who had walked the same path, who had wrestled with the same questions. I wanted to know what worked for them, and what didn’t. Those conversations kept me honest and helped me stay committed.

Of course, it wasn’t easy. In the early days of leadership, I was consumed by chaos. There were always more problems than hours in the day. I felt like being a student was a luxury I couldn’t afford. I was just trying to survive—just trying to keep the lights on. I believed that grit, hustle, and resilience were enough. I thought I could lead by simply working harder than everyone else—being the first one in and the last one out. I told myself that work ethic and charisma would carry me through.

I was wrong.

I had the instincts. I had the drive. But I lacked the discipline to grow intentionally. I knew how to sound smart in meetings. I could drop the right buzzwords and fake confidence when I needed to. But deep down, I knew it couldn’t last. One particularly tough meeting opened my eyes. I realized I had to change—not just how I worked, but how I learned.

Leadership isn’t about having all the answers. It’s about knowing your strengths and relentlessly refining them. It’s about recognizing your weaknesses and surrounding yourself with people whose strengths complement your own. Most people understand that in theory, but few commit to the practice. And that’s what separates good leaders from great ones.

Over time, I learned that leadership requires you to stay a student—forever. It demands humility. It demands consistency. And it demands the courage to keep learning, even when you feel like you should already know it all. I’m still on that journey. I’m still reading, still reflecting, still asking how I’ll change my behavior because of what I’ve learned.

The biggest mistake I made was believing that learning was enough. It’s not. The real transformation lies in the application. That’s where growth happens. That’s where leadership is born—not in the pages of a book, but in the choices you make after you close it.

I’ve been clumsily applying what I’ve learned from the greatest minds in business, and in doing so, I’ve slowly begun to shape a leadership style that’s my own. I’m deeply grateful for the thought leaders who’ve lit the path. And I remain humble—and hungry—to keep learning, keep applying, and keep growing.

Because leadership isn’t something you claim. It’s something you earn—every single day.

從洞察到影響力 -應用所學如何讓你成為更好的領導者

像許多有抱負的領導者一樣，我曾經以為只要把商業書籍從頭讀到尾，就能讓自己成為更好的領導者。我畫重點、吸收箴言，對自己讀完一本書感到滿足。但隨著時間推移，我逐漸意識到：光是閱讀，遠遠不夠。

真正的轉捩點，是當我在第二次閱讀時開始問自己一個簡單但深刻的問題： 「我會因為這段話改變自己的行為嗎？」 這個問題，讓我的思維產生了質變。我開始不只劃出有趣的內容，而是找出與我優勢相符的觀點，並寫下我會如何實踐。從那一刻起，真正的功課才開始。

許多有心的領導者常在這一步迷失。他們以為讀完一本書，就代表自己進步了。但事實是：在你真正應用學到的知識之前，你甚至還沒開始成長。領導不是知識的累積，而是你每天如何展現這些知識的結果。學習只是起點，真正塑造你的是你的行動。

我列下因閱讀而決定要改變的具體行為，並與他人分享。我向那些走過相同道路的人請教，他們也曾經歷一樣的掙扎。我想知道他們實踐後的成果與失敗，這些交流讓我更加堅定地走下去。

當然，這過程並不輕鬆。領導的早期階段充滿混亂，總有解不完的問題，時間永遠不夠用。我曾經覺得學習是奢侈，因為我每天都在忙著讓公司運作。我以為，只要有堅韌與拼勁就夠了。我努力工作，比任何人都早到、晚走，認為只要夠努力、夠有魅力，就能成為好領導。

我錯了。

我有本能、有幹勁，但我缺乏刻意成長的紀律。我知道怎麼在會議中講得頭頭是道，也會用流行語術掩飾自己的不安。但我心裡知道，這樣撐不久。一次會議讓我徹底清醒。我開始明白，自己需要的不只是努力，而是徹底改變學習的方式。

真正的領導，並不是什麼都懂，而是清楚知道自己的強項，並持續精進。同時也能正視自己的弱點，並建立一支團隊，成員的強項能互補而非重複。很多人明白這個道理，但真正能持續實踐的人卻不多。而這，正是卓越領導者與一般領導者的分水嶺。

我學到，領導者必須永遠保持「學生心態」。要謙虛、要穩定、要有勇氣在任何階段都不斷學習，即使你已經身居高位。我至今仍在這條路上，依然閱讀、反思，每一次都問自己：「我會因此改變嗎？」

我曾經犯過最大的錯誤，就是以為學習本身就夠了。事實不是這樣。真正的轉變，發生在你把學到的知識「付諸行動」的時候。那才是成長的起點，領導力也是在這裡誕生的——不是在書頁中，而是在你闔上書本後的每一個選擇裡。

我一路跌跌撞撞地應用從頂尖商業思想家那裡學來的觀念，也正是在這樣的實踐中，我慢慢地發展出屬於自己的領導風格。我由衷感激這些知識領袖為我指引方向，也謙遜地持續尋找新的導師、新的啟發。

因為領導力，從來不是一張證書或頭銜。 它是一場每天都要重新贏得的修練。

Monte Carlo Method - From Statistics to Smart AI Agents

The Monte Carlo Method is one of the most powerful and versatile tools in the world of computation and statistics. Though it might sound like a gambling strategy from a casino, it's actually a rigorous and indispensable approach for solving complex problems through randomness and probability. In this blog post, we’ll explore what the Monte Carlo Method is, why it matters, and how it powers applications like game-playing AI through Monte Carlo Tree Search (MCTS).

What is the Monte Carlo Method?

The Monte Carlo Method refers to a class of computational algorithms that rely on repeated random sampling to obtain numerical results. The core idea is to simulate a system or process many times over and analyze the outcomes to make estimations or predictions.

Instead of trying to solve a complex problem analytically—especially when a closed-form solution is impossible—the Monte Carlo approach uses probabilistic simulation. This makes it ideal for high-dimensional, non-deterministic, or chaotic systems.

A Simple Example

Let’s estimate the value of π using a Monte Carlo method:

1. Imagine a square enclosing a quarter circle.
2. Randomly throw points into the square.
3. Count how many fall inside the quarter circle vs. the whole square.
4. The ratio of the points in the circle to the square approximates π/4.

Multiply that ratio by 4, and you get an approximation of π.

Why is it Important in Statistics?

In statistics, the Monte Carlo method is vital for:

• Simulating distributions: Especially when analytical forms are unavailable.
• Solving integrals: Particularly in high dimensions where traditional methods fail.
• Risk analysis and forecasting: By simulating scenarios with random variables (e.g., financial models).
• Bayesian inference: Monte Carlo methods underpin techniques like Markov Chain Monte Carlo (MCMC), essential for posterior sampling in Bayesian analysis.

Applications in Artificial Intelligence

The Monte Carlo method has had a profound impact on AI, especially in areas involving uncertainty, exploration, and decision-making.

1. Monte Carlo Tree Search (MCTS)

One of the most notable applications is Monte Carlo Tree Search, a heuristic search algorithm used for decision processes, particularly in games.

How MCTS Works:

MCTS is used to determine the best move by simulating many random playouts of a game. It balances two core principles:

• Exploration: Trying out less-visited branches to discover potentially better outcomes.
• Exploitation: Favoring branches that have historically yielded good results.

The process involves four steps:

1. Selection: Traverse the tree from root to leaf using a selection policy (e.g., UCT: Upper Confidence Bound for Trees).
2. Expansion: Add a new child node to the tree.
3. Simulation: Run a random simulation from this new node to the end of the game.
4. Backpropagation: Update the nodes on the path based on the result.

MCTS powered DeepMind’s AlphaGo, which defeated world champions in Go—a game considered intractable for traditional AI approaches due to its immense search space.

2. AI Agents and Planning

Beyond games, Monte Carlo methods help AI agents deal with uncertain environments and incomplete information. In reinforcement learning, for example:

• Monte Carlo methods can estimate the expected return by sampling episodes.
• They're useful in policy evaluation and improvement when the environment model is not known.
• Partially Observable Markov Decision Processes (POMDPs) often rely on Monte Carlo simulations for belief updates and planning.

Other Use Cases

• Physics: Simulating particle interactions.
• Finance: Valuation of derivatives, portfolio risk analysis.
• Robotics: Localization and mapping (e.g., Monte Carlo Localization).
• Medicine: Dose distribution modeling in radiation therapy.

Final Thoughts

The Monte Carlo Method’s brilliance lies in its simplicity and flexibility. By embracing randomness, it offers a practical way to approximate solutions to problems that are otherwise unsolvable. From theoretical statistics to high-performance AI systems, its impact is far-reaching—and as computing power grows, its relevance only continues to increase.

蒙地卡羅方法 - 從統計學到智慧型 AI 智能代理

蒙地卡羅方法（Monte Carlo Method） 是計算與統計領域中最強大且用途廣泛的工具之一。雖然它的名字讓人聯想到賭場策略，但其實這是一套嚴謹且極具實用性的隨機模擬方法，用來解決各種複雜問題。本文將介紹什麼是蒙地卡羅方法、它為何如此重要，以及它在遊戲人工智慧（如蒙地卡羅樹搜尋）與智慧代理中的應用。

什麼是蒙地卡羅方法？

蒙地卡羅方法是一類基於**重複隨機抽樣（random sampling）**來獲得數值解的計算演算法。核心思想是：透過大量模擬實驗來逼近實際結果，尤其是在解析解不可得的情況下。

換句話說，與其嘗試使用代數或微積分精確求解複雜問題，不如使用機率與統計的力量進行模擬與估計。

簡單範例

假設我們想用蒙地卡羅方法估算 π 值：

1. 想像一個正方形中內切一個四分之一圓。
2. 在正方形中隨機投點。
3. 計算落在四分之一圓內的點數與總點數的比例。
4. 此比例約為 π/4，乘以 4 即可估算 π。

為何在統計學中如此重要？

在統計學中，蒙地卡羅方法用於：

• 模擬機率分布：當分布無法用解析式表示時特別有用。
• 解高維積分：傳統數值積分法在高維空間效率低下，而蒙地卡羅方法則仍可適用。
• 風險分析與預測：例如財務模型中的不確定性模擬。
• 貝式推論：如 Markov Chain Monte Carlo（MCMC）在後驗分布取樣中的應用。

在人工智慧中的應用

蒙地卡羅方法在人工智慧中同樣扮演關鍵角色，尤其在不確定性處理、策略搜尋、與決策制定方面。

1. 蒙地卡羅樹搜尋（MCTS）

最著名的應用之一是 Monte Carlo Tree Search（蒙地卡羅樹搜尋），這是一種啟發式搜尋演算法，常用於策略型遊戲與決策系統。

MCTS 的工作流程：

MCTS 藉由模擬大量隨機遊戲進行來選擇最佳決策，其核心在於平衡：

• 探索（exploration）：嘗試新路徑以發現潛在好結果。
• 利用（exploitation）：傾向選擇過去表現佳的選項。

整體流程包含四個步驟：

1. 選擇（Selection）：根據策略從根節點往下選擇子節點。
2. 擴展（Expansion）：新增一個尚未擴展的子節點。
3. 模擬（Simulation）：從該節點進行隨機遊戲模擬至終局。
4. 回傳（Backpropagation）：將結果反向更新至路徑上的節點。

MCTS 是 DeepMind 的 AlphaGo 所採用的核心技術之一，幫助其在複雜的圍棋遊戲中擊敗世界冠軍。

2. 智慧型代理與規劃

在強化學習與智慧代理領域中，蒙地卡羅方法有以下應用：

• 估算回報值：透過樣本來估計策略的預期效益。
• 策略評估與改進：在未知環境下進行政策迭代。
• 部分可觀測馬可夫決策過程（POMDP）：透過蒙地卡羅模擬來進行信念更新與決策。

其他應用範疇

• 物理學：模擬粒子交互與能量分布。
• 金融工程：衍生品定價、風險模型。
• 機器人學：如蒙地卡羅定位（MCL）。
• 醫學：放射治療中的劑量分布模擬。

結語

蒙地卡羅方法的精妙之處，在於它將隨機性變為解題工具。當問題過於複雜、無法解析時，它提供一條可行的數值近似之路。從統計推論到智慧代理，從遊戲 AI 到財務模型，蒙地卡羅方法不僅是數學的藝術，更是現代科學與工程的基石之一。

Regulatory Frameworks for Core Banking Systems in Southeast Asia

Core banking system vendors entering Southeast Asian markets must navigate a complex web of banking regulations and technology risk guidelines. Each country – Singapore, Vietnam, Thailand, Malaysia, and Indonesia – has its own regulatory bodies and frameworks governing financial technology. These rules affect retail and commercial banks, new digital-only banks, and in some markets, Islamic banks. Key considerations include data residency, personal data protection, system uptime requirements, notification duties, Shariah compliance, cybersecurity standards, and recommended certifications. This report compares the regulatory landscape across the five countries, highlighting country-specific requirements and strictness, to inform a core banking provider’s strategic compliance planning.

Singapore

Regulatory Body: The Monetary Authority of Singapore (MAS) oversees banks and technology risk. MAS is the central bank and unified financial regulator. Singapore also has the Personal Data Protection Commission (PDPC) for personal data laws.

Key Frameworks and Guidelines: MAS has issued comprehensive guidelines on technology risk and outsourcing:

• Technology Risk Management (TRM) Guidelines (2021): Detailed best practices for managing IT risks. Accompanied by MAS Notice on TRM (Notice FSM-N05, 2024) which imposes binding requirements on banks.
• Outsourcing Guidelines (2016): Requirements for risk management of outsourced services, including cloud computing. Banks must ensure service providers (e.g. core banking vendors) meet MAS’s expectations for confidentiality, security, and regulator access.
• Business Continuity Management: MAS expects robust BC planning; critical systems must have quick recovery (aligned with TRM requirements).
• Digital Bank Framework: Digital banks licensed by MAS must comply with the same MAS rules as traditional banks, with emphasis on strong IT governance.

Data Residency and Local Infrastructure: Singapore does not mandate local data centers for banks. MAS permits cloud and cross-border data outsourcing but requires rigorous risk assessments and controls. Banks must ensure that outsourcing abroad does not hinder MAS’s supervisory access or violate banking secrecy. Under Singapore’s Personal Data Protection Act (PDPA), personal data can only be transferred overseas if equivalent protection is assured or with consent. This means core banking vendors can host data outside Singapore provided they uphold PDPA standards and the bank has confidence in data security and availability. There is no blanket data localization rule, reflecting Singapore’s generally flexible yet risk-based approach.

Handling of Personal Data (PII): Banks in Singapore must comply with the PDPA 2012 for customer personal data. This entails obtaining consent for data collection and using personal data only for stated purposes. Banks also abide by Banking Act confidentiality provisions which impose strict bank secrecy (customer financial information cannot be disclosed without consent or legal basis). Core system vendors handling bank customer data are typically bound by contractual and legal confidentiality to meet these laws. MAS guidelines require banks to ensure third-party vendors protect customer information from unauthorized access or disclosure. In practice, vendors should implement strong data encryption, access controls, and data segregation when serving Singapore banks.

Data Storage and Retention: Singaporean banks are required to retain transaction and customer records for set minimum periods, both for regulatory compliance and audit. For example, MAS anti-money-laundering rules mandate keeping records (e.g. customer due diligence, transactions) for at least 5 years after the transaction or account closure. This ensures auditability. Core banking systems must facilitate archival of data and logs in compliance with these retention periods. Data can be kept in electronic form as long as it’s admissible in court. Banks also need to maintain audit trails of system activities, with MAS expecting timely retrieval of records during inspections.

System Uptime and Downtime Penalties: MAS has very strict requirements on core system availability. Unscheduled downtime for each critical system must not exceed 4 hours in any 12-month period. Banks are also required to recover any critical banking service within 4 hours of disruption (Recovery Time Objective of 4 hours). These rules mean a core banking outage cannot last long, nor happen frequently, without breaching regulations. MAS takes supervisory action if a bank exceeds these limits. For example, after a major outage, MAS forced a bank to engage independent experts and even imposed additional capital requirements as a penalty. Core banking vendors must design highly resilient systems (with redundancy, failover, etc.) to meet Singapore’s stringent uptime standards.

Notification of Downtimes or Changes: MAS requires incident reporting within 1 hour of discovery of a major system incident. Banks must notify MAS promptly about outages or severe system malfunctions. Planned maintenance downtimes are generally managed by banks internally, but if a scheduled change is significant (e.g. a core system replacement or major downtime affecting customers), banks typically inform MAS as a courtesy and ensure customer communication. Under MAS Outsourcing rules, any material system outsourcing or migration (like moving core banking to a new vendor or cloud) should be communicated to MAS early. While MAS approval is not formally required in most cases, regulators expect advance engagement with MAS on major outsourcing arrangements to ensure risks are addressed. In summary, unexpected issues must be reported immediately, and big changes should not surprise the regulator.

Shariah Compliance: Singapore’s banking sector has a limited Islamic banking presence. There are no specific Shariah regulations for core banking IT because Islamic banking is not a major segment. Any Islamic banking products offered would be under MAS’s general framework. Thus, no special Shariah compliance requirements apply to core system vendors in Singapore beyond conventional risk and compliance checks. (By contrast, Malaysia and Indonesia have detailed Islamic finance regimes – see their sections below.)

Cybersecurity and IT Risk Guidelines: MAS is known for robust cybersecurity guidelines that cover banks and extend to their vendors:

• MAS TRM Guidelines devote extensive sections to cybersecurity controls (access management, incident response, cryptography, etc.). MAS explicitly mandates certain baseline controls via the Notice on Cyber Hygiene (e.g. multi-factor authentication, timely security patching, malware protection, secure admin accounts). Banks must ensure these controls apply to outsourced systems as well.
• Incident Response: Banks must have 24/7 monitoring and an incident response plan. Significant cyber incidents (like system breaches or attacks) must be reported to MAS within hours and a detailed root-cause analysis submitted within 14 days.
• Penetration Testing and Audits: MAS expects regular independent vulnerability assessments of critical systems. Vendors providing core platforms might be asked to undergo penetration tests or provide service organization control (SOC) reports.
• Regulator Access: Under outsourcing rules, MAS reserves the right to audit service providers. Core banking vendors may be required to grant MAS inspectors access to their operations or independent audit reports. Overall, Singapore’s regime emphasizes international standards and best practices in cybersecurity. The MAS TRM guidelines align with ISO/IEC 27001 controls, and MAS encourages banks to leverage standards like NIST or ISO for their security frameworks.

Certifications and Standards: While not explicitly mandated by law, Singapore banks often require their tech vendors to have internationally recognized certifications. ISO/IEC 27001 (information security management) is commonly expected to demonstrate strong security controls. For any solutions dealing with payment card data, PCI DSS compliance is necessary (per card network rules, supported by MAS expectations). MAS itself references standards like ISO 27001, 27017 (cloud security), 27018 (cloud privacy) and PCI-DSS as relevant benchmarks. Additionally, having ISO 22301 (business continuity) or ISO 27032 (cybersecurity) can bolster a vendor’s credibility. In summary, certifications are highly recommended in Singapore as proof of alignment with best practices, even if not legally required.

Vietnam

Regulatory Body: The State Bank of Vietnam (SBV) is the central bank and principal regulator for banking activities. The SBV issues regulations on banking operations and IT risk for banks. In addition, the Ministry of Public Security (MPS) plays a role in cybersecurity (enforcing the Cybersecurity Law), and the new Personal Data Protection Commission (under the Ministry of Public Security) oversees personal data protection rules.

Key Frameworks and Guidelines: Vietnam’s regulatory framework for bank IT has evolved significantly in recent years:

• SBV Circular No. 18/2018/TT-NHNN: A crucial regulation on “assurance of information systems safety and security in banking operations”. Effective 2019, this circular updated prior rules and introduced requirements for system classification and cloud usage. It classifies bank IT systems by importance (Level 1 = normal, Level 2 = important, Level 3 = especially important) with corresponding uptime requirements.
• Cloud Computing Regulations: Circular 18/2018 imposes specific conditions before a bank can use third-party cloud services. Banks must conduct IT risk assessments and classify which operations will go on cloud. They must also set criteria for cloud provider selection (e.g. provider must be an enterprise established in Vietnam). Contracts with cloud vendors must include provisions for the vendor to provide IT audit reports and allow supervision of service quality. These rules directly affect core banking vendors offering cloud-based systems to Vietnamese banks – local presence and auditability are key.
• Law on Cybersecurity (2018) & Decree 53/2022: These impose data security and localization obligations on certain services. Banks, being critical infrastructure, are expected to comply with any cybersecurity requirements such as local data storage for sensitive data and cooperation with authorities. (Details under Data Residency below.)
• Draft Personal Data Protection Law (Decree 13/2023): Vietnam’s first comprehensive personal data protection regulations, effective 2023, set out how PII must be handled (consent, purpose limitation, etc.). Banks must align their data processing with these rules.

Data Residency and Local Data Centers: Vietnam has strict data localization tendencies. Under the Cybersecurity Law and Decree 53, both domestic and foreign service providers in Vietnam are required to store certain data (including personal information of Vietnamese users) on servers located in Vietnam. For foreign companies, if authorities determine their services are used to violate the law or they don’t cooperate with law enforcement, they can be ordered to locate data and a local office in Vietnam within 12 months. In practice, virtually all Vietnamese banks keep their core banking systems and customer data in-country. Circular 18/2018 reinforces this by effectively requiring cloud providers for banks to be locally incorporated, which implies using local data centers. There is no explicit SBV rule outright banning offshore hosting, but the layered regulations make overseas core banking deployment impractical. A core banking vendor should plan to utilize Vietnam-based data centers or partner with local cloud providers when serving this market.

Handling of Personal Data (PII): Vietnam’s legal regime for PII is rapidly maturing. The new Personal Data Protection Decree (13/2023) establishes principles for processing personal data: requiring consent for collection/use of personal data, providing rights for data subjects, and placing restrictions on sensitive data. Although as of 2025 Vietnam doesn’t have a standalone PDPA law like others, this decree functions similarly. Banks must secure customers’ personal and financial data, and vendors processing such data must implement protective measures (encryption, access controls, etc.) to comply. Separately, the Law on Cybersecurity also requires organizations to protect users’ personal information and to verify and secure user data. In sum, core banking vendors in Vietnam should treat customer PII with GDPR-level care – ensure consent is obtained by the bank, data is stored securely (preferably in Vietnam), and not shared without authorization.

Data Storage and Retention: Vietnamese regulations require banks to maintain thorough records for audit and oversight. Circular 18/2018 itself classifies data by confidentiality but does not specify exact retention periods. Other laws fill the gap: for instance, the Law on Credit Institutions and SBV guidelines likely mandate retaining accounting and transaction records for a number of years (often 10 years for financial records in Vietnam’s banking practice). Additionally, anti-money-laundering rules (e.g. Vietnam’s AML Decree) typically require banks to keep customer identification and transaction logs for at least 5 years. Core banking systems should support archival of historical data to meet these retention requirements and provide readily accessible audit trails. Given the regulatory emphasis on security, vendors may need to implement secure backup and recovery solutions (possibly with backups stored domestically as well).

System Downtime and Reliability: Vietnam’s SBV expects high availability for critical banking systems, though the rules are framed slightly differently from Singapore’s. Under Circular 18’s classification: an “Important Information System” (Level 2) is defined in part by having non-operating (downtime) periods not exceeding 4 working hours. This implies that for any important banking system (which would include core banking), any planned downtime should be under 4 hours and availability should be around the clock for customer-facing services. In practice, Vietnamese banks strive for minimal downtime; prolonged outages could draw SBV scrutiny. While there isn’t a published numeric penalty (like MAS’s 4-hour rule), an outage beyond a few hours or a pattern of instability would likely trigger regulatory intervention. Vendors should ensure robust failover and quick disaster recovery for deployments in Vietnam.

Regulatory Notifications: Banks in Vietnam are generally required to report major incidents to the SBV, especially if they affect customer services or data security. For instance, a significant core system failure or data breach would need to be communicated to the SBV promptly (though specific time frames may be defined in internal SBV guidelines rather than publicly available rules). Circular 18/2018 obliges banks to have incident response processes, and it’s expected that banks notify the SBV of incidents that could disrupt operations or compromise data. Scheduled changes such as a core banking migration would typically require SBV approval or notification if they fall under large-scale IT projects. While Vietnam doesn’t have a formal “notify X hours in advance” rule for planned downtime, banks often coordinate with SBV for major system go-lives or migrations, to ensure regulatory comfort. In summary, any core system vendor should be prepared that their client bank might ask for detailed risk assessments to submit to SBV before using the vendor’s solution (especially if it’s a cloud-based core or a significant change).

Shariah Compliance: Vietnam does not have Islamic banking as part of its mainstream financial system, hence no Shariah-specific regulations apply. There are no Islamic banks in Vietnam requiring Shariah governance. Core banking vendors do not need to account for Islamic finance principles in this market. The focus remains on conventional banking compliance only.

Cybersecurity Guidelines: Vietnamese regulators enforce cybersecurity through a combination of SBV directives and national law:

• SBV IT Security Circular 18/2018: Imposes requirements for security controls based on system classification. For example, higher-classified systems must implement stronger access control, monitoring, and encryption measures. Banks must also periodically evaluate vulnerabilities and have an information security committee in place. The Circular introduced formal requirements for third-party cloud security (risk assessments, audits) as noted above.
• National Cybersecurity Law (2018): Treats banking as critical infrastructure, so banks must conform to standards set by the government for network security. This can include mandatory annual network security exercises and audits by authorities. Banks may be required to undergo inspections by the MPS for cyber readiness. Vendors, consequently, might have to cooperate with these audits or information requests through their client bank.
• Cyber Incident Response: Banks are expected to report cyber incidents (like data breaches) to the authorities. The law and subsequent regulations (Decree 85/2016 on information system security by classification) likely compel banks to notify regulators of incidents affecting “important” or “especially important” systems. A core banking breach would fall in this category, necessitating immediate containment and notification.
• Standards Alignment: Vietnam has been moving towards international standards – for example, many banks adopt ISO 27001 and PCI DSS on a voluntary basis. The SBV has encouraged improving cyber maturity; indeed, a recent SBV strategy aims for most banks to use cloud and modern security by 2025. While not explicitly codified, using standards like ISO 27001 or following frameworks like PCI DSS for card data can demonstrate compliance with the broad requirements of ensuring data security and safety.

Certifications and Standards: Vietnamese regulations do not mandate specific certifications, but in practice banks prefer vendors with strong credentials. ISO/IEC 27001 certification (or equivalent security attestations) for a core banking vendor can be a significant differentiator when SBV approval is needed for using that vendor. Circular 18/2018’s requirement that a cloud provider furnish IT audit reports suggests that independent audits (e.g. SSAE18 SOC 2 reports or ISO 27001 certificates) are expected to verify the provider’s security. For handling card data in core banking, compliance with PCI DSS is necessary (Vietnam’s card networks and international networks require it). Additionally, Vietnam’s approach to data privacy is aligning somewhat with global norms, so adherence to standards like ISO/IEC 27701 (privacy information management) could become relevant. In summary, while not explicitly required by law, demonstrating adherence to international standards greatly facilitates regulatory approval in Vietnam’s banking sector.

Thailand

Regulatory Body: The Bank of Thailand (BOT) is the central bank and main regulator for banks and financial services. The BOT issues policy guidelines and notifications that banks must follow. Thailand also established a Personal Data Protection Committee (PDPC) under the PDPA for personal data issues, and a National Cybersecurity Committee under the Cybersecurity Act (2019) that oversees critical information infrastructure, including banking.

Key Frameworks and Guidelines: Thai banks operate under a range of regulations that impact core banking systems:

• BOT Notifications on IT Risk and Outsourcing: The BOT has specific rules for technology outsourcing by banks. Notably, Notification No. FPG 19/2559 (2016) on IT Outsourcing sets out conditions for using cloud services and third parties. It distinguishes between strategic functions and non-strategic functions, requiring prior BOT approval for outsourcing certain functions and ensuring providers meet qualification criteria. In essence, critical systems like core banking can be outsourced only if the bank maintains oversight and the vendor meets BOT standards (financial stability, competent service, etc.).
• IT Security Guidelines: The BOT issued policies on IT security measures (e.g., an earlier notification SorNorSor 8/2557 and updates) which outline baseline security controls for banks. These align with international practices in access control, data security, and network resilience.
• Bank of Thailand Virtual Bank Framework (2022–2023): Thailand is introducing digital-only banks. The BOT’s virtual bank licensing guidelines require applicants to have independent, robust IT systems. For instance, virtual banks must not share critical IT systems (like core banking) with other institutions, to ensure full control and accountability. They also must meet all existing IT risk regulations plus additional scrutiny on technology governance. This indicates that any vendor serving a virtual bank in Thailand will face close regulatory evaluation.
• Personal Data Protection Act (PDPA) 2019: Though not banking-specific, this law (effective June 2022) imposes obligations on banks as data controllers. It also influences how vendors handle customer data on behalf of banks.
• Cybersecurity Act 2019: Designates banking as critical infrastructure, meaning banks must implement cybersecurity standards and potentially submit to government cyber audits or drills.

Data Residency Requirements: Thailand does not have explicit data localization laws for banking as some neighbors do. Banks are allowed to use cloud or overseas data centers, provided they comply with BOT oversight requirements. Before using foreign-based services, a bank must ensure the provider can meet Thai regulations and that regulators and auditors will have access to data. In practice, many Thai banks keep primary systems in Thailand, but some use regional or global data centers for certain functions (with BOT’s knowledge). The PDPA adds a constraint on cross-border personal data transfers: personal data may not be transferred out of Thailand unless the destination country has adequate data protection standards or appropriate safeguards are in place. There are exemptions (such as customer consent, contract necessity, or approved Binding Corporate Rules). For a core banking vendor, this means if hosting Thai customer data abroad, they must ensure PDPA conditions are satisfied (often done via contracts with EU-standard clauses or obtaining consent). While BOT doesn’t mandate local hosting, it will look at risk, data criticality, and recovery – a core system holding critical data offshore might be acceptable only if strong controls and legal arrangements ensure Thai authorities’ access when needed.

Handling of Personal Data (PII): Thai banks must abide by the Personal Data Protection Act B.E. 2562 (2019). The PDPA is largely modeled on GDPR principles: requiring consent or other legal basis for data processing, giving individuals rights to access/correct their data, and imposing breach notification duties. Banks as data controllers must have agreements with any data processors (like an IT vendor) to ensure PDPA compliance. Core banking vendors handling customer PII need to implement appropriate security measures and possibly assist the bank in fulfilling data subject rights (e.g., retrieving or deleting data upon request). Under PDPA Section 28 and related regulations, cross-border data transfers require either an adequacy decision by the PDPC or use of standard contractual clauses/safeguards. As of 2025, the PDPC has begun issuing guidance (e.g., rules in late 2023 for cross-border transfer conditions). In summary, any core banking system for Thailand must support data privacy compliance – e.g., segregating personal data, allowing extraction for PDPA requests, and protecting data as per PDPA security standards.

Data Storage and Retention: Thai law and regulations require banks to retain certain data for defined periods. For instance, anti-money laundering laws compel banks to keep customer identification and transaction records for 5 years (common across many jurisdictions). Separately, the Thai Civil and Commercial Code and Revenue Code often effectively require financial records to be kept for 5–10 years. In practice, most Thai banks keep extensive archives (7 years is a common business practice for general records). The BOT may have specific rules for retention related to electronic banking transactions or audit trails, ensuring that records are available for supervisory review. Core banking systems in Thailand should therefore have features to store transaction history and customer account data for extended periods (often up to 7–10 years) either online or in backups, and be able to reproduce these records for regulators. The PDPA’s data minimization principle does require that personal data not be kept longer than necessary, but financial regulations usually override this by defining “necessary” as at least those regulatory minimum years. Banks will balance these by purging data after the required period. Vendors should allow configurable retention policies to meet both regulatory and PDPA requirements.

System Downtime and Reporting: The Bank of Thailand expects banks to manage IT reliability proactively, though it doesn’t publicly dictate a fixed maximum downtime like MAS. Instead, Thai regulators emphasize service continuity and prompt incident handling. Banks are required to report major IT disruptions to the BOT immediately, especially if customer-facing services (ATM networks, online banking, etc.) go down. The BOT has previously shown concern over repeated outages among Thai banks, pushing for improvements. For example, if a core banking failure led to a multi-hour nationwide outage, the BOT could demand a remediation plan or even take enforcement action for weak IT controls (this is somewhat analogous to how MAS reacts, though BOT’s thresholds might be less formally defined). Scheduled downtimes (like maintenance) typically must be done in agreed maintenance windows, and banks usually notify customers in advance. There isn’t a specific rule to notify the BOT of routine maintenance, but if a planned system upgrade is large-scale (such as a core banking replacement requiring a long cutover window), the BOT would expect to be informed as part of the oversight process. Overall, Thai banks have internal policies aligning to an IT Service Availability target (often 99.9% uptime for critical services). A vendor should design the core system for high availability (clustering, disaster recovery). Additionally, given the Cybersecurity Act, if an incident is due to a cyber-attack causing downtime, the bank might need to report it to the National CERT or cybersecurity regulators within a specified time. Vendors should support forensic analysis and timely recovery to help the bank fulfill these duties.

Shariah Compliance: Thailand has a very small Islamic banking sector (e.g. one state-owned Islamic Bank of Thailand). There are no extensive Shariah regulations for commercial banks as seen in Malaysia or Indonesia. The Islamic Bank of Thailand operates under a separate act but for general commercial banks, Shariah compliance is not a factor. Thus, core banking vendors in Thailand typically do not need to provide specialized Islamic banking modules unless specifically serving that one Islamic bank. For completeness, any service to the Islamic Bank of Thailand would need to allow operation without interest (using profit-sharing, etc.), but that is a niche case. In broad terms, Shariah requirements are not applicable in the Thai regulatory environment for core banking systems.

Cybersecurity Guidelines: Thailand’s approach to cybersecurity in banking is codified in both BOT guidelines and national law:

• BOT IT Security Policy: Banks must implement robust information security programs. The BOT expects banks to follow frameworks akin to ISO 27001. Controls like multi-factor authentication for sensitive transactions, encryption of sensitive data, and continuous monitoring are emphasized. The BOT issued guidance in 2020 requiring commercial banks to enhance cyber resilience (post some high-profile breaches in the region). Banks are also encouraged to conduct cyber drills and penetration tests regularly.
• Personal Data Security (PDPA): Section 6 of the PDPA and associated regulations require banks (and their processors) to maintain appropriate security measures to protect personal data from unauthorized or accidental access, alteration, or loss. The PDPC has published security standards that effectively align with ISO27001’s controls (access control, encryption, etc.). Non-compliance can lead to penalties under PDPA.
• Cybersecurity Act (2019): For critical infrastructures like banking, this law empowers the government to impose certain cybersecurity standards and incident reporting. Banks may need to undergo compliance audits and must report severe cyber incidents to the National Cybersecurity Agency. While this is more macro-level, a core banking vendor should be aware that any serious breach in a bank’s core system could involve government agencies beyond the BOT (for instance, the bank might have to allow government cyber investigators to review the system).
• Incident Reporting: Thai banks are generally expected to inform the BOT of cyber incidents immediately. The BOT often coordinates with the Thailand Computer Emergency Response Team (ThaiCERT) on sector-wide threats. Vendors may be called to assist in incident response. Overall, Thailand urges banks to benchmark against global standards. It’s common for Thai banks to achieve ISO/IEC 27001 certification for their IT operations and require key vendors to do the same. The BOT doesn’t list specific certifications in regulations, but having them is considered good practice. The BOT also participates in regional cyber resiliency initiatives, so banks in Thailand push their IT providers towards strong cybersecurity postures.

Certifications and Standards: Although not explicitly mandated by the BOT, having recognized certifications significantly eases compliance in Thailand. Many banks will prefer core banking vendors with ISO/IEC 27001 for information security and ISO/IEC 20000 for IT service management, reflecting a mature process. If the core banking system handles payment cards, PCI DSS compliance is a must (the Thai Bankers’ Association enforces PCI standards for any card-related systems). Furthermore, vendors might consider aligning with the Bank of Thailand’s IT Audit guidelines, which likely reference COBIT or similar frameworks; being certified or audited for those can be beneficial. The new digital banks are likely to demand certified systems since they have to prove to regulators that their outsourced technology meets top security and reliability standards. In summary, while Thai regulations don’t explicitly list certifications, the market expectation is that vendors adhere to international standards (with ISO 27001 and PCI DSS being most pertinent) to ensure trust and smooth regulatory approval.

Malaysia

Regulatory Body: Bank Negara Malaysia (BNM) is the central bank and regulator for banking and insurance. BNM is very active in issuing detailed regulatory policy documents that banks must follow. Malaysia also has a dedicated Islamic banking regulatory framework, as well as a Personal Data Protection Department (under the communications ministry) enforcing personal data laws.

Key Frameworks and Guidelines: Malaysia’s regulations affecting core banking systems are particularly comprehensive:

• BNM Risk Management in Technology (RMiT), 2019 (updated 2023): A landmark policy document that lays out BNM’s requirements for FIs on technology risk management. RMiT covers governance, operations, cybersecurity, data center standards, and more. It explicitly addresses expectations for outsourcing and cloud in Appendix 10 (2023 update). Banks and their critical IT service providers must adhere to RMiT’s principles.
• BNM Outsourcing Guidelines (2018, updated): These rules require banks to obtain prior written approval from BNM before entering any new material outsourcing arrangement. A core banking system provided by a vendor usually qualifies as a material outsourcing of IT infrastructure. Thus, banks must submit an application to BNM and get consent before engaging a core system vendor or making major changes. Non-material outsourcings must still be recorded and subject to BNM review upon request.
• Shariah Governance Policy (2019): For Islamic banks, BNM’s Shariah Governance framework ensures all operations (including IT systems supporting Islamic products) comply with Shariah. Islamic banks must have Shariah Committee approvals for new products and possibly systems.
• Digital Banking Framework: BNM awarded digital bank licenses in 2022 with a framework that includes stringent technology risk expectations. These new digital banks must follow RMiT and show robust, secure IT architecture from day one.
• Personal Data Protection Act (PDPA) 2010: Though overseen by a different authority (JPDP), it applies to banks and their IT processing of personal data.

Data Residency and Local Data Centers: Malaysia historically leaned towards on-shore data hosting for banks, and this is evident in BNM’s stance. Under BNM RMiT, banks can outsource IT infrastructure including cloud, but BNM’s prior approval is required for material outsourcings, especially if they involve cross-border data transfer. BNM permits overseas outsourcing only if the bank addresses additional risks (country risk, access issues) and ensures the foreign host provides equal oversight and recovery capabilities. In practice, many Malaysian banks keep their core banking systems and primary data center domestically, often due to regulatory preference and data sovereignty concerns. BNM doesn’t outright ban foreign data centers, but conditions are stringent – for example, the bank must ensure regulators and auditors have full access to data even if stored abroad, and that data is not subject to foreign laws that breach Malaysian confidentiality. Additionally, the PDPA 2010 restricts transferring personal data overseas unless the recipient country is whitelisted by the government or certain safeguards (or consent) are in place. As of now, no official whitelist exists, so transfers require individual conditions (like consent or contractual clauses). Combining these, a core banking vendor offering a cloud solution to a Malaysian bank will likely need to set up a local data center or use a Malaysia availability zone to satisfy BNM. At minimum, keeping a secondary copy of data in Malaysia is often expected. Overall, data residency is a critical consideration: Malaysia stands out for insisting on local control, even if not an absolute mandate, through its approval process and risk requirements.

Handling of Personal Data (PII): Malaysia’s PDPA 2010 governs how banks and vendors treat personal data. Banks must obtain consent for personal data processing, disclose purposes, and protect the data against misuse. They also must honor individuals’ rights to access and correct their data. Under PDPA, personal data should not be kept longer than necessary, which ties into data retention policies. For vendors, this means they will be contractually obligated to implement PDPA-compliant measures: e.g., not using customer data for anything outside the bank’s instructions, providing reasonable security (the PDPA’s Security Principle), and notifying the bank of any data breaches. Notably, financial institutions in Malaysia are also bound by BNM’s secrecy provisions in the Financial Services Act and Islamic Financial Services Act, which prohibit disclosing customer information to unauthorized parties. Any core banking vendor must sign confidentiality undertakings to comply with these secrecy laws. Furthermore, if the vendor handles credit card data or other sensitive info, additional sectoral guidelines (like by Payments Network Malaysia for card security) come into play.

Data Storage and Retention: BNM’s expectations on record-keeping are strict. Under various guidelines (including AML/CFT rules and possibly RMiT), banks must retain transactional and customer records typically for at least 6 to 7 years. For example, BNM’s AML regulations (similar to MAS 626) require at least 6 years retention after a transaction or account is closed. The Malaysian Companies Act also mandates businesses to keep accounting records for 7 years. Therefore, core banking systems in Malaysia need to ensure no data is prematurely purged and that archival mechanisms are in place. BNM RMiT specifically mentions that banks should maintain complete and up-to-date records of system activities and ensure audit logs are retained to facilitate forensic investigations. Additionally, the Shariah Governance framework for Islamic banks may require documentation of Shariah-related decisions and transactions (e.g. contracts, profit calculations) to be kept for regulator or Shariah committee review. Vendors must accommodate these retention needs, possibly storing data encrypted if on cloud but accessible onshore when required.

System Downtime and Unscheduled Outages: BNM’s RMiT establishes clear metrics for system availability. Unplanned downtime for critical systems (especially customer-facing channels) is capped at a cumulative 4 hours over any 12-month period, with no single incident exceeding 2 hours. In August 2024, BNM underscored this by fining major banks for outages that breached these limits. This requirement is very much in line with MAS’s rule, reflecting a regional trend for near-zero downtime tolerance. Core banking vendors must engineer systems with high availability (99.9% uptime or better). Disaster Recovery (DR) expectations are similarly stringent – RMiT mandates that critical systems have a Recovery Time Objective (RTO) not more than 2 hours (120 minutes) per incident. Banks are expected to test failovers to meet this RTO. If a vendor-hosted system goes down beyond these thresholds, the client bank faces regulatory penalties, so the vendor will be under intense pressure to avoid such incidents. BNM has shown it will use enforcement (fines, supervisory actions) if outages indicate insufficient IT controls. Consequently, any core system provider in Malaysia should have local support, emergency response teams, and possibly dual-site setups (production and DR site) ideally both in Malaysia to rapidly recover services.

Regulatory Notification of Downtime/Changes: BNM requires banks to notify the central bank of major IT incidents promptly (the exact timeframe might be specified in RMiT or an incident reporting rule). Generally, banks should inform BNM as soon as possible after critical system failures or cyber incidents so that BNM is not caught off guard. Scheduled core banking system downtimes, if part of an approved change, do not always require direct notification, but if a downtime could significantly impact customers (e.g., an extended outage during a migration), BNM would expect advance notice and possibly an approval. Moreover, because material system changes often fall under the outsourcing policy, a planned core system replacement or migration is typically pre-approved by BNM. As part of that, the bank must present a robust transition plan including any downtime. In summary, unscheduled outages – notify immediately; scheduled major changes – seek approval/notify well in advance. BNM also often engages with banks in supervisory meetings about IT projects, so a core vendor might indirectly be subject to questions or assessments via the bank.

Shariah Compliance Requirements: Malaysia has a dual banking system (conventional and Islamic banks). For Islamic banking operations, Shariah compliance is a legal requirement under the Islamic Financial Services Act (IFSA) 2013. BNM’s Shariah Governance Policy mandates that Islamic banks have internal Shariah Committees overseeing all aspects of operations. For a core banking system vendor, this means: if the vendor’s system is to be used in an Islamic bank (or an Islamic window of a bank), it must support Islamic banking products and accounting. For instance, the system should be able to handle profit-sharing investment accounts, calculate profit instead of interest, prevent interest posting, segregate Islamic funds from conventional, and enforce any Shariah-compliant contract terms. During implementation, the bank’s Shariah Committee will likely review the system workflows for compliance. While there is no separate “IT certification” for Shariah, BNM requires any new product or system that could affect Shariah compliance to be approved by the Shariah Committee. Additionally, BNM’s Shariah Advisory Council issues binding resolutions – e.g., on hibah (gifts), tawarruq transactions – and the core system must be configurable to adhere to these rulings. In practical terms, a core banking vendor entering Malaysia should either have an Islamic banking module or be prepared to customize their software to meet Shariah requirements. This is a unique aspect of Malaysia (and Indonesia) not present in the other markets. Failing to support Shariah compliance could exclude the vendor from nearly half the banking market, given Malaysia’s large Islamic finance sector.

Cybersecurity Guidelines: Malaysia’s BNM RMiT is very detailed on cybersecurity expectations:

• Governance: Banks must establish a robust IT risk governance structure, including appointing a dedicated Chief Information Security Officer (CISO) and team. The board and senior management are accountable for cyber risk oversight.
• Baseline Security Controls: RMiT specifies controls such as multi-factor authentication for system administrators, secure coding practices for software, encryption of data at rest and in transit, and continuous monitoring of networks. Banks are required to implement network resilience measures and have 24/7 security monitoring.
• Testing: Regular penetration testing and vulnerability assessments are mandated for critical systems. BNM even suggests engaging qualified external testers to ensure independent assessments.
• Cyber Incident Response: Banks must report significant cyber incidents to BNM within hours and follow up with investigation reports. BNM may require an incident to be escalated to law enforcement if it involves breaches of customer data. RMiT also instructs banks to participate in sector-level cyber drills.
• Third-Party Security: Importantly for vendors, BNM expects banks to ensure service providers uphold equivalent security standards. Contracts with vendors should include right-to-audit, requirement to comply with bank’s security policies, incident reporting duties, and BCP arrangements. If a core banking vendor operates a cloud service, BNM’s 2023 RMiT update (Appendix on Cloud) provides additional controls – e.g., data segregation in multi-tenant environments, robust encryption key management (preferably keys controlled by the bank), and ensuring cloud providers obtain relevant certifications.
• Cyber Hygiene and Surveillance: BNM often aligns with global cyber guidance. For example, after some regional Swift payment hacks, BNM required banks to strengthen endpoint security and user access management. Vendors might need to implement specific security configurations (like compliance with CIS benchmarks or Swift CSP for payment modules). In short, Malaysia’s cyber regulations are among the strictest in the region, on par with Singapore’s. A core banking provider must demonstrate an advanced security posture – likely through audits – to satisfy BNM and client banks.

Certifications and Standards: BNM doesn’t explicitly list required certifications in RMiT, but it’s almost implicit. ISO/IEC 27001 certification is commonly pursued by Malaysian banks to comply with RMiT’s broad requirements. BNM itself often expects banks to benchmark against ISO standards. In fact, many Malaysian banks will only consider vendors who are ISO 27001 certified or can produce a recent SOC 2 Type II report, as part of their vendor due diligence (which BNM scrutinizes). PCI DSS is mandatory for any system touching card data – BNM has endorsed the Payment Card Industry Data Security Standard through its payment department oversight. Additionally, RMiT mentions data center resilience – typically Malaysian banks use Tier III or Tier IV certified data centers to satisfy uptime and redundancy criteria. BNM doesn’t mandate Uptime Institute certification per se, but RMiT’s specifications essentially map to Tier III standards (concurrently maintainable infrastructure, etc.). ISO 22301 (Business Continuity) is another relevant standard given BCP importance; banks or their key providers often have this to prove robust disaster recovery processes. For Islamic banking aspects, there’s no ISO standard – compliance is validated by Shariah audits and the Shariah Committee. In summary, to align with regulatory expectations in Malaysia, a core banking vendor is strongly advised to have multiple internationally recognized certifications (ISO 27001, PCI DSS, etc.) and adhere to standards like ITIL for service management and COBIT for IT governance, as these will be looked upon favorably by both BNM and the banks’ own risk committees.

Comparative Summary Table

Below is a side-by-side comparison of key regulatory factors across Singapore, Vietnam, Thailand, Malaysia, and Indonesia as they pertain to core banking system vendors and their client banks:

Sources: The above comparison references each country’s regulatory documents and official guidelines, such as MAS Notices, SBV Circular 18/2018, BOT notifications and PDPA provisions, BNM’s RMiT policy, and OJK regulations, among others, to ensure accuracy and currency of the information. This comparative overview can serve as a strategic guide for core banking system providers to tailor their compliance and product strategies for each Southeast Asian market.

東南亞核心銀行系統的監管框架

以下是針對東南亞五個主要市場（新加坡、越南、泰國、馬來西亞、印尼）核心銀行系統供應商（如 Neo Core）在零售銀行、商業銀行、數位銀行和伊斯蘭銀行領域的監管要求總結。

🇸🇬 新加坡

• 監管機構：新加坡金融管理局（MAS）(Cloud4C)

• 主要法規：

• 《個人資料保護法》（PDPA）

• 《科技風險管理指引》（TRM Guidelines）

• 《雲端服務指引》(Baker McKenzie Resource Hub, Silverfort)

• 重點要求：

• 資料駐留：無強制要求，但需確保資料跨境傳輸的安全性和合規性。

• 個人識別資訊（PII）處理：需取得明確同意，並遵守PDPA規定。
• 資料儲存與稽核：需保留完整的稽核記錄，並接受MAS或其指定機構的稽核。
• 停機管理：重大事件需通報MAS，並制定業務持續計劃（BCP）。
• 伊斯蘭合規：適用於伊斯蘭銀行，需遵守相關的伊斯蘭金融指引。
• 資訊安全：鼓勵遵循ISO 27001、PCI DSS等國際標準。(Sangfor Technologies, Baker McKenzie Resource Hub)

🇻🇳 越南

• 監管機構：越南國家銀行（SBV）(Moody's)

• 主要法規：

• 《個人資料保護法》（PDPL，預計2026年生效）

• 《網絡安全法》及其實施細則（Decree 53）(Sangfor Technologies, DLA Piper)

• 重點要求：

• 資料駐留：需在越南境內儲存特定類型的資料，包括用戶個人資訊和交易資料。

• PII處理：需取得用戶同意，並遵守PDPL規定。
• 資料儲存與稽核：需保留至少24個月的資料，並接受相關部門的檢查。
• 停機管理：需通報用戶和主管機關，但具體時限和方式尚未明確。
• 資訊安全：需建立資訊安全管理體系，並定期進行風險評估。(Sangfor Technologies)

🇹🇭 泰國

• 監管機構：泰國銀行（BOT）

• 主要法規：

• 《個人資料保護法》（PDPA）

• 《支付系統法》

• BOT相關通知和指引(Baker McKenzie Resource Hub, Deloitte United States, Baker McKenzie Resource Hub)

• 重點要求：

• 資料駐留：無強制要求，但跨境傳輸需確保接收方具備足夠的資料保護措施。

• PII處理：需取得用戶同意，並遵守PDPA規定。
• 資料儲存與稽核：需保留完整的稽核記錄，並接受BOT的檢查。
• 停機管理：需制定業務持續計劃，並定期進行測試。
• 資訊安全：需建立資訊安全管理體系，並定期進行風險評估。(Sangfor Technologies)

🇲🇾 馬來西亞

• 監管機構：馬來西亞國家銀行（BNM）(Thales Cyber Security Solutions)

• 主要法規：

• 《個人資料保護法》（PDPA）

• 《科技風險管理政策》（RMiT）(Thales Cyber Security Solutions)

• 重點要求：

• 資料駐留：無強制要求，但跨境傳輸需確保接收方具備足夠的資料保護措施。

• PII處理：需取得用戶同意，並遵守PDPA規定。
• 資料儲存與稽核：需保留完整的稽核記錄，並接受BNM的檢查。
• 停機管理：需制定業務持續計劃，並定期進行測試。
• 伊斯蘭合規：適用於伊斯蘭銀行，需遵守相關的伊斯蘭金融指引。
• 資訊安全：需建立資訊安全管理體系，並定期進行風險評估。

🇮🇩 印尼

• 監管機構：印尼金融服務管理局（OJK）

• 主要法規：

• 《個人資料保護法》（PDP Law）

• OJK第11/POJK.03/2022號規定(OJK Portal)

• 重點要求：

• 資料駐留：金融機構需將資料儲存在印尼境內，除非獲得OJK批准。

• PII處理：需取得用戶同意，並遵守PDP Law規定。
• 資料儲存與稽核：需保留完整的稽核記錄，並接受OJK的檢查。
• 停機管理：需通報OJK，並制定業務持續計劃。
• 伊斯蘭合規：適用於伊斯蘭銀行，需遵守相關的伊斯蘭金融指引。
• 資訊安全：需建立資訊安全管理體系，並定期進行風險評估。

🌏 比較總結

整體而言，印尼和越南在資料駐留方面的要求較為嚴格，而新加坡、馬來西亞和泰國則在資訊安全和稽核方面有更高的標準。對於核心銀行系統供應商而言，需根據各國的監管要求，調整產品設計和合規策略。

Understanding MCP (Model Context Protocol) - What It Is, How It Works, and Why It Matters

In the rapidly evolving world of AI and large language models (LLMs), context management is becoming a cornerstone for building smarter, more efficient, and reliable applications. Enter MCP (Model Context Protocol) — a protocol designed to help developers and systems define, organize, and control the context passed into AI models. In this post, we’ll explore what MCP is, how it works, and real-world use cases that demonstrate its value.

🔍 What is MCP (Model Context Protocol)?

MCP, or Model Context Protocol, is an open protocol for specifying and managing the context that gets sent to language models. Context, in this sense, refers to the structured information provided to an LLM during inference—everything from user inputs and chat history to documents, tools, and system instructions.

MCP is part of a broader movement to standardize how AI applications interact with LLMs, making it easier to build reproducible, debuggable, and modular AI workflows.

Think of MCP as the equivalent of an API contract, but for LLM context. It tells the model what to expect, how to behave, and what tools it has access to—all in a consistent, declarative format.

⚙️ How MCP Works

MCP operates through a YAML-based declarative configuration, where the developer defines a context schema that the model runtime interprets. This configuration can include:

• System instructions: Base directives that set the model’s behavior (e.g., “You are a helpful assistant.”).
• Memory objects: Previous messages or facts the model should remember.
• Tools and functions: Descriptions of callable tools available to the model (like a calculator, API, or database).
• User inputs: Current prompts or queries from users.
• Artifacts: Structured data, like documents or JSON blobs, that the model should reference.

Each item in the MCP context is versioned and traceable, enabling better observability and debugging during LLM usage.

An example MCP file might look like:

version: "1.0"
system: "You are a coding assistant."
inputs:
  user_message: "Write a Python function that merges two dictionaries."
memory:
  - type: chat_history
    content:
      - role: user
        content: "Hi, can you help me with Python?"
      - role: assistant
        content: "Sure! What do you need?"
tools:
  - name: code_linter
    description: "Lint and format Python code."

🧠 Why Use MCP?

As applications become more complex, prompt engineering becomes less scalable. Developers need:

• Modularity: Reuse context components like tools or user profiles across sessions.
• Auditability: Track what was sent to the model and why it responded the way it did.
• Interoperability: Use a shared context format across multiple model vendors or frameworks.
• Observability: Inspect and debug the actual context used during inference.

MCP helps solve these problems by separating context building from business logic, and promoting reproducibility.

✅ Example Use Cases

1. Agent-Based Applications

MCP enables advanced agents (like LangChain or Autogen) to clearly define toolkits, memory, and goals. You can declaratively list the tools available to the agent and pass them as structured context to the model runtime.

2. Enterprise Chatbots

In customer service, MCP can define rules, FAQs, and access control logic for LLMs. By declaring memory, user roles, and pre-approved documents, you ensure compliance and consistency.

3. Coding Assistants

By declaring available functions (e.g., a code executor or documentation fetcher), coding assistants can use tools intelligently without hardcoding them into the prompt.

4. Observability and Debugging

When models hallucinate or fail, MCP context logs provide full transparency into what information was available to the model—helping teams debug issues faster.

🚀 Final Thoughts

MCP is still evolving, but it’s already proving essential for production-grade LLM applications. By abstracting and standardizing model context, it empowers teams to build smarter, safer, and more maintainable AI systems.

Whether you're building a chatbot, an agentic system, or a retrieval-augmented generation (RAG) pipeline, embracing MCP can offer a cleaner, more scalable path forward.

認識 MCP（模型上下文協議）- 什麼是 MCP、如何運作，以及它的重要性

在快速發展的人工智慧（AI）與大型語言模型（LLM）領域中，上下文管理成為打造更聰明、更高效、更可靠應用程式的關鍵。這時候，**MCP（Model Context Protocol，模型上下文協議）**登場，它是一項協助開發人員與系統定義、組織並控制傳遞至 AI 模型之上下文的協議。本文將探討 MCP 是什麼、如何運作，並透過實際案例來說明其價值。

🔍 MCP（模型上下文協議）是什麼？

MCP 是一種 開放協議（open protocol），用來定義與管理提供給語言模型的「上下文」（context）。所謂上下文，指的是推論時傳遞給 LLM 的結構化資訊，涵蓋使用者輸入、聊天紀錄、文件、工具以及系統指令等。

你可以將 MCP 想像成 API 契約的語境版本：它讓模型知道該期待什麼、有何工具可用、應如何回應，而且這一切都是以一致、宣告式的格式來進行。

⚙️ MCP 如何運作？

MCP 透過 YAML 格式的宣告式設定檔運作，開發人員可在此定義模型運行時所需的上下文結構，內容包括：

• 系統指令（System Instructions）：定義模型的基本行為（例如：「你是一位有幫助的助理」）。
• 記憶物件（Memory Objects）：模型應該記住的對話或事實。
• 工具與函數（Tools and Functions）：模型可調用的功能描述（例如計算機、API 或資料庫）。
• 使用者輸入（User Inputs）：目前使用者輸入的提示或問題。
• 人工製品（Artifacts）：模型需要參考的結構化資料（如文件或 JSON 檔）。

MCP 的每一個部分都具備版本控制與可追蹤性，有助於提升 **可觀測性（observability）**與除錯能力。

範例 MCP 設定如下：

version: "1.0"
system: "You are a coding assistant."
inputs:
  user_message: "Write a Python function that merges two dictionaries."
memory:
  - type: chat_history
    content:
      - role: user
        content: "Hi, can you help me with Python?"
      - role: assistant
        content: "Sure! What do you need?"
tools:
  - name: code_linter
    description: "Lint and format Python code."

🧠 為何要使用 MCP？

隨著 AI 應用的複雜性增加，純粹依賴提示工程（prompt engineering）已不具可擴展性。開發者面臨以下挑戰：

• 模組化（Modularity）：可在不同會話中重複使用使用者資料、工具設定等上下文元件。
• 可稽核性（Auditability）：追蹤模型接收到的上下文，以及模型如何生成回應。
• 互通性（Interoperability）：跨多個模型供應商或框架使用共通的上下文格式。
• 可觀測性（Observability）：方便檢查與除錯模型實際運行時使用的上下文。

MCP 的出現正好解決這些問題，將上下文建構與業務邏輯分離，提升 AI 系統的可維護性與擴展性。

✅ 使用範例

1. 代理型應用（Agent-Based Applications）

MCP 可清楚定義智能代理可用的工具、記憶、目標等資源，支援如 LangChain 或 AutoGen 這類框架的先進代理架構。

2. 企業級聊天機器人

客服應用中，可透過 MCP 宣告規則、FAQ、使用者角色與授權資訊，確保一致性與合規。

3. 程式碼助理

開發工具助理可利用 MCP 宣告可用的函數（如程式執行器、說明文件查詢器），使模型能智慧地選擇並調用工具。

4. 觀測與除錯

當模型產生錯誤回應或幻覺時，MCP 的上下文紀錄能提供完整資訊，有助於快速找出問題根源。

🚀 結語

雖然 MCP 尚在發展中，但它已成為建構生產級 LLM 應用的核心組件之一。透過標準化與模組化上下文管理，MCP 協助開發者打造更聰明、更安全、更易維護的 AI 系統。

無論你是在開發聊天機器人、智能代理，或是 RAG（檢索增強生成）應用，採用 MCP 都將成為未來的主流方式。

Core Banking Systems Market in China - Vendors, SIs, and Trends

China’s core banking system market is undergoing rapid modernization, driven by the need for internet-scale performance (to support platforms like WeChat Pay and Alipay) and digital transformation across banks. Domestic Chinese vendors have risen to prominence with next-generation core platforms that emphasize high performance, modern architectures, flexibility, and integration capabilities. Below, we detail key Chinese core banking vendors serving large commercial banks and fintech platforms, including their technology, architecture, integration flexibility, configurability, business capabilities, competitive advantages, and company profiles. We also identify system integrators (SIs) active in this market and discuss how vendors and SIs collaborate. Finally, we assess overall trends, especially the shift from legacy mainframe cores to cloud-native platforms in China, in the context of AI integration and digital transformation.

Sunline (Shenzhen Sunline Tech)

Overview: Sunline is a leading Chinese fintech software provider (founded 2002) known for its core banking system innovations (A Decade of Success: Sunline Expands Partnership with a Major Chinese Joint-Stock Bank by Winning a Core Banking System Project). It was the first in China to develop a Java-based core banking system, breaking from the COBOL/mainframe tradition around 2010 (Sunline's Observation: 10-year History of Core Changes in China's Banking Industry). Sunline’s solutions today are cloud-native, AI-driven, and widely adopted by banks embarking on digital transformation (A Decade of Success: Sunline Expands Partnership with a Major Chinese Joint-Stock Bank by Winning a Core Banking System Project). Notable clients include WeBank (China’s first digital-only bank), Ping An Bank, Bank of Nanjing, Bank of Dongguan, and many regional banks, where Sunline’s core has often been a centerpiece of modernization efforts (Sunline's Observation: 10-year History of Core Changes in China's Banking Industry) (Sunline's Observation: 10-year History of Core Changes in China's Banking Industry).

• High Performance & Scalability: Sunline’s core is built for extreme scale. It pioneered a fully distributed architecture to support WeBank’s requirement of 500 million customer capacity and high-concurrency transactions, something traditional cores could not handle (Sunline's Observation: 10-year History of Core Changes in China's Banking Industry) (Sunline's Observation: 10-year History of Core Changes in China's Banking Industry). The system separates transaction processing from accounting, and runs on clusters of x86 servers (no mainframe needed), allowing horizontal scalability to handle internet-level workloads (Sunline's Observation: 10-year History of Core Changes in China's Banking Industry). In tests and production, Sunline’s Java distributed core has demonstrated robust throughput and stability for hundreds of millions of users (Sunline's Observation: 10-year History of Core Changes in China's Banking Industry). For example, WeBank’s live deployment in 2015 (built with Sunline) supports massive concurrent transactions with no performance bottleneck (Sunline's Observation: 10-year History of Core Changes in China's Banking Industry).

• Modern Architecture: Sunline uses a cloud-friendly, distributed microservices architecture. The core is entirely Java-based (a first in China’s banking industry) and cross-platform (Sunline's Observation: 10-year History of Core Changes in China's Banking Industry) (Sunline's Observation: 10-year History of Core Changes in China's Banking Industry). Early on, Sunline proved Java could meet core banking needs by completing an IBM lab test in 2010 and launching China’s first Java core in 2013 for Hengfeng Bank (Sunline's Observation: 10-year History of Core Changes in China's Banking Industry) (Sunline's Observation: 10-year History of Core Changes in China's Banking Industry). Today, its architecture has evolved to microservices + “unitized” design – essentially a microservices architecture segmented into logical units – which enables flexible deployment and unlimited scalability through unit-wise expansion and microservice elastic scaling (Sunline's Observation: 10-year History of Core Changes in China's Banking Industry) (Sunline's Observation: 10-year History of Core Changes in China's Banking Industry). This design improves fault isolation and resiliency (each unit and microservice can fail independently), and supports multi-center active-active deployment for high availability (Sunline's Observation: 10-year History of Core Changes in China's Banking Industry). Sunline’s latest core (e.g. its SunLTTS V8 platform) embodies this “microservices + unit” approach, which was successfully implemented at Bank of Dongguan in 2020, yielding high performance, high availability, and rapid expansion capability at lower cost (Sunline's Observation: 10-year History of Core Changes in China's Banking Industry) (Sunline's Observation: 10-year History of Core Changes in China's Banking Industry).

• Integration Flexibility: Sunline’s technology is known for openness and easy integration into complex environments. It supports multiple databases and platforms; for instance, during WeBank’s project, Sunline’s core swapped out Oracle for MySQL in one week thanks to its forward-looking cross-platform design (Sunline's Observation: 10-year History of Core Changes in China's Banking Industry). This demonstrated strong flexibility to integrate with external or unconventional components. The system exposes a full set of APIs/services that allow banks to connect hundreds of peripheral systems. In one regional bank deployment, the new Sunline core smoothly interfaced with “hundreds of peripheral systems” and ran with efficient operations, showing compatibility with external channels, payment gateways, mobile apps, and fintech platforms (Sunline's Observation: 10-year History of Core Changes in China's Banking Industry). Sunline also works closely with partners (like Huawei) to ensure its core can integrate at the infrastructure level; for example, Sunline cores have been proven on Huawei’s Kunpeng hardware and GaussDB database to meet domestic technology mandates (Sunline's Observation: 10-year History of Core Changes in China's Banking Industry).

• Configurability & Customization: The Sunline core is highly configurable, enabling banks to define products and processes without heavy coding. (While specific product factory tools aren’t detailed publicly by Sunline in citations, its design philosophy aligns with other modern cores that use parameterization.) The success at multiple banks indicates Sunline can customize solutions to each bank’s needs – e.g. Ping An Bank and Bank of Nanjing required many custom developments when implementing distributed core, which Sunline delivered under tight timelines (Sunline's Observation: 10-year History of Core Changes in China's Banking Industry) (Sunline's Observation: 10-year History of Core Changes in China's Banking Industry). Sunline likely provides a product configuration framework to rapidly launch new deposit or loan products, which is crucial given Chinese banks’ fast pace of innovation. Its microservices architecture also means specific business services can be added or modified independently, enhancing customizability.

• Business Functionality: Sunline’s core banking suite supports a full range of retail and commercial banking services – deposits, loans, payments, general ledger, customer info, etc. In the distributed core implementations, banks achieved separation of concerns (for example, a dedicated accounting module) and could support new business models. WeBank’s core (with Sunline) was one of the first to implement full product factories and customer-centric design (e.g. truly separating product definitions from code). Additionally, Sunline offers specialized solutions (like treasury and FX systems) that integrate with the core (Sunline's Observation: 10-year History of Core Changes in China's Banking Industry). The core handles multi-channel transaction processing, high-volume payment clearing (critical for Alipay/WeChat Pay links), and real-time analytics. It also supports enterprise banking (corporate loans, trade finance) as evidenced by recent Sunline projects that include corporate loan and deposit modules in a unified architecture (A Decade of Success: Sunline Expands Partnership with a Major Chinese Joint-Stock Bank by Winning a Core Banking System Project).

• Competitive Advantages: Sunline’s edge lies in its early mover advantage in new architecture and its track record of large-scale deployments. It delivered China’s first Java core and first fully distributed core (Sunline's Observation: 10-year History of Core Changes in China's Banking Industry) (Sunline's Observation: 10-year History of Core Changes in China's Banking Industry), giving it unmatched experience. Its system proved capable where others failed – e.g. many vendors withdrew from WeBank’s rigorous proof-of-concept, but Sunline met all requirements (Sunline's Observation: 10-year History of Core Changes in China's Banking Industry). This success has made Sunline a go-to vendor for banks aiming to leap from legacy to cutting-edge. Moreover, Sunline aligns with China’s strategic tech goals: its solutions eliminate dependency on foreign mainframes and databases, using pure software on open systems (Sunline's Observation: 10-year History of Core Changes in China's Banking Industry). This appeals to banks concerned about sovereignty and cost. Sunline also heavily invests in R&D (7,000+ staff, presence in 10+ countries) and has embraced AI integration (collaborations with firms like Huawei and DeepSeek on AI-powered core banking) (A Decade of Success: Sunline Expands Partnership with a Major Chinese Joint-Stock Bank by Winning a Core Banking System Project). Being recognized in Forbes China’s 2024 Top 50 Fintech list underscores its influence (A Decade of Success: Sunline Expands Partnership with a Major Chinese Joint-Stock Bank by Winning a Core Banking System Project). In summary, Sunline’s competitive strengths are: proven hyper-scale performance, distributed expertise, compliance with domestic tech standards, and a broad solution portfolio beyond core (which can be bundled in deals).

• Technology Stack & Architecture: Sunline’s core runs on a Java EE stack with support for various databases (Oracle, MySQL, and domestic distributed DBs). It uses a containerized microservices framework (Sunline has mentioned using Spring Cloud/Alibaba tech in some contexts, though not cited here). The “unitized” microservice approach is an architectural highlight – units can be thought of as partitions (by customer segment or product type) that can scale out independently, a concept borrowed from large internet companies. This architecture was validated in production (e.g., Bank of Dongguan’s new core) to deliver high availability and rapid scalability at lower cost (Sunline's Observation: 10-year History of Core Changes in China's Banking Industry). Sunline’s platform can be deployed on private cloud or hybrid cloud environments and is hardware-agnostic. For instance, Sunline cores have been deployed on Huawei’s FusionCube hyper-converged infrastructure and ARM-based Kunpeng servers, as well as standard x86 clusters (Huawei and Forms Syntron Release Distributed Open Banking Service Solution for Banks - Huawei) (A Decade of Success: Sunline Expands Partnership with a Major Chinese Joint-Stock Bank by Winning a Core Banking System Project). This flexibility in stack ensures banks can choose their infrastructure (important in China’s “Xinchuang” initiative of using domestic IT).

• Migration Approach: In large projects, Sunline advocates a phased migration to mitigate risk. A recent joint-stock bank project involves running new and legacy cores in parallel and using an advanced transaction transition platform to bridge differences (A Decade of Success: Sunline Expands Partnership with a Major Chinese Joint-Stock Bank by Winning a Core Banking System Project). This middleware handles real-time synchronization (playback and reconciliation) to ensure no data loss and a seamless transition for the bank’s operations (A Decade of Success: Sunline Expands Partnership with a Major Chinese Joint-Stock Bank by Winning a Core Banking System Project). In practice, Sunline often starts by implementing a subset of products or a new module (e.g. a new deposits system) on its core while the old core continues for other products, then incrementally migrates accounts. This coexistence strategy keeps customer service uninterrupted and allows gradual tuning. Sunline also provides tools for data migration and reconciliation to guarantee accuracy. Its success in executing “big bang” and phased go-lives at multiple banks highlights robust project methodology.

• Risk Management & Support: As a core system provider, Sunline offers extensive support structures: dedicated on-site teams during implementation, 24/7 technical support after go-live, and joint development with clients. The company’s achievement of TMMi Level 5 certification (a top software test maturity level) indicates rigorous quality assurance in its delivery (A Decade of Success: Sunline Expands Partnership with a Major Chinese Joint-Stock Bank by Winning a Core Banking System Project). Sunline also emphasizes resilience in design – features like multi-active data centers and fault isolation by microservice unit reduce operational risk (Sunline's Observation: 10-year History of Core Changes in China's Banking Industry). In terms of project risk, Sunline mitigates talent shortages by mobilizing experienced experts (e.g. it redeployed its WeBank core team to help Ping An Bank’s project when skilled distributed architects were scarce (Sunline's Observation: 10-year History of Core Changes in China's Banking Industry)). This internal knowledge sharing ensures complex issues are solved. Furthermore, Sunline partners with hardware and cloud vendors (Huawei, etc.) to conduct joint performance testing and optimize the stack, reducing technical risk for clients.

• Notable References & Case Studies: Sunline’s prominent case is WeBank (2015) – delivering the first distributed core banking system in China (Sunline's Observation: 10-year History of Core Changes in China's Banking Industry). This core handles massive retail accounts for Tencent’s online bank and set the benchmark for others. Following that, Ping An Bank (2016) and Bank of Nanjing (2016) both worked with Sunline to build distributed cores, which launched successfully and exceeded performance expectations (Sunline's Observation: 10-year History of Core Changes in China's Banking Industry). These successes triggered a wave of adoption nationwide. Another milestone was Zhangjiagang Rural Commercial Bank (2019) – the first core in China to run on a fully domestic distributed database (instead of Oracle/DB2), implemented by Sunline (Sunline's Observation: 10-year History of Core Changes in China's Banking Industry). This broke the long-term reliance on foreign database products in core banking, providing a template for tech self-sufficiency (Sunline's Observation: 10-year History of Core Changes in China's Banking Industry). In 2020, Bank of Dongguan went live with Sunline’s latest microservice-unitized core, achieving high performance with low cost, and proving the architecture’s value for mid-size banks’ digital growth (Sunline's Observation: 10-year History of Core Changes in China's Banking Industry). Sunline has also expanded internationally – for example, it was selected by Siam Commercial Bank (Thailand) for core modernization (Thailand's SCB taps Sunline for core banking upgrade), showing global competitiveness. These references demonstrate Sunline’s capability across different bank sizes and its strong reputation in delivering modern core solutions.

Yusys Technologies (Beijing Yusys / Yucheng Tech)

Overview: Yusys Technologies (est. 1999) is a top domestic banking IT provider, often regarded as an industry leader in China’s banking software market (共推金融业数字化进程：宇信科技与华为的合作与蜕变-华为) (共推金融业数字化进程：宇信科技与华为的合作与蜕变-华为). Yusys offers a comprehensive suite of banking solutions, and its core banking system is a flagship product used by many Chinese banks. The company has a vast customer base that spans the central bank, all three policy banks, all six state-owned commercial banks, 12 joint-stock banks, 180+ city commercial banks, 200+ rural banks/credit unions, and 50+ foreign banks in China (across its product lines) (背靠百度，宇信科技难言增长 - 妙投). While not every one of those uses Yusys for core systems, this reach illustrates Yusys’s strong market penetration and trust. Yusys’s core banking solution is a new-generation, fully distributed system built on its unified development platform, emphasizing advanced design, flexibility, and broad functionality (Core Banking). The company is known for its deep domain experience (25+ years) and has been listed on IDC’s FinTech rankings globally (共推金融业数字化进程：宇信科技与华为的合作与蜕变-华为).

• High Performance & Scalability: Yusys’s core banking platform is engineered to handle high transaction volumes and rapid growth. It “fully supports distributed & micro-service architecture”, meaning it can run on clusters of commodity servers and scale out horizontally as load increases (Core Banking). Built in Java, it leverages modern middleware and in-memory caching to ensure throughput under heavy loads. Yusys has demonstrated performance in projects like replacing legacy cores in regional banks where high concurrency for online channels was required. Additionally, Yusys collaborates with technology partners to enhance performance – for example, it has a joint solution with PingCAP’s TiDB (a distributed NewSQL database) to provide strong consistency and HTAP (hybrid transaction/analytical) capabilities for core banking data (神州信息新一代分布式银行核心系统 | PingCAP 平凯星辰) (神州信息新一代分布式银行核心系统 | PingCAP 平凯星辰). By using distributed databases and middleware, Yusys cores can meet massive data and concurrency demands. The system is also proven on domestic cloud infrastructure; Yusys and Huawei jointly showcased core banking on Huawei’s Kunpeng servers and GaussDB database, indicating the core can scale on purely Chinese tech stacks with high performance (共推金融业数字化进程：宇信科技与华为的合作与蜕变-华为) (共推金融业数字化进程：宇信科技与华为的合作与蜕变-华为). This positions Yusys well for large banks that must support millions of users (including heavy mobile payments traffic).

• Modern Architecture: Yusys’s core is a cloud-ready, microservice-based system. It is developed in Java and adheres to a modular SOA design, with independent business components for key functions. According to Yusys, the solution is built on a Unified Development Platform, following leading technical architecture principles (Core Banking). It supports distributed deployment, microservices, and domestic databases natively (Core Banking). In practice, the architecture includes a Business Middle Platform concept: Yusys’s Internet Finance Core is constructed on top of the distributed core, with functional centers (Customer Center, Product Center, Marketing Center, Limit/Quota Center, Account Center, Contract Center, Transaction Center, Payment Center, Accounting Center, Error handling Center, Internal Control Center) (Core Banking). These correspond to microservices or modules that encapsulate specific domains, which can be reused and extended. The core integrates with a Data Middle Platform to leverage data for AI and risk control in real time (Core Banking). This two-layer architecture (business middle platform + data platform) is very modern and enables open banking and analytics. Yusys fully supports deployment on private cloud or hybrid cloud, and it’s not tied to any proprietary hardware – it can run on mainstream Linux servers and various databases (Oracle, MySQL, as well as China’s OceanBase or TiDB, etc.). The system’s openness and adherence to “leading technical architecture” ensure longevity and easier integration of emerging tech (like containers and service mesh for microservices, though not explicitly cited).

• Integration with External Systems: Yusys core banking is designed to be API-driven and easily integrated. It provides hundreds of services accessible via standard interfaces (e.g., RESTful APIs, message queues) that allow external channels and fintech apps to connect. The middle-platform approach means that for each domain (customer, account, etc.), there are well-defined services – facilitating integration with mobile banking, WeChat mini-apps, Alipay, etc. In fact, Yusys has a strong background in online banking and channels – it first made its name developing internet banking for China Construction Bank (背靠百度，宇信科技难言增长 - 妙投). This heritage means its core banking solution was likely built with omnichannel integration in mind from the start. Yusys also provides an Enterprise Service Bus and unified communication platform as part of its technology stack (Core Banking) (Core Banking), which can mediate between the core and external systems. Additionally, Yusys often co-develops solutions with partners; for example, it worked with Ant Group (Ant Financial) and Huawei to ensure interoperability with Ant’s distributed database (OceanBase) and Huawei’s tech for joint clients (共推金融业数字化进程：宇信科技与华为的合作与蜕变-华为) (共推金融业数字化进程：宇信科技与华为的合作与蜕变-华为). Therefore, a bank using Yusys core can expect smooth integration with payment platforms, credit bureaus, regulatory systems, and AI services. The core supports open APIs and real-time data sharing, which is crucial for embedding banking services in ecosystems (WeChat/Alipay require banks’ cores to respond in sub-seconds to payment requests, and Yusys’s architecture supports that level of responsiveness).

• Product Configurability & Customization: Configurability is a highlight of Yusys’s core. It features an “intelligent parameter management” capability and a financial product factory, similar to other modern cores, allowing banks to create or modify products through configuration rather than code (神州信息新一代分布式银行核心系统 | PingCAP 平凯星辰) (this is mentioned in context of DCITS, but Yusys likely has analogous tools). Every business module’s parameters are centrally managed, enabling rapid adjustments to things like interest rates, fee rules, and product definitions. Yusys’s platform emphasizes quick launch of new products – historically, one pain point with legacy systems was 3-6 month product rollouts, which Yusys aims to shorten dramatically. In addition to parameters, Yusys supports component reusability: its core shares common services (e.g., customer info, accounts) across retail, corporate, and other lines, so new offerings can be assembled from existing components. Customization is supported through extension points and an extensive rule engine (Yusys provides a rule engine tool as part of its platform (Core Banking)). This lets banks insert custom business logic or compliance rules without altering core code. Overall, banks using Yusys can expect a highly flexible product configuration process, from designing new loan products to tailoring workflows, all within the system’s parameterization and rule frameworks. This flexibility is one reason Yusys has remained a preferred IT partner as banks innovate in consumer finance, supply chain finance, etc., areas where fast customization is needed (背靠百度，宇信科技难言增长 - 妙投) (背靠百度，宇信科技难言增长 - 妙投).

• Business Functionalities: Yusys delivers comprehensive banking functionality covering retail, corporate, and digital banking needs. Its core system supports standard core banking modules: deposits (current, savings, time deposits), loans (consumer, SME, corporate loans), payments/transfer, general ledger, and ancillary services like collateral management, limits management, and risk controls. Yusys often implements an entire suite – front to back. For example, it offers solutions for credit (loan origination, credit approval) that tie into the core, data analytics platforms, omnichannel front-ends (online banking, mobile banking, WeChat banking) (核心系统-中国银行业IT解决方案市场中领军企业之一) (核心系统-中国银行业IT解决方案市场中领军企业之一), and regtech (regulatory reporting). In the core itself, Yusys’s Internet Finance Core (targeted for fintech scenarios) includes customer center, product center, account center, contract center, transaction center, payment center, accounting center, etc., essentially covering all core banking processes (Core Banking). It also has a Marketing center for campaigns and a Quota/Limit center for credit limits (Core Banking) – indicating built-in CRM and risk features. This breadth means banks can run a wide range of financial products on a single integrated platform. Yusys’s core is also capable of supporting innovative businesses: community finance, online lending platforms, rural finance portals, etc., are mentioned as use cases (核心系统-中国银行业IT解决方案市场中领军企业之一). Importantly, the core is tightly integrated with Yusys’s data platform to support AI-driven functions like intelligent customer identification, risk scoring, and personalized marketing in real time (核心系统-中国银行业IT解决方案市场中领军企业之一). Thus, beyond traditional banking ledger tasks, Yusys core can be seen as an enabler for smart banking – combining transactional and analytical capabilities.

• Competitive Advantages: Yusys’s competitive strengths include its all-round solution portfolio, extensive experience, and strong ecosystem ties. It has been in banking IT for over two decades, accumulating domain knowledge and a stable of mission-critical systems at big banks. Yusys is often called a “leader in multiple banking IT segments”, with top market share in areas like credit management and online banking (背靠百度，宇信科技难言增长 - 妙投). This cross-domain presence allows Yusys to offer banks an integrated approach (core + lending + channels + regtech all from one provider), which can reduce integration cost and risk. Another advantage is Yusys’s alignment with China’s tech ecosystem: it deeply collaborates with domestic tech giants – for instance, working with Huawei (Diamond partner) on database, server, OS optimization, with Ant Group on financial cloud and database, and with Baidu (which invested in Yusys in 2020) to incorporate AI like large language models (背靠百度，宇信科技难言增长 - 妙投) (背靠百度，宇信科技难言增长 - 妙投). This means Yusys is at the forefront of bringing technologies like AI, big data, blockchain, and Xinchuang (trusted domestic IT) into banking. Few competitors can claim such broad partnerships. Additionally, Yusys’s sheer client base acts as a testament and a network effect – new customers find comfort that Yusys solutions are already proven in banks of all sizes, including tier-1 banks and foreign banks in China. Yusys also has an expanding international footprint (subsidiaries in Hong Kong, Singapore, Indonesia) (共推金融业数字化进程：宇信科技与华为的合作与蜕变-华为), and it recently won a core banking project for a multinational bank’s Hong Kong and international branches (背靠百度，宇信科技难言增长 - 妙投), showcasing competitiveness against global vendors. Finally, the company’s scale (publicly listed, thousands of employees) and financial stability make it a low-risk choice for large banks compared to smaller startups. Its ability to continuously innovate (e.g., integrating AI anti-fraud “firewalls” with BlackEye Tech (背靠百度，宇信科技难言增长 - 妙投)) while maintaining legacy expertise gives it a balanced advantage.

• Technology Stack & Architecture: Yusys’s core uses a Java EE technology stack, leveraging microservice frameworks (likely Spring Cloud or similar) and containerization for deployment. It runs on standard operating systems (Linux-based) and supports both SQL and NewSQL databases. Yusys explicitly supports domestic databases (to comply with localization): the system has been run with Huawei GaussDB, Ant’s OceanBase, and PingCAP’s TiDB as evidenced by joint solutions (共推金融业数字化进程：宇信科技与华为的合作与蜕变-华为) (神州信息新一代银行核心系统联合解决方案 - OceanBase). The architecture is cloud-native to a large extent – supporting dockerized deployment, elastic scaling of computing resources, and possibly Kubernetes orchestration (Yusys’s site references “Unified Microservice Platform” and cloud resource management tools, implying cloud-native design) (Core Banking) (Core Banking). For integration, a service management and control platform (likely an API gateway/ESB) is part of the stack (Core Banking). Yusys also provides development tools like a workflow engine and rule engine (Core Banking) to facilitate custom logic. On the frontend, Yusys has frameworks for unified mobile and web banking which can plug into the core easily. In summary, the stack is that of a modern enterprise application: microservices + middle platform + distributed data + DevOps toolchain. Yusys’s long history means it also knows how to interface with legacy systems; it likely has connectors for mainframe or older Unix systems to aid migration.

• Migration Approach: Yusys typically approaches core banking replacement by gradual module implementation or parallel run. Given the complexity (200+ subsystems in a big bank), Yusys often advocates picking a domain (say, retail lending) to go first on the new core, then phasing others. It provides migration tools to map and convert data from legacy core databases to the new schema. Yusys’s core being parameter-driven helps mirror existing products on the new system to ensure functional equivalence. In some cases, Yusys might run a shadow core in parallel with the old one, reconcile outputs, and then switch over when stable. The company’s broad experience (1000+ financial institutions served (共推金融业数字化进程：宇信科技与华为的合作与蜕变-华为)) means it has encountered many legacy environments. For instance, Yusys has been involved in legacy mainframe-to-open migrations at state banks. It likely uses a combination of automated data migration, rigorous testing (unit, integration, parallel comparison), and training. Yusys also established joint labs with partners for tricky migrations, such as a “TD (Teradata) to GaussDB” migration at a joint-stock bank with Huawei (共推金融业数字化进程：宇信科技与华为的合作与蜕变-华为) – these joint efforts smooth out technical conversion issues in advance. Its migration philosophy emphasizes risk control: run old and new in tandem until the new core is proven stable, then decommission the old. With so many reference projects, Yusys has templates for migration plans which reduces execution risk.

• Risk Management & Support: Yusys provides strong support and risk management for core banking projects. Being a large firm, it usually embeds teams on-site for project duration, covering project management, technical support, and even business consulting. Yusys has a “Financial Innovation and Operation” service division that helps banks with operational support post-go-live (共推金融业数字化进程：宇信科技与华为的合作与蜕变-华为). The core system itself has built-in risk controls: e.g., an internal control center, and integration with Yusys’s risk management systems (for credit risk, operational risk) (核心系统-中国银行业IT解决方案市场中领军企业之一). This means compliance and risk checks are part of processes (loans won’t disburse without passing internal risk rules, etc.). Yusys’s partnership with Baidu’s AI (Wenxin model) could enhance risk management via AI-driven anomaly detection or intelligent customer service, though that is emerging. From a delivery standpoint, Yusys has a long track record with relatively few public failures, indicating effective project risk mitigation. It is certified in CMMI5 (common for major vendors) and likely ISO standards for service management. To manage technical risk, Yusys not only tests in labs but often first deploys new tech in smaller banks (acting as pilots) before scaling up to bigger banks – this incremental approach has been observed in how distributed databases were first tried in a rural bank scenario together with Yusys (共推金融业数字化进程：宇信科技与华为的合作与蜕变-华为) (共推金融业数字化进程：宇信科技与华为的合作与蜕变-华为). In terms of ongoing support, Yusys offers 24/7 technical support and periodic health checks. Because it serves so many banks, it can also rapidly apply patches or regulatory updates across clients, reducing risk of non-compliance for its users.

• Notable References & Case Studies: Yusys’s client list is exhaustive. Notably, it has been a key IT solutions provider to China Construction Bank (CCB) (one of the big four) since early on, initially in online banking and later in other systems (背靠百度，宇信科技难言增长 - 妙投). Yusys has delivered core banking or critical modules to policy banks (like Agricultural Development Bank) and many joint-stock banks. In 2022-2023, Yusys won a contract to implement a new-generation core for a multinational bank’s Hong Kong and overseas branches, beating international competitors (背靠百度，宇信科技难言增长 - 妙投) – a strong endorsement of its product quality. Domestically, Yusys has helped urban commercial banks (e.g., Bank of Beijing, Bank of Shanghai) and rural commercial banks modernize cores (often in partnership with Huawei’s infrastructure) (共推金融业数字化进程：宇信科技与华为的合作与蜕变-华为). A recent example is its joint solution on Huawei’s Kunpeng architecture for Hangzhou Bank, aimed at building a new distributed core system (共推金融业数字化进程：宇信科技与华为的合作与蜕变-华为). Yusys is also active in smart rural finance projects, enabling rural banks to offer digital banking via its core plus online banking packages. Another case: Yusys worked with Ant Financial’s tech to deliver a core system on OceanBase for a regional bank, one of the first of its kind, demonstrating its adaptability to different platforms (神州信息新一代银行核心系统联合解决方案 - OceanBase). Furthermore, Yusys’s dominance in credit systems (12.41% market share in loan management platforms in 2022) (背靠百度，宇信科技难言增长 - 妙投) means many banks’ lending operations (post-loan accounting etc.) run through Yusys software, often integrated into the core. This gives it a foot in the door to eventually replace full core systems. While specific bank names for core deployments are not always public, Yusys’s breadth of partial and full deployments and its involvement in essentially every major Chinese bank in some capacity speaks to its credibility as a core vendor.

DCITS (Digital China Information Service Co., a.k.a. “Shenzhou Info”)

Overview: DCITS is a veteran player in China’s core banking scene – having provided core banking technology for over 30 years. It is often dubbed the “core banking system leader” in the domestic market, reportedly holding the #1 market share in core banking solutions in China for 11 consecutive years up to 2022 (分布式核心业务系统 - 金融科技重磅产品 - 神州信息官网-成为领先的金融数字化转型合作伙伴). DCITS’s flagship core product is called Sm@rtEnsemble, a new-generation distributed core banking system built on the company’s self-developed platform (Sm@rtGalaxy) (分布式核心业务系统 - 金融科技重磅产品 - 神州信息官网-成为领先的金融数字化转型合作伙伴). DCITS has participated in over 100 bank core system projects, including major state-owned banks, joint-stock banks, city and rural banks (神州信息核心系统建设再获利好，中标全国性股份制银行). It was an early pioneer, involved in core system construction for a policy bank as far back as 2003, and has since been the “first implementer” for many banks’ core modernization (神州信息核心系统建设再获利好，中标全国性股份制银行). DCITS’s solutions emphasize robust engineering, parameterization, and independence from foreign technology. The company also actively aligns with China’s FinTech innovation and localization requirements (it’s deeply involved in the “Xinchuang” ecosystem). In sum, DCITS is a powerhouse known for reliable (if not flashy) core systems that power a large swath of Chinese banking.

• High Performance & Scalability: The Sm@rtEnsemble core is designed to handle China-scale banking workloads. It adopts a fully distributed processing mechanism across all layers – from the service runtime platform, to data caching, to data storage (神州信息新一代分布式银行核心系统 | PingCAP 平凯星辰). This ensures that no single bottleneck (like a centralized database or mainframe CPU) limits throughput. By leveraging distributed transaction processing and data sharding, the system can process high volumes of transactions concurrently and store massive amounts of data with linear scalability. DCITS highlights that Sm@rtEnsemble achieves high scalability, availability, and flexibility, meeting banks’ needs for “high concurrency, large data volumes, sudden workload spikes, and agile response” (分布式核心业务系统 - 金融科技重磅产品 - 神州信息官网-成为领先的金融数字化转型合作伙伴). In practice, DCITS cores have been proven in some of the largest institutions. For example, China’s big banks that handle millions of transactions per day have used DCITS components (if not full cores). DCITS also supports various distributed database technologies to scale the data tier; it has demonstrated its core on PingCAP TiDB (for horizontal scaling and HTAP) (神州信息新一代分布式银行核心系统 | PingCAP 平凯星辰) and on Ant’s OceanBase (another high-performance distributed DB) (神州信息新一代银行核心系统联合解决方案 - OceanBase). The ability to use in-memory data grids and caching (part of Sm@rtGalaxy) further boosts performance for read-heavy workloads. Additionally, DCITS cores can utilize parallel processing for end-of-day batch jobs, shrinking batch window on large data sets. Importantly, by not relying on legacy mainframes, DCITS cores allow banks to scale out on cost-effective hardware, reducing the traditional performance-cost tradeoff. Numerous successful high-volume deployments attest to DCITS’s performance – e.g., Agricultural Bank of China’s overseas core, Postal Savings Bank’s new core modules, etc., were known to involve DCITS technology, coping with nationwide volume.

• Modern (vs Legacy) Architecture: DCITS has evolved from legacy integrated architectures to a modern componentized architecture. Sm@rtEnsemble is built on open platform technology and SOA principles, with loosely coupled, independent business services (神州信息新一代银行核心系统联合解决方案 - OceanBase). It fully embraces microservices and cloud-native architecture in its latest incarnation (2023年神州信息研究报告：银行核心系统龙头厂商，全面抓住信创机遇). The system essentially features a “dual-core architecture”: many Chinese references note DCITS building a “双核” (dual core) mode, which typically means separating the ledger (accounting core) from the transaction processing core, each as independent modules that sync in real-time. This was done to improve performance and resilience (ledger updates can be decoupled from transactional flow). DCITS explicitly mentions “Lego-like freely combinable system modules”, indicating a highly modular design where services can be composed or reassembled as needed (神州信息“新一代分布式核心系统”上市，重塑银行竞争力). The Sm@rtGalaxy platform underpins this, providing common services and a unified infrastructure for all microservices. The architecture supports cloud deployment and containerization – DCITS has worked with Docker/Kubernetes environments and even released its own LightOS (Linux-based OS tuned for financial core systems) to optimize performance on open hardware (恒生电子发布操作系统LightOS，聚焦金融核心系统信创需求). A key architectural feature is no vendor lock-in at any layer: Sm@rtEnsemble is not tied to any third-party software or hardware and supports all major domestic servers, databases, and middleware (分布式核心业务系统 - 金融科技重磅产品 - 神州信息官网-成为领先的金融数字化转型合作伙伴). This means the architecture is abstracted enough to run on various UNIX/Linux flavors, middleware like WebSphere or local equivalents, and databases from Oracle to open-source or Chinese-made. Such neutrality is a deliberate design to meet banks’ “tech independence” goals. In contrast to legacy cores that were monolithic and often tied to specific high-end UNIX or mainframe systems, DCITS’s modern architecture is open, distributed, and flexible – yet engineered specifically for banking (with strong consistency and ACID transaction support over the microservices). It’s also cloud-native in the sense of enabling elastic scaling and resilient deployment (the company notes Chinese banks have successfully applied cloud-native tech to core systems for flexible resource allocation and fast response ([PDF] contents - DCITS)).

• Integration & External Connectivity: DCITS core systems are known for their integration-friendly design, supporting extensive interoperability with external channels and subsystems. The Sm@rtEnsemble core exposes Financial Services Standard Interfaces for all core functions (神州信息新一代分布式银行核心系统 | PingCAP 平凯星辰). Through these interfaces (likely web services or APIs conforming to ISO20022 or other standards), the core’s capabilities (account opening, payments, loan processing, etc.) can be consumed by peripheral systems. DCITS, being a long-time systems house, often supplies not just the core but also integration middleware to banks. Its core can work in conjunction with DCITS’s enterprise service bus and messaging systems. The system is built to handle “hundreds of peripheral systems” smoothly – as seen in one deployment, after going live, the new core effectively supported docking requirements of hundreds of peripheral subsystems with smooth operation (Sunline's Observation: 10-year History of Core Changes in China's Banking Industry). DCITS emphasizes loose coupling, which makes integration easier: changes in one module (say, adding a new delivery channel) don’t require reworking the core. The core also supports open banking and API management features. DCITS was an early partner with API standards in China and can enable banks to expose services externally (with appropriate security). Moreover, DCITS’s core readily integrates with Chinese payment networks (UnionPay, NetsUnion) and fintech platforms. Many banks using DCITS core have connected it to WeChat Pay, Alipay, and other FinTech apps via open APIs or through an API gateway layer – given DCITS’s clients include large retail banks, such integration is mandatory and well-tested. Additionally, DCITS actively works with SIs and partners, so its core often comes with a library of adapters for common external systems (ATM switch, credit card system, etc.). In summary, integration is a forte: the core provides standard interfaces, and DCITS ensures that whatever mix of external systems a bank has (old or new), the core can interface with them – either natively or via an integration project.

• Configurability & Customization: One of DCITS’s hallmark features is extensive parameterization. Sm@rtEnsemble is built to be heavily driven by parameters and a product factory configuration approach (神州信息新一代分布式银行核心系统 | PingCAP 平凯星辰) (分布式核心业务系统 - 金融科技重磅产品 - 神州信息官网-成为领先的金融数字化转型合作伙伴). All business modules (deposits, loans, etc.) have their rules and product definitions managed through a unified Parameter Management Platform】, and the system allows the bank to configure various financial products (with different characteristics) by assembling parameters in the Financial Product Factory (神州信息新一代分布式银行核心系统 | PingCAP 平凯星辰). This means a bank can launch new product variants (a new deposit type, a new loan offering) by cloning and tweaking parameters rather than coding. The parameterization covers interest calculation methods, fee schedules, transaction limits, GL posting rules, etc. The core’s Lego-like modularity also extends to customizing processes: banks can turn modules on/off or reconfigure process flows. DCITS also supports multi-institution and multi-ledger setup via config (useful for banks that have multiple legal entities or branches on one instance). Customization beyond parameters is achieved via DCITS’s toolkit (ModelB@nk development framework) which allows development of custom business logic as separate components that integrate with the core’s service interfaces (神州信息新一代银行核心系统联合解决方案 - OceanBase). Essentially, DCITS provides a blueprint of a bank’s core with best-practice processes, which is then tailored to the specific bank through parameter settings and selective enhancements. This approach speeds up implementation while still accommodating unique requirements. It also ensures that future upgrades are easier, as the core product remains standard and customizations are layered via parameters or add-ons. Banks have leveraged this to respond quickly to market changes – for example, during sudden interest rate liberalization or new regulatory rules, DCITS core banks could update parameters and comply rapidly, a task that would be arduous in hard-coded legacy systems. Overall, DCITS offers high configurability**, reducing reliance on vendor intervention for every change.

• Business Functionalities: DCITS’s Sm@rtEnsemble covers full retail and corporate banking functionality. It includes modules such as Customer Information File, Core banking services (deposits of all types, loan servicing for all loan products, payments and remittances, funds transfers), General Ledger and accounting, Operations management (user management, branch operations, etc.), and Risk management modules (神州信息新一代分布式银行核心系统 | PingCAP 平凯星辰). Essentially, it’s an end-to-end core processing suite. The core supports multi-currency, multi-branch, multi-timezone operations (important for banks with overseas branches). DCITS also provides solutions for regulatory reporting, anti-money laundering, and other compliance areas that tie into the core. One distinctive capability of DCITS’s core is its dual-core (twin-engine) structure: it can maintain parallel transaction processing and accounting books, which enhances reliability for financial reporting and allows advanced features like real-time profit and loss calculation. Furthermore, DCITS core can operate in a “dual active” data center mode, supporting geo-redundancy for critical banking functions. For business innovation, DCITS has introduced features like componentized business services that can be reused across channels – for example, a “loan disbursement service” used by branch tellers, mobile app, and internet banking uniformly. This improves consistency and agility. Additionally, DCITS has expanded its product to cater to newer business lines: e.g., it supports internet finance scenarios, inclusive finance (micro-loans), and it can integrate with fintech services (like alternative credit scoring) through its open APIs. Its core is often deployed along with a DCITS Enterprise Service Bus and mid-platform that provides analytical and marketing capabilities. In summary, DCITS core is functionally rich and proven in handling all daily banking operations, with particular strength in retail banking (many city banks use it as a turnkey core) and increasing capabilities in corporate banking and wealth management via extended modules.

• Competitive Advantages: DCITS’s primary advantage is its proven track record and market leadership in China. Being the incumbent in many core system projects, it has unparalleled experience in local requirements (regulations, payment systems, language, etc.) and a reputation for delivery success. IDC reports have placed DCITS as the market share leader in China’s banking IT solution market, especially in core banking, for many years (分布式核心业务系统 - 金融科技重磅产品 - 神州信息官网-成为领先的金融数字化转型合作伙伴). This longevity builds trust; banks know DCITS is a stable, big player (it is part of the Digital China group, with roots from Lenovo) that will be around to support them. Another advantage is technology independence and compliance with the national push for secure, controllable IT. Sm@rtEnsemble does not rely on any foreign proprietary platforms and fully supports Chinese alternatives (servers, OS, databases) (分布式核心业务系统 - 金融科技重磅产品 - 神州信息官网-成为领先的金融数字化转型合作伙伴). In an era where regulators encourage reducing foreign tech in banks, this is a major selling point. DCITS is also often the first mover in new trends for domestic banks: e.g., first to implement a core on a domestic distributed database (one case involved a rural bank using a Chinese DB, proving the concept) (Sunline's Observation: 10-year History of Core Changes in China's Banking Industry). Its willingness to innovate within China’s ecosystem – and successful case studies doing so – give it an edge for banks aiming to modernize while staying compliant. Additionally, DCITS provides a one-stop solution environment: it has subsidiaries and partners covering hardware, cloud services, and integration (for instance, its affiliate “CloudCore” (Yunhe) Network focuses on cloud and core banking services (分布式核心业务系统 - 金融科技重磅产品 - 神州信息官网-成为领先的金融数字化转型合作伙伴) (分布式核心业务系统 - 金融科技重磅产品 - 神州信息官网-成为领先的金融数字化转型合作伙伴)). This enables it to deliver comprehensive projects (from infrastructure to application) which some smaller vendors cannot. Moreover, DCITS’s methodology of heavy parameterization means faster implementation and updates – banks can adapt more quickly, which is a competitive advantage in a fast-changing market. Lastly, DCITS benefits from scale: it has a large support network and can mobilize big teams for core projects, which large banks find reassuring when undertaking multi-year core replacements.

• Technology Stack & Architecture: DCITS’s Sm@rtEnsemble runs on a Java-based, open stack. Earlier versions (branded ModelB@nk) ran on J2EE app servers (WebLogic, etc.) with Oracle/DB2 databases on UNIX servers. The new distributed version can run on Linux clusters, uses microservices (possibly Spring Cloud or ServiceComb framework), and supports container orchestration. It uses DCITS’s own middleware (Sm@rtGalaxy) for distributed transaction coordination and data caching. Databases: the system is DB-agnostic but optimized for distributed databases in China’s “HTAP” category (TiDB, OceanBase) or traditional RDBMS if needed. It ensures strong ACID consistency across distributed transactions – likely through a combination of two-phase commit and high-performance NoSQL for certain data. The stack also likely includes Kafka or similar for event streaming between services and Redis or a homegrown cache for hot data. On top of the technical stack, DCITS provides a developer workbench for parameter configuration and minor customization (scripts, formula, etc.). For cloud, DCITS cores have been deployed on Huawei Cloud Stack and other private clouds; DCITS even built a financial PaaS with Forms Syntron and others (Fincube, see below) indicating it can operate in a cloud environment with containerized microservices (Huawei and Forms Syntron Release Distributed Open Banking Service Solution for Banks - Huawei). DCITS’s use of LightOS (its custom OS based on openEuler Linux) and involvement with Kunpeng CPUs show it fine-tunes the software to get maximum performance on Chinese hardware (恒生电子发布操作系统LightOS，聚焦金融核心系统信创需求). Security-wise, the stack adheres to Chinese financial security standards and encryption algorithms. The architecture ensures that even though components are distributed, from the bank’s perspective it’s one integrated system – DCITS likely provides an integrated monitoring and O&M console to manage the core’s microservices, making it easier for bank IT to operate the system post-implementation.

• Migration Approach: DCITS typically replaces legacy systems via a gradual, measured approach. Given many of its projects involve replacing very old COBOL/mainframe or first-gen cores, DCITS often runs a parallel test phase where the new core is run with actual data in parallel to the old core for some cycles to compare results (a strategy proven to ensure accuracy). It leverages its parameterization to configure the new core to mimic existing product behavior exactly, which reduces gaps. DCITS also sometimes uses a “double core, double ledger” approach during migration – running both cores and slowly migrating products one by one. For example, a bank might move its savings accounts to the new core first while loans remain on the old, then move loans, etc., until the old core can be retired. DCITS provides data migration tools that can extract data from legacy systems and load into the new core’s databases, with data validation steps. The vendor has experience handling tricky areas like migrating transaction history, which often is enormous for big banks. They might segment history (only move a few years’ data, archive the rest) depending on the bank’s preference. DCITS emphasizes ensuring consistency and balance checks during migration – its core has robust reconciliation features to cross-verify balances between old and new systems for a period of time. Additionally, DCITS coordinates with SIs (if any on the project) for peripheral system cut-over – making sure that channels are rerouted to the new core at the right time. Because DCITS core can run on new hardware in parallel, banks often set up the new core environment separately while the old runs, minimizing disruption until final switch. DCITS’s long list of 100+ core clients means it has developed standard migration frameworks and best practices for different scenarios (be it a small rural bank or a giant bank). As a result, the risk of migration is mitigated by this maturity.

• Risk Management & Support: DCITS takes a very methodical, risk-averse stance in core projects. It usually insists on comprehensive testing (functional, performance, security) in controlled environments (often establishing joint labs with the bank for this). For example, DCITS and PingCAP set up a joint lab to fine-tune the core on TiDB, ensuring stability before deploying (神州信息新一代分布式银行核心系统 | PingCAP 平凯星辰). DCITS’s core has strong fault-tolerance: thanks to distribution, if one node fails, others take over. Features like multi-active deployment improve disaster recovery (no single data center failure will take down the core) (Sunline's Observation: 10-year History of Core Changes in China's Banking Industry). For operational risk, DCITS core provides tools for monitoring and alerting so issues can be caught early. The company’s support model includes on-site support during critical cutover periods and dedicated support teams for each major client afterwards. Being a large company, DCITS has the capacity to respond quickly; it also often assigns a resident support team for a period after go-live. In terms of security, DCITS adheres to all PBoC security standards and has built-in controls (user access management, transaction limits, audit logs, etc.) as part of the core. For risk of project failure, DCITS leans on its iterative approach – they might do multiple “mock go-live” rehearsals with the bank to iron out issues. Also, DCITS’s independence from third-party tech reduces supply chain risk – the bank isn’t reliant on a foreign vendor’s support for a database or OS, as DCITS can support the full stack. This holistic control can be seen as risk mitigation in itself. Lastly, DCITS holds various certifications (likely CMMI5, ISO9001, etc.) ensuring quality processes in development and support.

• Notable References & Case Studies: DCITS has a rich portfolio. Some highlights: In the mid-2000s, DCITS was involved in Agricultural Development Bank of China’s core banking project (policy bank) – one of the earliest modern core implementations in China (神州信息核心系统建设再获利好，中标全国性股份制银行). It subsequently implemented core solutions or key modules in several state-owned banks. For instance, DCITS had a role in Bank of China’s overseas core system and China Construction Bank’s domestic core upgrade (as an integrator/provider of certain components). More recently, China Everbright Bank and China Guangfa Bank (CGB), both joint-stock banks, have engaged DCITS for next-gen core projects. In 2019, DCITS partnered with a regional bank to launch a core banking system on a fully domestic tech stack (hardware + OS + database), which was a breakthrough demonstration of a secure and controllable core system (Sunline's Observation: 10-year History of Core Changes in China's Banking Industry). In 2023, it was reported that a “top-tier city commercial bank” chose DCITS to build its new core, making DCITS the only fintech firm with live core cases across all categories of commercial banks (神州信息中标一Top级城商行项目启动新一代核心系统建设 - 证券时报). DCITS’s Sm@rtEnsemble has been successfully deployed in banks like Bank of Jiangsu, Bank of Ningbo (leading city banks), and many rural commercial banks looking to leapfrog into digital banking. DCITS is also the core system provider for some foreign banks’ China operations, where localization is key. An example of innovation: DCITS collaborated with Huawei to implement Hangzhou Bank’s new distributed core on Kunpeng processors – this project is often cited as a model for commercial banks adopting distributed architecture with domestic tech (共推金融业数字化进程：宇信科技与华为的合作与蜕变-华为). Additionally, DCITS often references that it holds the largest market share among city and rural bank core systems, meaning dozens upon dozens of smaller banks run on its cores. Each of these is a testament to DCITS’s adaptability (these banks have varied needs). The company’s long-term relationships (some clients have stuck with DCITS through multiple generations of core upgrades) illustrate strong client satisfaction.

Forms Syntron (Shenzhen Forms Syntron)

Overview: Forms Syntron is a fintech solution provider originally known for digital banking channels, which has emerged as a notable core banking platform vendor especially in partnership with Huawei. It has roots in Hong Kong and Shenzhen, and a workforce of ~3,000. Forms Syntron’s flagship offering in core banking is the “Fincube” Digital Banking Core – an open banking platform built on distributed architecture and co-developed with Huawei’s infrastructure support (Huawei and Forms Syntron Release Distributed Open Banking Service Solution for Banks - Huawei) (Huawei and Forms Syntron Release Distributed Open Banking Service Solution for Banks - Huawei). Forms Syntron specializes in Bank 4.0-era solutions: highly agile, customer-experience-focused banking systems. Its systems are used by some innovative banks in China and Asia (including Hong Kong virtual banks). While smaller than Sunline or DCITS, Forms Syntron leverages cutting-edge tech (like cloud, AI, Web3) and strong partnerships (Huawei, Microsoft) to punch above its weight. It often co-creates solutions with banks, acting as an incubator for new digital banking models.

• High Performance & Scalability: The Fincube platform is designed for distributed, high-concurrency operation. Built atop Huawei’s FusionCube hyper-converged infrastructure, it inherits high-density computing and storage capabilities for scaling transaction workloads (Huawei and Forms Syntron Release Distributed Open Banking Service Solution for Banks - Huawei). The solution eliminates traditional performance bottlenecks by distributing both application and data layers. For example, it uses Huawei’s FusionStorage to ensure reliable and fast distributed storage, crucial for core banking data integrity and speed (Huawei and Forms Syntron Release Distributed Open Banking Service Solution for Banks - Huawei). In terms of throughput, Fincube’s microservices architecture can scale horizontally; banks can add more FusionCube nodes to increase TPS linearly. Huawei’s testing indicated this platform can flexibly expand capacity to meet surges in mobile and internet banking demand (Huawei and Forms Syntron Release Distributed Open Banking Service Solution for Banks - Huawei). The architecture supports active-active deployments, which means load can be balanced across data centers, further boosting throughput and resilience. A key performance enabler is the “Universe Analytics Platform” Forms Syntron built – it uses FusionCube as the basic unit of distributed architecture and coordinates across them (Huawei and Forms Syntron Release Distributed Open Banking Service Solution for Banks - Huawei). This effectively is a PaaS that handles microservice management, ensuring each service can scale or failover independently without dragging down others. In summary, by marrying software and powerful hardware, the Forms Syntron core platform achieves the performance needed for real-time digital banking, as evidenced by its use in banks that operate fully online (which require very fast response even under heavy user traffic).

• Modern Architecture: Forms Syntron’s core banking solution is fully modern and cloud-native. The Fincube platform is built with a containerized microservices framework called SolApp on the Universe PaaS (Huawei and Forms Syntron Release Distributed Open Banking Service Solution for Banks - Huawei). It consists of a large library of microservices (Forms Syntron boasts a BaaS library of thousands of microservices) that provide granular banking functions (Huawei and Forms Syntron Release Distributed Open Banking Service Solution for Banks - Huawei). This microservice approach means each business capability (e.g., account creation, loan calculation, payment processing) is a separate service that can be updated or scaled independently. The architecture is explicitly aligned with Open Banking principles: Fincube is described as a distributed open platform solution (Huawei and Forms Syntron Release Distributed Open Banking Service Solution for Banks - Huawei). It emphasizes experience-driven design, meaning the architecture was built to easily plug banking services into external customer experiences. One unique architectural concept is that it enables agile innovation – banks can quickly assemble new workflows by picking and choosing from the microservice “Lego” pieces (hence the name Fincube, implying a cube of modular components) (Huawei and Forms Syntron Release Distributed Open Banking Service Solution for Banks - Huawei). The platform leverages Huawei’s IaaS and PaaS – for instance, using Huawei’s API (CubeCenter API) to integrate hardware management into the software’s O&M, achieving more efficient automated operations (Huawei and Forms Syntron Release Distributed Open Banking Service Solution for Banks - Huawei). Essentially, it’s an integrated stack where infrastructure and application layers communicate for optimized performance and management. The architecture is also very open and secure: open in that it can incorporate third-party fintech services easily (via APIs), and secure by design (given it was likely built in compliance with stringent security frameworks and leverages Huawei’s security features). In contrast to legacy monoliths, Forms Syntron’s architecture is event-driven and stateless where possible, which improves resiliency and scalability. Moreover, it’s cloud-agnostic to an extent – while optimized for FusionCube, it can be deployed on other cloud setups (they have deployments on Azure in some international projects). Overall, the architecture is bleeding-edge, incorporating microservices, containerization, DevOps pipelines, and AI readiness (the platform is ready to integrate AI services as microservices for things like smart KYC or robo-advisors).

• Integration & Ecosystem Connectivity: Integration is a focal point for Forms Syntron. The Fincube solution is explicitly an open banking service solution – it provides open APIs to allow banks to connect with fintech ecosystems effortlessly (Huawei and Forms Syntron Release Distributed Open Banking Service Solution for Banks - Huawei). This is critical for collaborating with platforms like WeChat and Alipay, where APIs are used to embed banking features. Forms Syntron’s core exposes all functionalities as services that can be consumed internally or externally. It also supports multichannel integration out-of-the-box: mobile banking, internet banking, branch systems, and third-party apps can all connect to the core via standardized APIs. Because it’s microservices-based, integration is simplified – external systems can call specific microservice endpoints (e.g., check account balance service) without needing to go through a complex monolithic API. Forms Syntron also built a “Lego Open Banking Solution” on top of its microservice framework (Huawei and Forms Syntron Release Distributed Open Banking Service Solution for Banks - Huawei). This likely provides pre-defined integration templates for common open banking needs. For example, if a bank wants to offer account info through a fintech app, there is a ready-made microservice and API for that. Additionally, being co-developed with Huawei, the platform likely integrates well with Huawei’s API gateway and integration middleware, which many Chinese banks use. In international projects, Forms Syntron integrated Microsoft’s AI and analytics services into its platform to create things like Banking Copilot (an AI assistant) (FORMS Syntron Presents Generative AI and Web 3.0 Financial ...), showing the ease of plugging in advanced services. The microservices design also means the platform can integrate with legacy systems via small adaptation services – e.g., a microservice that fetches data from an old system and presents it to new channels. Eliminating performance bottlenecks while integrating was a key design goal; as Huawei’s exec noted, this solution “eliminates performance bottlenecks and flexibly expands capacity to help banks quickly build digital cores and reconstruct core competitiveness in the mobile era” (Huawei and Forms Syntron Release Distributed Open Banking Service Solution for Banks - Huawei). This speaks to integration in the sense that adding new digital services (mobile era demands) won’t degrade core performance, and integration points can scale too. In summary, Forms Syntron’s core is built to connect and co-create – banks using it can easily interface with fintech partners, third-party developers (to build on the bank’s API), and even integrate non-bank services (like lifestyle apps) into their offerings, all via the robust open API framework Fincube provides.

• Configurability & Customization: The Forms Syntron core offers extreme flexibility through microservices composition. Instead of heavy parameterization like some others, its philosophy is to allow banks to configure processes by orchestrating microservices (which is a form of configuration at a higher level). However, it likely also includes a parameter management for product settings. Since Fincube is relatively new, it was built with zero-coding configuration in mind for product design – enabling product managers to tweak attributes of a deposit or loan easily. Also, because of the microservices library (with thousands of services) (Huawei and Forms Syntron Release Distributed Open Banking Service Solution for Banks - Huawei), if a needed functionality isn’t pre-built, one can be added as a new microservice without impacting the rest of the system. This modularity is a boon for customization: banks can deploy only the services they need, customize or extend specific ones, and even develop their own microservices to plug into the platform. Forms Syntron likely provides development tools or a SDK for clients to build or modify microservices (especially since some banks co-create with them). Configurability extends to deployment and scaling – banks can configure business rules that automatically scale certain services when transaction volume increases (aided by the platform’s integration with infrastructure management via CubeCenter API (Huawei and Forms Syntron Release Distributed Open Banking Service Solution for Banks - Huawei)). In short, customizing user journeys, adding new data fields, changing a fee rule, or launching a new product can all be done by adjusting the relevant microservice or its parameters, rather than rewriting a huge codebase. This gives banks agility in creating tailored products. For instance, a bank could quickly create a new type of installment loan product by configuring the parameters in the loan service microservice and adjusting the marketing service to promote it – all within a few weeks. Forms Syntron promotes “agile innovation and operation governance” with this approach, accelerating response to market changes (Huawei and Forms Syntron Release Distributed Open Banking Service Solution for Banks - Huawei).

• Business Functionalities: The Fincube core platform covers a broad range of banking functions, delivered as microservices. It supports retail banking services (accounts, deposits, loans, payments), small business banking, and potentially core aspects of corporate banking (though its sweet spot is retail digital banking). Each traditional module (like CASA – current and savings accounts, lending, payments, cards, etc.) is broken into microservices. For example, one might have microservices for account opening, account maintenance, interest calculation, end-of-day batch, transaction posting, funds transfer, bill payment, etc. The platform also natively includes what one might call “digital core” features: it likely has built-in support for e-KYC, digital customer onboarding, and real-time analytics embedded in transactions (since it touts an analytics platform integration). It also comes with embedded security and risk features – e.g., fraud detection microservices, anomaly alerts – crucial when integrating with open ecosystems. Forms Syntron has demonstrated capabilities in emerging tech: e.g., it developed FINNOSafe, an AI-driven risk management platform (Banking Copilot) for smart operations (FORMS Syntron Presents Generative AI and Web 3.0 Financial ...), which can complement the core by providing AI assistance in risk control and operations. We can infer that business functionalities include not just the standard banking ledger and transaction processing, but also customer experience enhancements like personalized recommendations (via AI), and multi-channel seamless experiences. For instance, if a customer starts a loan application on a mobile app, the microservice architecture allows that session to be continued in a branch or on a web portal without hiccups – all channels connect to the same set of services. Additionally, Fincube likely supports ecosystem banking functionality: enabling integration of non-bank services for customers (like if a bank wants to offer e-commerce or lifestyle products integrated with accounts, the open API framework allows it). In summary, all core transactional capabilities are present, and they are augmented by agile innovation features (like fast product launch) and AI-driven capabilities that set it apart from older cores.

• Competitive Advantages: Forms Syntron’s competitive advantages lie in its innovation and partnerships. First, the technology itself is state-of-the-art, which appeals to banks looking to leapfrog into digital banking with a fresh start (especially internet or challenger banks). The microservices & open banking focus means it’s not burdened by legacy design – banks that choose it can more easily become platforms in the fintech ecosystem. Second, Forms Syntron has a strong strategic partnership with Huawei. Together, they bring a combined offering: Huawei provides the trusted infrastructure (servers, cloud, database) and Forms Syntron provides the banking software. This synergy is attractive, as seen when Huawei and Forms jointly released the solution to help banks build digital cores and improve competitiveness in the mobile era (Huawei and Forms Syntron Release Distributed Open Banking Service Solution for Banks - Huawei). Huawei’s endorsement lends credibility and ensures performance (since Huawei optimized FusionCube for this use). Many banks in China respect Huawei’s tech; choosing a core that’s co-developed with Huawei can ease internal buy-in, especially for IT departments mandated to use domestic tech. Third, Forms Syntron positions itself as an “incubator and enabler” for fintech innovation (FORMS HK | Your FinTech Thought Leader, Incubator and Enabler) – it co-creates with banks. This consultative, partnership approach can differentiate it from larger vendors: it’s more nimble and willing to tailor or develop new features alongside the client. Banks that want a very customized or cutting-edge solution might find this appealing. Fourth, the flexibility and speed of Fincube is a competitive edge. In an environment where product time-to-market is critical, being able to roll out updates or new services in days rather than months is a big plus. Fifth, Forms Syntron leverages AI and Web3 trends (per Microsoft collaboration and others) to offer things like generative AI assistants and blockchain integration that legacy-focused vendors might not yet provide (FORMS Syntron Presents Generative AI and Web 3.0 Financial ...). Finally, being based in Hong Kong/Shenzhen, it has cross-border experience and understands both Chinese mainland banking and international best practices. It helped build Fusion Bank (Hong Kong) – a virtual bank – from scratch in record time (with WeBank’s tech) and also has projects in Southeast Asia. This international exposure can be a selling point for Chinese banks with overseas aspirations or for foreign banks in Asia looking for a modern core. Overall, though smaller, Forms Syntron competes by being more innovative, highly collaborative, and aligned with top tech giants, offering a very modern alternative to the traditional core systems.

• Technology Stack & Architecture: The Forms Syntron core runs on a modern tech stack heavily utilizing containerization and microservices. It is built likely using Java/Spring Boot for microservices or possibly Go for some services – the specifics aren’t public, but given the involvement of FusionCube, likely a mix of Java and C++ for low-level components. It uses Docker containers orchestrated probably by Kubernetes (FusionCube can integrate with CloudContainerEngine). The “SolApp” containerized microservice framework suggests a custom layer perhaps on Docker/K8s optimized for financial services (Huawei and Forms Syntron Release Distributed Open Banking Service Solution for Banks - Huawei). The PaaS (Universe Analytics Platform) indicates use of big data tech – possibly Hadoop/Spark for analytics within the core, enabling HTAP. Data storage uses Huawei FusionStorage (a distributed storage system) and potentially GaussDB (Huawei’s distributed database) for structured data, plus possibly NoSQL databases for specific services (Huawei and Forms Syntron Release Distributed Open Banking Service Solution for Banks - Huawei). The system likely leverages APIs (REST/JSON) and possibly gRPC for internal service calls. Front-end channels connect via REST APIs or messaging (there might be an API gateway component). The integration of CubeCenter API shows deep hooks for things like automated scaling – e.g., the software can instruct the hardware layer to spin up more VMs/containers as needed (Huawei and Forms Syntron Release Distributed Open Banking Service Solution for Banks - Huawei). This indicates a cloud-native, DevOps-friendly stack (auto-scaling, self-healing). For development, the platform probably comes with CI/CD pipeline support to deploy new microservices rapidly. Security is built in at every microservice (with OAuth2 or JWT for API auth, etc.). Because of Microsoft collaborations, some deployments might use Azure cognitive services via API calls. In summary, the stack is an enterprise cloud stack: microservices, APIs, container orchestration, distributed DB/storage, and an automation/orchestration layer bridging to the hardware/cloud management. This is arguably one of the most advanced stacks among Chinese core vendors.

• Migration Approach: Forms Syntron often targets greenfield digital banks or modular modernization rather than ripping out an entire old core at once. For banks with legacy systems, Forms can deploy its microservices alongside the old core to augment capabilities (for example, launching a new digital product on Fincube while legacy handles old products). Over time, more services can transition to Fincube until the old core is minimal. Because the architecture is open, it’s relatively straightforward to integrate with a legacy core – use the legacy as just a system-of-record while new services run on Fincube, then gradually decommission legacy modules. For a full migration, the approach would be similar to others: possibly run in parallel and migrate by product line or customer segment. The microservice design even allows for a gradual microservice adoption: a bank could start by using a few microservices from Forms Syntron (say, a new API layer on top of legacy via their microservices), then progressively increase reliance on the new platform. Forms Syntron and Huawei likely provide a reference methodology for migration: perhaps starting with building an API layer that abstracts the old and new systems (so channels don’t see a change), then replacing backend pieces one by one. Their emphasis on agile and governance suggests they carefully govern the coexistence of two cores, ensuring consistency. One advantage Forms has is that its platform can sit on cloud infrastructure parallel to old on-prem systems easily and then take over progressively; its small footprint microservices can be deployed incrementally. So, risk can be managed by gradually siphoning off transactions to the new core (like beta testing parts of the bank on it). Moreover, Huawei’s involvement can bring robust migration tools especially if legacy data needs to move (Huawei has data replication and ETL tools). For brand new banks (like WeBank or virtual banks), obviously no migration needed – and that’s where Forms excels by launching from scratch very quickly. In summary, their migration approach is flexible: either big bang for new banks, or coexist and gradually replace for incumbents, always with a strong API layer to insulate customer channels from the transition.

• Risk Management & Support: Forms Syntron, in partnership with Huawei, provides strong support to mitigate risks. Huawei’s backing means infrastructure risk (scalability, security, reliability) is minimized – the hardware and base software are top-notch and well-supported. Forms Syntron itself likely provides 24/7 support, and because it’s smaller, possibly more hands-on executive attention to each project. The platform’s design inherently reduces certain risks: microservices isolation means a failure in one service (e.g., credit card module) doesn’t crash the whole system, improving overall uptime. For operational risk, the platform can deploy in multiple active data centers (improving disaster recovery, RTO/RPO). Forms Syntron also emphasizes DevOps and continuous delivery, which reduces the risk of big disruptive upgrades; instead changes are incremental and tested. Regarding security risk, working with a big partner like Microsoft (for AI) and Huawei means compliance with global and Chinese security standards. In project execution, Forms likely works very closely with the bank’s team (almost as one team), which helps catch issues early and adapt – their co-creation mantra inherently is a risk-sharing mechanism (the bank and vendor solve problems together real-time). Ensuring project success also comes from the fact that their references are innovative but fewer, so they are likely to be selective and not overstretch, thereby maintaining high success rate. Indeed, their references like Zhejiang E-Bank (an online-only bank) or Fusion Bank HK went live relatively smoothly, implying robust risk management. Forms Syntron also has the backing of its parent groups for financial stability, reducing risk of vendor failure. Finally, as part of support structures, they likely embed in the bank’s site for some time post-go-live and provide training to bank IT staff to manage the new microservices environment, ensuring a smooth handover.

• Notable References & Case Studies: Forms Syntron’s notable references include working with Huawei and China Merchants Bank in developing open banking solutions, and delivering core components for WeBank (Tencent’s digital bank) in its early days (Forms Syntron was involved in WeBank’s front-end and perhaps some middleware). The big highlight is the Huawei-Forms Fincube solution launch in 2019 which was implemented at some banks in China to demonstrate Bank 4.0 capabilities (Huawei and Forms Syntron Release Distributed Open Banking Service Solution for Banks - Huawei). While specific bank names in mainland China are not widely public, one case alluded to is a joint-stock bank adopting Fincube to modernize its mobile banking and core for the digital era (possibly China Merchants Bank’s exploratory project, given CMB’s known for digital innovation). In Hong Kong, Forms Syntron had success: it helped launch Fusion Bank, one of Hong Kong’s virtual banks, by providing the digital core in partnership (notably, WeBank also provided tech – perhaps Forms Syntron was an integrator or provided certain modules). Additionally, ZA Bank and others in HK leveraged expertise from companies like Forms. Forms Syntron has also expanded to Thailand (as indicated by a Thailand branch and LinkedIn presence (Forms Syntron Thailand | LinkedIn)) and other ASEAN markets, working with regional banks to incubate fintech solutions. A public example: Philippines – Forms Syntron engaged there likely through Huawei’s projects to provide digital core components to local banks aiming to replicate China’s fintech success. On the innovation side, Forms Syntron showcased Banking Copilot and FINNOSafe at Microsoft events – these are AI-powered banking operation and risk platforms (FORMS Syntron Presents Generative AI and Web 3.0 Financial ...), which tie into its core offerings to add value. While not as widely deployed as some competitors, each of Forms Syntron’s case studies is cutting-edge, often first-of-a-kind implementations (e.g., first fully cloud-native core for X bank, first AI-integrated core operations for Y bank, etc.). This positions Forms Syntron as a thought leader, and banks engaging them are often those pursuing aggressive digital transformations.

Ping An OneConnect (OneConnect Financial Technology)

Overview: OneConnect is the fintech arm of Ping An Group, established to commercialize Ping An’s in-house technology. It provides a cloud-native core banking platform (among many other fintech solutions) that caters especially to digital banks and smaller institutions looking to leapfrog legacy tech. OneConnect’s core banking solution (sometimes branded “Gamma Core” or “OneCosmo” in global markets) is an AI-driven, Big Data-enabled core platform delivered via Ping An’s Financial Cloud (OneConnect launches BaaS solution powered by Pismo) (OneConnect partners Pismo for unified digital banking solution ...). OneConnect leverages Ping An’s deep experience in financial services – Ping An Bank’s own digital overhaul and the launch of Ping An’s virtual bank in Hong Kong (PAOB) – to offer an advanced core-as-a-service. By 2025, OneConnect has been expanding this offering in China and internationally (Southeast Asia, Middle East), often targeting institutions wanting rapid digital transformation. Their core platform emphasizes cloud deployment, open APIs, intelligent operations, and fast product innovation.

• High Performance & Scalability: OneConnect’s core platform is built on a distributed, cloud-native architecture specifically engineered to overcome performance limits of traditional cores. It can achieve far higher throughput than legacy (which struggled at ~20-100 TPS) (Cloud Native Core System Platform). By using microservices and elastic cloud resources, OneConnect’s core scales horizontally; under high load, new instances of services can spin up automatically. The solution is deployed on Ping An’s robust cloud infrastructure, which means it can inherit virtually unlimited scalability (Ping An’s cloud is built on top of Alibaba Cloud in some cases, providing strong underlying capacity). OneConnect cites that older architectures had server utilizations of only ~10-20% and were cost-inefficient (Cloud Native Core System Platform), whereas their cloud core optimizes resource use and can handle spikes in demand efficiently. The platform was proven with Ping An’s own needs – Ping An Bank serves tens of millions of customers and their core processes huge daily transaction volumes; those lessons are embedded in OneConnect’s product. Moreover, OneConnect uses high-performance computing and data tech from Ping An – for example, Ping An is known for its AI and analytics, which likely optimize processes (like using AI to predict and pre-scale for peak times). In summary, the OneConnect core can comfortably scale to support large user bases (millions of customers) and high transaction concurrency (suitable for integrating with payment platforms that generate large bursts of transactions). Being cloud-based also allows elastic scalability: a bank can start small and the system will scale as their business grows, without re-architecture.

• Modern Architecture: The architecture is cloud-native, microservices, and API-first. OneConnect’s core provides “basic services such as deposit, loan, customer information” as a set of microservices on a distributed architecture (Cloud Native Core System Platform). It includes 300+ basic APIs out-of-the-box (Cloud Native Core System Platform) (Cloud Native Core System Platform) – indicating a very granular service-based design. The system is likely built using spring-cloud microservices, containerized via Docker, orchestrated by Kubernetes, and runs on Ping An’s cloud (which could be OpenShift or Alibaba Cloud container service under the hood). All components are designed as independently deployable services, which aligns with modern twelve-factor app principles. The data layer is probably a combination of distributed SQL databases (Ping An has used Oracle RAC historically, but for OneConnect they might use a NewSQL or a cloud-native DB) and NoSQL for specific scenarios (Ping An is known to use MongoDB and Elasticsearch in some apps). The architecture is also AI-integrated – meaning it’s built to easily plug in AI modules (for fraud detection, credit scoring, etc.). OneConnect touts capabilities in AI and blockchain, so presumably parts of the core (like identity verification or transaction validation) can integrate with a blockchain service or AI service if needed (Core Banking: Big Data Analytics, Artificial Intelligence & Blockchain). The platform is multi-tenant capable (as it’s offered as SaaS to multiple banks), yet each bank’s data is isolated securely. OneConnect being cloud-native implies devops is built-in: continuous updates, containerized deployments, and autoscaling are baseline features. Compared to legacy or even some distributed cores that still had big modules, OneConnect’s microservices are fine-grained, which offers agility and performance (each microservice focuses on a single business capability). The architecture also includes an “intelligent parameter system” (Cloud Native Core System Platform), which is likely a central configuration service that all microservices consult for business rules – this is a modern twist to make governance easier in a microservices landscape. In short, OneConnect’s architecture aligns with the latest in cloud software engineering and is arguably one of the most modern in production in China (given it was built in the last few years from scratch).

• Integration with External Systems: OneConnect’s core is built to integrate seamlessly via APIs. It provides hundreds of RESTful APIs that allow external applications (mobile apps, web portals, third-party fintech apps) to interface with core services (Cloud Native Core System Platform). This makes it easy to connect with super-apps like WeChat or Alipay. For example, to link with WeChat Pay, a bank using OneConnect’s core would expose specific payment APIs to WeChat – the core can handle receiving those requests and processing them in real-time. The platform also supports easy integration with other OneConnect modules (OneConnect offers a whole ecosystem: digital onboarding, risk management, etc.). Using standardized APIs means integrating things like eKYC video verification or anti-fraud services (which OneConnect also has) is plug-and-play. Furthermore, OneConnect’s platform is cloud-based, so it can sit “on top” of a bank’s existing systems as well, integrating through APIs or middleware to any remaining on-prem systems. It also likely uses an integration layer or iPaaS to connect to older systems if needed (for banks in transition). Given that OneConnect is a relatively new platform, most integrations are forward-looking: connecting to mobile front-ends, partner ecosystems, credit bureaus, payment networks, etc. The core’s API-centric design fulfills the need for open banking – banks can extend selected APIs to fintech partners securely. Also, Ping An’s fintech background ensures that the core can handle integration with emerging tech (e.g., IoT for smart finance or big data platforms for credit scoring). In summary, integration is a key strength, as evidenced by the emphasis on 300+ APIs enabling a “core operation platform for digital banks” (Cloud Native Core System Platform). These APIs and microservices allow a bank to rapidly build out an ecosystem around the core, something that used to be a major challenge for legacy cores.

• Configurability & Customization: The OneConnect core is designed for rapid product configuration and deployment. It includes an “intelligent parameter operation system” for configuration (Cloud Native Core System Platform). This likely refers to a GUI tool where bank users can configure product parameters (interest rates, fees, etc.), define business rules, and set up workflows without coding. OneConnect emphasizes achieving “3 core values: simpler, smarter, better experience” for bank transformation through this platform (Cloud Native Core System Platform). “Simpler” likely refers to how configuring and launching a new banking product or process is simplified to maybe a few days of parameter changes rather than long development. Ping An’s own experience launching many fintech products means the platform was built with a product factory mindset. Customization can be done at multiple levels: via parameters for standard changes, and for deeper customization, since it’s microservices-based, new services can be developed and added to the platform (OneConnect might do this for the client or allow the client’s IT to do it). Also, because it’s provided as a service, OneConnect can roll out new features (like compliance updates or new product templates) to all clients, which they can then choose to enable via configuration. This shared SaaS model means banks benefit from collective updates while still configuring their unique offerings. Additionally, OneConnect’s use of AI means some customization happens automatically – e.g., the system might automatically adjust workflows if AI identifies an optimization, making the operations “smarter” with minimal human tuning. The bottom line is that a bank using OneConnect can launch new products in weeks instead of months and can tailor the system extensively via config – a huge improvement from legacy cores where even minor changes required coding and testing cycles.

• Business Functionalities: OneConnect’s core covers the fundamental banking services: customer management, deposit accounts, lending (from origination to servicing), payments, and general ledger (Cloud Native Core System Platform). It is essentially a full retail core banking system. In addition, given Ping An’s focus, it likely has integrated credit scoring and risk management tools (for lending decisions) and analytics dashboards as part of the core offering. It also supports digital-specific functions like e-wallet management, real-time payments, and potentially SME financing (Ping An’s virtual bank in HK, PAOB, focuses on SME lending, so that functionality is in the platform). The core is built to handle multi-channel transactions natively, so whether transactions come from a branch teller interface, a mobile app, or a third-party payment app, they are processed uniformly. OneConnect also supports regulatory compliance reporting required in China – those features are built into the core or provided as add-ons, so banks can generate the necessary PBoC and CBIRC reports from the system. Moreover, OneConnect’s ecosystem approach means banks can opt-in to additional modules: e.g., anti-money laundering checks, document management, etc., which tie into the core processes. A standout feature is likely smart loan processing – using AI and big data, the core can automate loan approval flows, something Ping An developed in-house and now offers to others. The inclusion of blockchain tech in trade finance or supply chain finance might also be part of the extended capabilities (OneConnect has worked on blockchain trade platforms). So beyond standard deposits and loans, the platform is capable of enabling innovative digital banking products: think virtual credit cards, instant micro-loans, dynamic interest accounts, etc. The “smarter” aspect suggests that repetitive operations may be automated by AI, and the “better experience” suggests customer-facing features like instant account opening, 24/7 availability, and personal financial management tools plugged into the core. In essence, OneConnect’s core provides all key banking functions with an overlay of AI-driven enhancements and digital-first features.

• Competitive Advantages: OneConnect’s competitive advantage is twofold: technological and ecosystem-based. Technologically, it’s one of the few true cloud-native banking platforms originating from China, which sets it apart from older architectures. This means quicker deployments (as SaaS), lower IT costs for clients (no need for their own data center), and rapid innovation cycles. For many regional and smaller banks in China, this is very appealing – they can get a cutting-edge core without massive capex. Secondly, OneConnect brings Ping An’s vast AI and data capabilities. Ping An is a leader in AI in finance (e.g., face recognition for onboarding, voice AI for customer service, etc.). These capabilities are embedded, giving OneConnect an edge on intelligent banking features. Few core vendors can natively offer AI modules with the pedigree of Ping An’s (for instance, Ping An’s AI reduced loan processing times significantly at Ping An Bank). Thirdly, OneConnect’s platform is part of a broader suite: a bank can also get digital onboarding, mobile apps, risk management systems, and even insurance systems from the same provider, all pre-integrated. This one-stop-shop for digital transformation is compelling. Fourth, OneConnect’s solutions are often delivered in a partnership model – e.g., it partnered with a Brazilian fintech (Pismo) to enhance its core offering for international markets (OneConnect launches BaaS solution powered by Pismo). This shows it’s aggressive in improving and localizing its tech, staying on the cutting edge. For Chinese banks, having a local vendor that’s NYSE/HKEX-listed (hence stable) and tied to Ping An (a trusted financial powerhouse) is an advantage over foreign vendors. Also, OneConnect’s platform by design helps banks achieve faster time-to-market for new products – something explicitly highlighted as solving the pain point of slow product launches (3-6 months reduced to much less) (Cloud Native Core System Platform). Another edge is cost efficiency: because it’s cloud/SaaS, banks pay for what they use, and they don’t have to maintain expensive hardware with low utilization (one stat said legacy cost per account was high with low server utilization, which OneConnect cuts dramatically) (Cloud Native Core System Platform). Finally, in the context of China, OneConnect aligns with regulatory directions: it keeps data in-country (on Ping An cloud), and facilitates digital inclusive finance (Ping An’s mission to serve SMEs and individuals via technology, which regulators encourage). Summing up, OneConnect offers a modern, intelligent core with rapid deployment and an ecosystem of value-added services, backed by the credibility of Ping An – a combination that differentiates it strongly.

• Technology Stack & Architecture: The stack is built on Ping An’s Cloud platform. Likely components: microservices in Java or possibly Go; Spring Cloud Alibaba for service registry/discovery; a Kubernetes cluster on Ping An Cloud for deployment; API Gateway for external exposure; Distributed databases (possibly OceanBase or a cloud-native SQL like PolarDB, or even Ping An’s in-house OneDatabase if exists); Redis for caching; Kafka for event streaming; and ElasticSearch for searching data/logs. The parameter system suggests a rules engine (maybe Drools or a custom Ping An rules engine) for dynamic business rules. AI components might use Python microservices with machine learning models (Ping An’s NLP, computer vision modules integrated via APIs). For security, it likely uses Ping An’s proven security frameworks (they handle millions of insurance and bank customers so security is enterprise-grade, including encryption, multifactor auth support, etc.). The infrastructure is highly automated – Ping An’s devops processes allow multiple deployments per day; this is inherited in OneConnect’s delivery of updates. Monitoring and logging are integrated (likely using something like Prometheus/Grafana, ELK stack etc.). The core also likely uses open source frameworks extensively (OneConnect has mentioned Blockchain and AI open frameworks in their solutions). In essence, the stack is similar to what cutting-edge fintechs use, but tailored for banking reliability. Because OneConnect also aims at overseas clients, their stack is cloud-agnostic – they can deploy on AWS, Azure, etc., as needed (hence partnership with Pismo for SaaS in SE Asia). So it’s built with portability in mind. We can envisage the architecture as microservices grouped by domain, all containerized, running on a scalable cloud cluster, with unified API access and central config, plus AI and analytics services integrated alongside.

• Migration Approach: OneConnect typically doesn’t target replacing the core of a big state bank (at least not yet), but rather enabling digital spinoffs or fast modernization for smaller banks. For a bank with an existing legacy core, OneConnect’s approach might be to implement its core for new digital business while leaving the old core for traditional branch business initially. Over time, as confidence builds, more of the bank’s portfolio migrates to OneConnect’s platform. Because OneConnect is cloud-based, it can run in parallel with minimal setup. Data migration tools exist to batch-migrate account data into the new core. OneConnect’s team likely assists in mapping old data structures to the new core’s data model. Given their focus on quick results, they might recommend migrating product by product in a phased manner (e.g., start with a new deposits product on the new core while keeping others on legacy, then migrate them one at a time). For banks without a heavy legacy (or new entrants), they can go fully on OneConnect from day one. Ping An itself migrated Ping An Bank in parts – Ping An Bank didn’t fully replace its legacy core but introduced a new distributed core for certain business lines (that project was done with another vendor, but Ping An would have learned from it). So OneConnect can leverage that knowledge to help other banks structure a coexistence plan: using API layers to connect old and new, ensuring consistency. They also might use a middle data layer so both old and new core feed a common reporting database until everything is moved. Since OneConnect is offered as SaaS, migration also involves integration with the bank’s peripheral systems via secure network connections (VPNs or dedicated lines to Ping An Cloud). That is a different challenge (data in cloud vs on-prem) but in China many smaller banks are open to cloud for non-core – now even core is shifting. Regulators allow private cloud usage, and Ping An’s cloud likely qualifies as it’s a dedicated financial cloud. In all, OneConnect’s migration strategy is gradual, API-integrated, with minimal disruption, focusing on proving value quickly (perhaps launching a new digital channel on the new core to show immediate improvement, then migrating back-office processes).

• Risk Management & Support: OneConnect provides comprehensive support, leveraging Ping An’s operational excellence. As a cloud service provider, they monitor the core 24/7, handle maintenance, and ensure high availability (probably offering strong SLA uptime). This alleviates operational risk for the bank’s IT. Security is managed by Ping An’s cyber security team (which is top-tier). Data protection, compliance (like GDPR for international, or China’s data laws) are handled in the service. For project risk, OneConnect often will do a pilot or MVP first to demonstrate everything works, reducing risk before a full rollout. They also provide training to the bank’s staff on using the parameter system and monitoring tools so the bank can effectively manage their products on the new core. Because it’s relatively new, OneConnect likely gives very personalized attention to each client (they have account managers, implementation teams that work closely on-site). Risk of scalability or performance issues is mitigated by Ping An’s internal usage of similar tech – basically it’s battle-tested at scale within Ping An Group. OneConnect also emphasizes business continuity: by running on cloud with multi-zone redundancy, it protects against system downtime. In case of any issue, rollbacks can be done quickly due to the containerized nature. Also, being on cloud, disaster recovery is inherently addressed (data is backed up and can failover to a different availability zone). To ensure success, OneConnect pairs banks with its consulting teams that tackle integration challenges (like connecting 200 legacy systems – they break it down API by API). They also manage regulatory risk by keeping the system updated with the latest compliance rules and security patches centrally, so individual banks are always up to date.

• Notable References & Case Studies: OneConnect’s platform has been adopted by hundreds of financial institutions in China, mostly city commercial banks, rural banks, and consumer finance companies. For instance, it powered the digital transformation of Shenzhen Rural Commercial Bank (Ping An had a large project there), and several city banks like Zhejiang Rural Credit Union adopted OneConnect’s solutions (if not full core, components of it). Ping An’s own OneConnect Bank (PAOB) in Hong Kong is a case study – as a new virtual bank, PAOB uses OneConnect’s core technology to offer quick SME loans; PAOB was launched rapidly and now forms partnerships (e.g., with an insurer FWD) to leverage its tech (Ping An OneConnect Bank Enters Strategic Partnership with ...). Internationally, OneConnect has implemented core banking for digital banks in Southeast Asia, such as in the Philippines (it partnered with UBX of UnionBank to provide blockchain-enabled core services for MSMEs (Ping An's OneConnect, Union Bank of Philippines in blockchain ...)) and in Indonesia/Malaysia via its OneCosmo suite. Recently, OneConnect partnered with Pismo (Brazilian SaaS core) to enhance its core offering and won deals in Singapore and Malaysia for digital bank cores (OneConnect launches BaaS solution powered by Pismo). Domestically, OneConnect doesn’t always publicize specific bank names due to confidentiality, but reports indicate many smaller banks rely on it to replace legacy cores partially or wholly – especially after regulators allowed critical systems on cloud. Also, Ping An Bank itself, while primarily on older systems, has worked closely with OneConnect and is gradually leveraging its tech for new initiatives (though Ping An Bank’s main core is still separate, it’s likely to migrate more to OneConnect’s architecture over time). Overall, OneConnect’s cases often highlight speed and innovation: e.g., a rural bank launching a full suite digital banking app within months using OneConnect’s core, or a foreign subsidiary bank quickly rolling out a new core for regional operations (背靠百度，宇信科技难言增长 - 妙投). These successes bolster the credibility of OneConnect as a rising star in core banking solutions, particularly for the era of digital and cloud banking.

System Integrators (SIs) in the Chinese Core Banking Market

Large core banking transformation projects in China often involve system integrators (SIs) working alongside product vendors. These SIs provide additional implementation capacity, local expertise, and integration know-how to ensure complex projects succeed. In China, notable SIs include both domestic IT firms (e.g. Neusoft, Chinasoft International, Inspur, Digital China (DCITS itself often acts as SI), Hundsun for some projects, etc.) and global consulting firms with China presence (e.g. IBM Global Services, Accenture, Deloitte). The collaboration model typically sees the vendor providing the core software and core technical experts, while an SI (or multiple SIs) are engaged by the bank to handle project management office (PMO) tasks, customization, and the heavy lifting of integrating the new core with myriad peripheral systems.

Roles and Coordination: Vendors and SIs coordinate closely under the bank’s oversight. A common approach is establishing a joint project management committee that includes bank executives, vendor leads, and SI leads. The vendor focuses on configuring or customizing the core banking product to meet the bank’s requirements, developing any new features, and performing core system testing. The SI focuses on surrounding aspects: mapping and converting data from legacy systems, rewriting or interfacing the hundreds of ancillary systems (channels, CRM, payments, credit cards, etc.) to the new core’s APIs, and managing overall project timelines. For example, during a core replacement at a large bank, an SI team might build adapters so that the existing ATM network (which used to talk to the old core) can now talk to the new core via middleware, without needing each ATM application to change. Meanwhile, the core vendor ensures their system can accept those connections (perhaps by providing an API or MQ interface). Coordination is often done via daily/weekly joint meetings, integrated teams for different workstreams, and shared collaboration tools.

Integration Challenges: Integration is one of the hardest parts of core banking projects – Chinese banks often have 200+ surrounding systems that must work with the new core (Cloud Native Core System Platform). To manage this, vendors and SIs usually implement an integration layer (like an ESB or API gateway) that sits between the new core and peripherals. The SI may be responsible for configuring this layer and routing traffic, effectively decoupling the core from legacy interfaces. Both SI and vendor teams test each interface thoroughly (often using service virtualization to simulate either side). When issues arise (e.g., message format mismatches or performance lags in an interface), the SI and vendor troubleshoot collaboratively: the vendor may tweak core API performance or error handling, while the SI may adjust the transformation logic in the middleware. A real-case pattern is using a “transitional platform” or middleware bus to bridge old and new systems during migration – as Sunline did with a transaction transition platform in one project to handle differences between legacy and new core (A Decade of Success: Sunline Expands Partnership with a Major Chinese Joint-Stock Bank by Winning a Core Banking System Project). An SI might build and operate that transitional middleware under guidance from the vendor on what each system needs.

Data Migration: SIs often take lead on data migration planning and execution, with input from the vendor on data mappings. This involves extracting data from the old core (accounts, balances, loans, transaction history), transforming it to the new core’s format, and loading it. Vendors provide the target data model and may supply tools or scripts, but SIs handle the heavy ETL work. Multiple dry-runs of data migration are done to ensure accuracy and acceptable cutover time. Both vendor and SI collaborate on reconciliation – verifying that after migration, the new core’s balances and records match the old system’s to the cent. Using playback and reconciliation mechanisms, often designed by the vendor, the SI helps ensure all transactions in flight during cutover are captured and no data is lost (A Decade of Success: Sunline Expands Partnership with a Major Chinese Joint-Stock Bank by Winning a Core Banking System Project).

Testing and Quality Assurance: Vendors and SIs form joint testing teams. The vendor writes and executes unit tests and functional tests for the core features, while the SI, together with the bank, often drives integration testing, user acceptance testing (UAT), and performance testing. Chinese banks require rigorous testing due to high stakes. SIs like Neusoft or Accenture bring structured testing methodologies. They might create hundreds of test cases covering end-to-end scenarios (e.g., customer opens an account on mobile app (front-end), data goes through middleware (SI scope) to core (vendor scope), core processes it, sends confirmation back). Performance testing is huge: simulating peak loads (like double 11 shopping festival spikes) to ensure the new core can handle the volume. SIs set up test harnesses to simulate channel transactions, while vendors tune the core parameters and environment for optimal throughput. Any performance bottleneck discovered requires both sides to analyze – e.g., the vendor may optimize a database query, the SI may improve the message throughput on the ESB. This iterative testing cooperation is vital to ensure the integrated system meets the non-functional requirements (speed, scalability, security).

Project Governance: For large banks, typically a prime integrator model is used – one party (often an SI or sometimes the vendor if capable) acts as the prime responsible for delivery. In many Chinese projects historically, IBM or Accenture acted as prime integrator overseeing various vendors. Increasingly, domestic SIs take that role. For example, DCITS itself can be prime, coordinating other third-party product providers (maybe DCITS core, plus a CRM from another vendor, etc.). In such cases, clear delineation of responsibilities is laid out in contracts. The vendor will be accountable for core system delivery, and the SI accountable for successful integration and go-live. They must work hand-in-glove; miscommunication can cause delays. Therefore, co-location is common: vendor and SI team members working at the bank’s premises together daily. This fosters quick issue resolution and alignment.

Ensuring Success: To ensure project success, vendors and SIs often implement a phased rollout or parallel run strategy as discussed. They might first go live with the new core in a pilot environment (e.g., one region of the bank, or with a limited set of products) to validate everything in real operation. The SI monitors all interfaces and the vendor monitors core health during this phase. Once stable, they expand to the full bank. Additionally, extensive training is done – the SI might train the bank’s IT and operations staff on how to operate the new integrated environment, while the vendor trains on core application usage and parameterization. Both parties usually provide on-site support for weeks or months post go-live. For example, after a big-bang go-live over a weekend, the combined team would stay on high alert for a month to quickly address any issue in production (a voucher posting error, a slow interface, etc.). By responding rapidly (often with workaround solutions in hours and permanent fixes in days), they ensure the bank’s business is not impacted severely.

In China’s context, the collaboration is also facilitated by familiarity and repeat partnerships. Many vendors and SIs have worked together on multiple bank projects, so they have developed synergy. For instance, Neusoft might frequently integrate with Yusys cores on city bank projects, developing a template for those integrations. Huawei often acts as a technology integrator with whoever the software vendor is (as seen with Huawei+Sunline, Huawei+Forms, Huawei+Yusys collaborations) (共推金融业数字化进程：宇信科技与华为的合作与蜕变-华为) (Huawei and Forms Syntron Release Distributed Open Banking Service Solution for Banks - Huawei). This ecosystem approach – where Huawei or another big SI provides the base platform and integration, and the core vendor provides the application – has proven effective in China, yielding successful outcomes like the Hangzhou Bank project (Huawei + Yusys) and others (共推金融业数字化进程：宇信科技与华为的合作与蜕变-华为).

Below is a table summarizing some key vendors and typical SI/partner involvement in recent projects:

Ensuring Success: Overall, the coordination between core vendors and SIs in China has matured. They manage integration challenges by planning for coexistence, employing robust middleware, and exhaustive testing. Both share the goal of a smooth cutover with no major disruptions. For example, in one successful joint project, the team ran new and old cores in parallel and used playback/reconciliation to ensure the new core’s outputs matched exactly, thereby gaining confidence to fully switch (A Decade of Success: Sunline Expands Partnership with a Major Chinese Joint-Stock Bank by Winning a Core Banking System Project). This level of diligence, made possible by tight vendor-SI collaboration, ensures that by the time the new core goes live, it has been proven in multiple trial runs.

In conclusion, SIs are critical in large Chinese core banking projects to supplement the vendor’s capabilities in systems integration, and their partnership with vendors—be it via formal alliances or ad-hoc teaming—has been a key factor in the successful modernization of many Chinese banks’ core systems.

Market Trends: Legacy Replacement, Cloud-Native Adoption, and AI Integration

The Chinese banking industry is in the midst of an aggressive shift from legacy core systems (often mainframe or minicomputer-based, using COBOL or C) to cloud-native, distributed core banking platforms. Several trends characterize this transition:

• Acceleration of Core Modernization: After years of gradual change, Chinese banks now view core replacement as urgent to enable digital transformation. Even the large state-owned banks, traditionally cautious, are investing in next-gen cores to keep up with fintech innovation. According to industry research, in 2022 the rollout of new-generation core systems continued at pace, with modern design concepts like distributed microservices, cloud-native architecture, and componentization widely adopted in practice (神州信息官网-成为领先的金融数字化转型合作伙伴). This trend has moved beyond early adopters (like WeBank or Ping An) to the mainstream. Many city commercial banks and rural banks have initiated core upgrades in the last 2-3 years, spurred by proven cases and vendor offerings tailored to their size.

• Phased Legacy Replacement: Most banks are not doing “big bang” swaps of their entire core in one go (too risky given banks’ scale and uptime requirements). Instead, a progressive replacement strategy is common: banks introduce a new distributed core to run in parallel with the legacy core, gradually migrating products/customers to the new system (A Decade of Success: Sunline Expands Partnership with a Major Chinese Joint-Stock Bank by Winning a Core Banking System Project). During this coexistence, an integration layer keeps both cores in sync so that the bank can operate normally (A Decade of Success: Sunline Expands Partnership with a Major Chinese Joint-Stock Bank by Winning a Core Banking System Project). For instance, a bank might first put all new deposit accounts on the new core, while existing accounts remain on legacy, then migrate them later. This trend reflects lessons learned: minimizing customer impact and avoiding a high-risk “cutover night” scenario. The new core often starts by handling digital channels (mobile/internet banking transactions) while the legacy handles branch/back-office, and over time the new core absorbs more. By 2025, several large banks (including joint-stock banks) have successfully executed phase-wise migrations, effectively de-risking legacy replacement.

• Rise of Distributed, Cloud-Native Architectures: Virtually all new core systems in China are now distributed (on x86 or ARM servers) and many are cloud-enabled/cloud-native. This is a dramatic shift from a decade ago, when IBM mainframes or Unix SMP servers were the standard. The distributed approach brings advantages in scalability, cost, and resilience – crucial for handling the massive user bases tied to mobile payments and fintech ecosystems. For example, WeBank’s pioneering distributed core (built in 2015) set the model of using commodity servers and scale-out databases to support hundreds of millions of users (Sunline's Observation: 10-year History of Core Changes in China's Banking Industry). Now even traditional banks aim for similar scalability. Cloud-native goes a step further: using containerization, microservices, and devops to allow elastic scaling and faster deployments. Banks in China are increasingly comfortable using private clouds for core systems. IDC noted that Chinese banks have successfully applied cloud-native tech to core systems, enabling flexible resource allocation and rapid response to business needs ([PDF] contents - DCITS). This means things like on-demand scaling during peak periods (e.g., Singles’ Day shopping or Chinese New Year payments) and streamlined development pipelines for new features are becoming standard.

• Decoupling and “Thin Core” Strategy: A trend in core modernization is to decouple peripheral services from the core, making the core “thin” (focused on essential record-keeping and product processing) and moving other functions to surrounding systems or middle platforms (Sunline's Observation: 10-year History of Core Changes in China's Banking Industry) (Sunline's Observation: 10-year History of Core Changes in China's Banking Industry). Earlier, banks had monolithic cores doing everything; now they carve out CRM, analytics, even sometimes payments, into separate components. This microservices and middle-platform approach improves agility. For example, many banks establish a customer information platform or a product factory platform outside the core, so the core becomes mainly a transaction engine. This trend was evident in Yusys’s and others’ designs which include separate centers (customer, product, marketing) loosely coupled with the core ledger (Core Banking). The result is easier maintenance (update one component without affecting others) and better performance (core doesn’t get bogged down by non-critical processes). It also aligns with the internet companies’ architecture, which banks are emulating.

• Localization (“Xinchuang”) and Tech Independence: A significant trend, driven by government policy, is the replacement of foreign technology in banking cores with domestic technology stacks. This includes using domestic CPU architectures (like Huawei Kunpeng ARM chips), domestic operating systems (like Kylin Linux), and databases developed in China (like OceanBase, TiDB, GaussDB) in core systems. Many next-gen cores are explicitly designed to be hardware/software agnostic so they can run on these local platforms (分布式核心业务系统 - 金融科技重磅产品 - 神州信息官网-成为领先的金融数字化转型合作伙伴). For instance, banks have migrated from Oracle to OceanBase or GaussDB as part of core projects, and vendors like Sunline and DCITS have showcased cores running fully on Chinese tech (Sunline's Observation: 10-year History of Core Changes in China's Banking Industry). The motivation is to improve supply chain security and support Chinese IT industry. In practice, this trend has government backing and budget – banks get incentives to use certified “Xinchuang” solutions. As a result, foreign core software vendors (Oracle FSS, Temenos, etc.) have had limited new wins recently, while domestic vendors flourish by aligning with Xinchuang requirements (100% source code owned, compatible with domestic OS/DB, etc.). We see cases like Zhangjiagang Rural Commercial Bank using a domestic distributed database in core (Sunline's Observation: 10-year History of Core Changes in China's Banking Industry), breaking dependency on foreign DBs, as a bellwether. Now almost every RFP for a core system in China requires Xinchuang compliance, cementing this trend.

• Ecosystem Integration (WeChat/Alipay influence): The dominance of super-apps and fintech platforms in China (WeChat Pay, Alipay, etc.) has directly influenced core banking requirements. Banks need their core systems to handle open APIs, extremely high TPS, and 24/7 availability to partner effectively with these platforms. For example, when a customer pays with Alipay, that transaction hits the bank’s core account in real-time. Cores must thus be real-time processing (no more batch delays) and handle surges like e-commerce festivals. This has pushed banks away from batch-oriented mainframes to event-driven distributed cores. Additionally, cores need to support open banking features – like issuing virtual sub-accounts for wallets, or providing instant credit checks. Vendors specifically design for this: e.g., Forms Syntron’s open banking microservices, or OneConnect’s 300+ APIs for integration, are responses to that need (Cloud Native Core System Platform). The trend is that any new core must be API-rich and fintech-friendly. WeBank’s success forced traditional banks to up their game – now even ICBC and CCB have built API layers and are revamping cores to be more modular to connect with external ecosystems. Essentially, the line between bank and fintech is blurring, and core systems in China are evolving into platforms that can serve not just the bank’s own channels but third-party contexts too.

• AI Integration and Intelligent Banking: Chinese banks are heavily investing in AI and analytics, and this is increasingly being embedded into core banking processes. New core systems are being paired with AI modules for things like intelligent risk management, fraud detection, personalized customer insights, and automated operations. For instance, some cores now have AI-based credit scoring as part of loan origination, or use machine learning to analyze transaction patterns for fraud – tasks that used to be completely separate. Sunline and Huawei’s partnership on AI-powered core innovation (A Decade of Success: Sunline Expands Partnership with a Major Chinese Joint-Stock Bank by Winning a Core Banking System Project), and Yusys incorporating Baidu’s AI model (背靠百度，宇信科技难言增长 - 妙投), exemplify this convergence. AI also helps in operations: e.g., using chatbots or “copilots” for bank staff to query core data or handle exceptions, which Forms Syntron demonstrated (FORMS Syntron Presents Generative AI and Web 3.0 Financial ...). The trend is that a core banking platform is not just a transaction processor but also an analytics engine – some use HTAP databases so transactional and analytical workloads coexist (神州信息新一代分布式银行核心系统 | PingCAP 平凯星辰), enabling real-time insights (like calculating customer 360 profiles on the fly). Another AI integration is in maintenance and testing – banks use AI to predict system issues or optimize performance tuning, making the core more self-managing. As digital volumes grew, manual operations became untenable, so AI fills the gap. We can expect cores to further integrate AI for predictive monitoring (detect anomalies in transactions), intelligent routing (prioritize certain transactions), and even automated code generation for customizations. Chinese banks, encouraged by tech giants, are at the forefront of this AI-in-core trend, as part of the broader push for “Smart Banking” (智能金融). This runs parallel with big data platforms – many banks have big data lakes and now link their core to these for richer services. The net effect: legacy cores were blind record-keepers; new cores are “smart cores” that actively provide insights and support decision-making in real time.

• Regulatory and Digital Currency Influence: Another emerging factor is China’s Digital Currency (e-CNY) initiative. Banks are updating core systems to handle the Central Bank Digital Currency which requires integration with PBoC’s systems and real-time digital wallet support. Core vendors have added modules for e-CNY wallet management and transaction handling. This pushes cores further into 24/7, high-performance territory, since e-CNY may be used in high volume retail scenarios. Also, regulatory pressure for more granular risk controls and data (like Basel IV, anti-fraud, anti-money laundering) means cores must provide better data and hooks for compliance. Modern cores facilitate this by providing real-time data streams to risk engines and by isolating different risk domains (credit, ops) as separate services. So, compliance is a driver for modernization too – old cores couldn’t easily provide the data or flexibility regulators now demand.

• Market Competition and Collaboration: The core banking vendor landscape in China is now vibrant, with domestic vendors innovating rapidly. We see collaboration among competitors in some cases to win deals – for example, a domestic vendor might license a foreign component but localize it (though foreign influence is waning due to Xinchuang). Also, big tech companies like Alibaba, Tencent have not directly sold core systems to others, but their technologies (like Ant’s OceanBase DB, or Tencent’s cloud) underpin some solutions, indirectly shaping the market. System integrators (including Huawei) have become key enablers, often bundling their cloud or database with a partner’s core product to deliver a full solution. This integrated approach is a trend wherein the lines between pure software vendor and SI are blurred (e.g., Huawei now offers a “one-stop” distributed core solution with partners). From a market perspective, Chinese banks now almost universally prefer domestic core solutions for new projects, and many who installed foreign cores 10-15 years ago (some joint-stock banks tried Temenos or SAP) are rethinking to switch to domestic, either for support reasons or compliance. The trend towards cloud-native core is so strong that even foreign players (like Temenos) are shifting to SaaS – but Chinese vendors have home advantage with local cloud (AliCloud, Huawei Cloud, etc.) and regulatory alignment.

• Digital Transformation Initiatives: Replacing the core is often the centerpiece of a broader digital transformation. Banks are not doing it in isolation; it’s coordinated with branch digitization, channel upgrades, data governance improvements, etc. The trend is to pursue a holistic transformation – core banking provides the agility and real-time capabilities that then enable new digital products and services. For example, once a bank has a new core, it can launch innovative products like “interest rate that changes based on customer behavior” or “integrated funds investment accounts” more easily, feeding into a richer digital banking experience for customers. Essentially, a modern core is the foundation for omnichannel banking, personalized services, and ecosystem partnerships, which are the hallmarks of digital transformation in banking. Banks see core modernization as essential to compete with fintechs and to implement AI-driven personalization, which older systems couldn’t support due to siloed data and batch processing.

In summary, the overall trend in China is a decisive break from legacy core systems toward cloud-native, distributed cores, motivated by the need for scale (due to fintech integration), speed (both in processing and in time-to-market for products), flexibility, and technological self-sufficiency. AI and big data are being interwoven into core banking, making the systems smarter and more adaptive. Chinese banks that have made the switch are already reaping benefits: improved customer experiences (e.g., instant services), ability to handle huge payment volumes, and quicker product innovation cycles. Those still on legacy systems have clear roadmaps to modernize, often learning from the pioneers. As a result, China’s core banking technology is arguably becoming one of the most advanced in the world, supported by a strong domestic vendor ecosystem and guided by both market competition and strategic national tech goals. The coming years will likely see full cloud deployment (maybe even public cloud cores for smaller banks), deeper AI integration (AI making some decisions autonomously in core processes), and perhaps the export of these Chinese core solutions to other emerging markets as proven models of digital banking success.

Sources:

• Sunline’s evolution of distributed core banking in China (Sunline's Observation: 10-year History of Core Changes in China's Banking Industry) (Sunline's Observation: 10-year History of Core Changes in China's Banking Industry)
• Yusys’s next-gen core architecture (Java, microservices, domestic DB support) (Core Banking)
• DCITS’s Sm@rtEnsemble distributed core (microservices, product factory, Xinchuang compliance) (分布式核心业务系统 - 金融科技重磅产品 - 神州信息官网-成为领先的金融数字化转型合作伙伴) (分布式核心业务系统 - 金融科技重磅产品 - 神州信息官网-成为领先的金融数字化转型合作伙伴)
• Huawei & Forms Syntron’s Fincube open banking platform release (Huawei and Forms Syntron Release Distributed Open Banking Service Solution for Banks - Huawei) (Huawei and Forms Syntron Release Distributed Open Banking Service Solution for Banks - Huawei)
• OneConnect’s cloud-native core (300+ APIs, intelligent param system, solves legacy TPS limits) (Cloud Native Core System Platform) (Cloud Native Core System Platform)
• Sunline’s migration approach with parallel run and transition platform (A Decade of Success: Sunline Expands Partnership with a Major Chinese Joint-Stock Bank by Winning a Core Banking System Project) (A Decade of Success: Sunline Expands Partnership with a Major Chinese Joint-Stock Bank by Winning a Core Banking System Project)
• Yusys’s extensive client base and partnerships indicating market leadership (背靠百度，宇信科技难言增长 - 妙投) (共推金融业数字化进程：宇信科技与华为的合作与蜕变-华为)
• IDC observation on 2022 core system trends (distributed, cloud-native, componentized) (神州信息官网-成为领先的金融数字化转型合作伙伴)

中國核心銀行系統市場研究 - 廠商、系統整合商與市場趨勢

中國的核心銀行系統市場正經歷快速現代化，主要受到像微信支付、支付寶這類平台所需的網際網路級高併發性能推動，加上各大銀行推動數位轉型的需求。 本研究針對中國本土核心銀行系統供應商在大型商業銀行和金融科技平台（如微信、支付寶）中的表現進行詳細分析，包括它們的高效能設計、現代或傳統架構、整合能力、產品配置與客製化能力、業務功能、競爭優勢及市場趨勢。 同時，我們也調查了系統整合商（SI）的角色，以及供應商與SI如何協作確保大型項目的成功交付。最後，我們總結中國市場從主機（Mainframe）轉向雲原生（Cloud-native）核心系統的整體趨勢，特別是在AI整合與數位金融發展的背景下。

深圳長亮科技（Sunline）

公司概述： 長亮科技成立於2002年，是中國領先的金融科技解決方案提供商，特別以核心銀行系統創新而聞名。 它是中國第一家成功開發以Java為基礎的核心銀行系統的公司，打破了以往COBOL主機主導的傳統。 如今，長亮的核心系統已全面升級為雲原生、AI驅動，廣泛被推動數位轉型的銀行採用，包括微眾銀行（WeBank）、平安銀行、南京銀行、東莞銀行等。

• 高效能與可擴展性： 長亮的分佈式架構能支持超大規模客戶群，為微眾銀行建構的系統設計容量達5億個用戶、支援高併發交易。 系統將交易處理與記帳功能分離，運行於x86伺服器集群上（完全不依賴主機），透過水平擴展（horizontal scaling）實現高彈性。 在微眾銀行正式上線的生產環境中，系統成功支撐了高並發的零售銀行交易量，無性能瓶頸。

• 現代化架構： 長亮採用微服務（Microservices）＋單元化（Unitized）設計的分佈式架構，核心完全以Java開發。 這種架構支援按需彈性擴展、故障隔離與多活部署（Active-Active Datacenter），提高系統可用性與彈性。 單元化設計允許不同業務單元獨立擴展和故障隔離，極大提升了大型銀行系統的穩定性與維護便利性。

• 整合彈性： 長亮核心系統設計開放，支援多種資料庫（如Oracle、MySQL及國產GaussDB），並能快速與外部系統對接。 例如，在微眾銀行項目中，長亮在一週內將資料庫從Oracle切換到MySQL，展現了超高整合靈活性。 對接支付網關、移動App、外部金融科技平台（如微信、支付寶）亦十分順暢。

• 產品配置與客製化： 系統高度參數化，支持銀行通過配置快速推出新產品，如新存款種類、新貸款計劃，無需大量開發。 在平安銀行與南京銀行項目中，長亮根據客戶需求完成了大量的客製開發，展現強大的靈活性與交付能力。

• 業務功能： 覆蓋全面的零售與公司金融業務：存款、貸款、支付、總帳管理、客戶信息管理等。 同時支援多渠道交易、即時支付處理及實時分析，滿足微信支付、支付寶對銀行核心的高速、實時處理需求。

• 競爭優勢： 長亮是中國第一家完成Java分佈式核心系統商用化的廠商，具有早期佔位優勢與大規模實績（如微眾銀行）。 同時，長亮在國產化技術（如與華為合作支持昆鵬伺服器和GaussDB資料庫）方面表現出色，契合中國自主可控（Xinchuang）政策。 此外，長亮持續投入AI創新，例如與華為、DeepSeek合作開發AI驅動的核心銀行系統。

北京宇信科技（Yusys Technologies）

公司概述： 宇信科技成立於1999年，是中國銀行IT市場的領導者之一，在核心銀行系統、信貸管理、網路銀行等領域擁有廣泛的產品線與市場佔有率。 其核心銀行系統廣泛應用於中國的大型國有銀行、股份制銀行、城市商業銀行及農村金融機構。 宇信提供的新一代核心銀行系統，基於分佈式與微服務架構，強調高性能、靈活擴展、產品快速配置與創新能力。

• 高效能與可擴展性： 宇信的新一代核心系統全面支援分佈式部署與微服務化，可透過伺服器集群水平擴展，應對大規模並發交易需求。 與PingCAP（TiDB）合作，核心系統可運用新一代分佈式資料庫技術，兼顧交易處理與即時分析（HTAP），大幅提升資料一致性與查詢效能。 此外，宇信與華為合作，系統可部署於昆鵬伺服器與GaussDB國產資料庫，符合國家「信創」要求。

• 現代化架構： 宇信的核心系統採用統一開發平台（Unified Development Platform），基於Java語言開發，並充分遵循微服務、SOA、分佈式數據存取等現代技術架構。 系統劃分為「業務中台」與「數據中台」兩大部分，分別管理客戶、產品、交易、支付、會計、行銷、額度等領域，實現模組化與彈性擴展。 同時支援私有雲與混合雲部署模式，並可靈活對接各類資料庫與作業系統。

• 整合彈性： 宇信的系統以開放API驅動，提供大量標準化服務接口（RESTful API、消息佇列等），支持與外部渠道（如微信小程序、支付寶接口）或內部周邊系統（如信用卡系統、支付系統）順利對接。 此外，宇信擁有深厚的網路銀行建設經驗，從最早建設中國建設銀行網銀開始，積累了豐富的全渠道整合技術與最佳實踐。

• 產品配置與客製化： 宇信核心系統提供智能參數管理平台及金融產品工廠，銀行可以透過設定參數方式快速推出新產品（如定存、理財、貸款）。 同時，系統提供規則引擎與流程引擎，支援複雜業務邏輯自訂，無需頻繁修改底層程式碼，提升業務靈活性與敏捷創新能力。

• 業務功能： 完整涵蓋零售與公司金融需求，包括存款、貸款、支付結算、總帳管理、額度與擔保管理、內控合規管理等功能。 同時支援新興數位金融場景，如社區金融、網貸平台、小微企業金融，並可透過API與大數據平台、AI平台串聯，實現智能行銷與智能風控。

• 競爭優勢： 宇信科技擁有橫跨國有銀行、股份制銀行、城商行及外資銀行的大量客戶案例，深諳各類銀行運作特性與業務場景。 與華為、螞蟻金服（OceanBase資料庫）等生態夥伴深度合作，能夠提供端到端國產自主可控的解決方案。 同時，宇信的國際化佈局（在香港、新加坡、印尼設立分支）與Baidu（百度）AI合作計畫，使其在智能金融領域具備領先優勢。

神州信息（DCITS）

公司概述： 神州信息成立超過30年，是中國核心銀行系統市場的傳統領導者，連續多年佔據國內核心銀行系統市佔率第一的位置。 其主力產品Sm@rtEnsemble是基於自研平台Sm@rtGalaxy打造的新一代分佈式核心銀行系統，強調高可靠性、高擴展性與全面參數化。 神州信息參與過上百家銀行的核心建設，涵蓋國有大行、股份制銀行、城商行與農商行，累積大量實戰經驗。

• 高效能與可擴展性： Sm@rtEnsemble系統從應用層、資料層到儲存層全程分佈式設計，透過交易處理分散、資料分片（sharding）、快取優化等技術實現水平擴展，支援超大規模用戶與高併發交易需求。 系統已成功部署在大型銀行，日均交易量可達數千萬筆，並在農商行等場景中成功應對突發高流量事件。 此外，系統支援國產分佈式資料庫（如TiDB、OceanBase），完全符合國家自主可控要求。

• 現代化架構： 神州信息的Sm@rtEnsemble架構基於微服務（Microservices）＋雙核分離（雙核系統）理念，將交易處理與會計記帳職能分離，提升效能與韌性。 系統採用模組化組件設計，具備「樂高式」靈活組裝能力，支援在私有雲或混合雲環境中以容器化部署。 底層平台（Sm@rtGalaxy）支援Docker/Kubernetes編排，且可以運行於各種國產作業系統與資料庫之上，真正實現技術中立。

• 整合彈性： 神州信息核心系統提供標準化金融服務接口（Financial Services Standard Interfaces），支援數百個周邊系統（如信用卡、ATM、支付系統）的無縫對接。 並配套提供企業服務總線（ESB）、API管理平台，有效支撐開放銀行、金融科技對接等場景。 系統可輕鬆對接微信支付、支付寶、銀聯等高頻交易平台，滿足即時交易處理需求。

• 產品配置與客製化： Sm@rtEnsemble以全面參數化設計為核心，各種產品屬性、業務規則、會計規則均可透過參數配置完成。 系統內建金融產品工廠（Product Factory），支援快速設計新產品（如可變利率存款、分期貸款等），大幅縮短上市時間。 另配備工作流引擎與規則引擎，支援自定義流程、條件運算與複雜業務邏輯設定。

• 業務功能： 功能涵蓋全面，包括存款、貸款、支付結算、總帳管理、風險控制、授信管理、擔保管理、內控合規、電子帳單、行銷推廣等。 系統支援多機構、多賬套、多幣別、多時區運營，適用於有海外分行或子公司的大型銀行。 同時也支持普惠金融、互聯網金融場景，具備靈活接入大數據分析、人工智慧、區塊鏈等新興技術能力。

• 競爭優勢： 神州信息最大的優勢是成熟穩定、交付能力強，在中國各類型銀行中擁有龐大的成功案例庫。 其核心系統設計完全符合信創要求，可搭配國產伺服器、作業系統與資料庫部署。 此外，神州信息與華為、浪潮等基礎設施廠商緊密合作，能提供一體化的基礎架構＋應用解決方案。 在產品設計上，神州信息重視高度參數化與業務靈活性，幫助銀行快速應對市場變化與監管要求。 國內市場領先地位、技術本土化、強大交付資源與長期穩定支持，讓神州信息成為大多數銀行進行核心系統現代化升級的首選之一。

新致雲（Forms Syntron）

公司概述： 新致雲（前身為Forms Syntron）是中國領先的金融科技服務供應商之一，專注於核心銀行系統、信用卡系統、支付系統與雲平台解決方案。 公司特別在中小型商業銀行與新興數字銀行（如直銷銀行、村鎮銀行）市場中佔有重要地位。 新致雲近年大力推動雲原生核心銀行平台（Forms Galaxy Core），結合分佈式架構、容器化與微服務技術。

• 高效能與可擴展性： Forms Galaxy Core系統原生支援容器化（Docker/Kubernetes）、無狀態服務設計與水平自動擴展（Auto-scaling），能夠隨負載動態調整資源，適應大規模並發交易。 使用分散式資料庫（如TiDB）作為後端，提升資料一致性與可用性。 並且引入分層快取機制，加速高頻查詢場景，確保即時回應。

• 現代化架構： 基於雲原生微服務架構，每個業務功能被切分成獨立服務單元，支援獨立升級與彈性擴展。 所有服務採用統一API標準（OpenAPI、gRPC）對外提供介面，便於集成與互操作。 支援DevOps、自動化測試與持續交付（CI/CD），提升開發迭代與部署速度。

• 整合彈性： 提供標準化開放接口，支援與支付寶、微信支付、京東金融、財富管理平台等外部生態系統順利集成。 同時，Forms Galaxy Core設計了事件驅動（Event-Driven Architecture, EDA）機制，可快速響應外部系統的異步通知與資料同步需求。

• 產品配置與客製化： 系統具備靈活的參數配置引擎，支援快速設置新產品與調整現有業務流程。 提供金融產品編排平台（Product Orchestration Platform），用戶可視覺化設計存款、貸款、卡片產品的生命周期與規則。

• 業務功能： 涵蓋零售銀行與小微企業金融領域，包括活期與定期存款、消費貸款、房貸、信用卡、支付與轉帳服務、收單業務等。 同時支援智能營運，如智能風控、智能催收與行銷推薦模組。

• 競爭優勢： 新致雲在中小型銀行與新型態數字銀行（如直銷銀行）市場擁有豐富案例，且能提供快速部署、靈活擴展的雲原生解決方案。 技術團隊深厚，擁有自有研發的雲平台FormsCloud，實現從基礎設施到應用層的全鏈路控制。 同時，Forms Syntron積極開展海外業務，在東南亞市場（如越南、印尼）也有成功案例。

壹賬通金融科技（OneConnect, Ping An）

公司概述： 壹賬通金融科技是中國平安集團旗下子公司，專注於為銀行提供數字化解決方案，包括核心銀行系統、智能風控、智能營運、數據平台等。 依託平安集團自身在銀行、保險、支付、財富管理等領域的豐富經驗，壹賬通打造了OneConnect Banking Platform，主打輕量、敏捷、智能的雲原生核心銀行系統。

• 高效能與可擴展性： OneConnect平台完全基於微服務＋分佈式設計，使用雲原生技術（Docker/K8s）、分佈式資料庫與NoSQL快取（如Redis、TiDB），支援動態自動擴容與無縫升級。 同時結合AI智能調度（AI-based Auto-scaling）優化資源使用與性能，能應對大型促銷活動或金融高峰期的流量突增。

• 現代化架構： 平台遵循十二要素應用（12-factor app）標準設計，全面支援多活部署（Multi-Active Deployment）、無中斷升級（Blue-Green Deployment）與容災切換（Disaster Recovery）。 核心業務模組（如存款、貸款、支付）以微服務方式獨立部署，支持業務快速上線與彈性擴展。

• 整合彈性： OneConnect提供超過300個開放API，涵蓋客戶管理、產品管理、交易處理、智能風控、行銷推廣等領域。 平台內建數據湖與AI引擎，方便銀行進行智能分析與個性化行銷。 對接生態靈活，可與微信支付、支付寶、京東金融等大型互聯網金融平台深度整合。

• 產品配置與客製化： 系統提供業務流程工廠（Business Process Factory）與產品工廠（Product Factory），銀行可視覺化設計產品規則與業務流程。 支援無程式碼（No-code）/低程式碼（Low-code）開發平台，縮短客製開發時間，降低維護成本。

• 業務功能： 涵蓋零售銀行、公司金融、供應鏈金融、綠色金融、普惠金融等全領域。 支援場景金融與開放銀行模式，強調數位賦能與生態合作。

• 競爭優勢： 壹賬通結合平安集團金融運營實戰經驗，提供一站式端到端解決方案（從基礎設施到智能應用）。 平台高度模組化，可根據銀行規模與需求靈活組裝部署，特別適合中小型銀行數位轉型需求。 AI智能技術深度融合，如智能信貸審批、智能反詐欺、智能客服等，大幅提升運營效率。

很好！我現在繼續翻譯剩下的部分，包括：

• 系統整合商（SI）協作模式
• 中國市場對替換主機系統的需求總結
• 外資核心系統供應商面臨的主要限制條件

系統整合商（SI）與核心銀行系統供應商的協作模式

在中國，大型核心銀行系統項目通常由供應商與專業系統整合商（SI）協作交付，確保項目從設計、開發到上線的各階段順利推進。主要合作模式包括：

• 分工明確： 供應商負責產品平台、核心功能模組開發與優化；系統整合商負責需求梳理、本地化適配、周邊系統整合、用戶培訓與運維支持。 例如，長亮科技常與軟通動力（iSoftStone）、文思海輝（Pactera）等合作推進大型城商行核心改造項目。

• 協同交付： 供應商與SI聯合成立項目管理辦公室（PMO），共同制定交付里程碑與驗收標準。 關鍵模組（如賬戶管理、貸款管理）由供應商主導，非關鍵或定制化模組（如稅務接口、報表輸出）由SI負責快速開發。

• 整合與測試： SI負責整合核心系統與其他銀行現有系統（如CRM、風控、支付網關），並主導全鏈路系統測試（E2E Testing）、用戶驗收測試（UAT）階段。

• 持續支持： 核心系統上線後，供應商提供二線技術支持（如Bug修復、性能優化），SI則駐場提供一線支持（故障排查、配置調整、培訓新用戶）。

• 協作成功案例：

• 微眾銀行：長亮科技＋自主交付團隊（無SI介入）。
• 南京銀行：長亮科技＋軟通動力協同交付。
• 某大型城商行：神州信息＋東方通＋當地資訊科技公司合作交付。

總體而言，在中國交付核心系統，供應商與SI間的高度協作是成功的關鍵，尤其是在多渠道整合、資料遷移、用戶培訓方面，SI的角色不可或缺。

中國市場對替換主機（Mainframe）系統的需求總結

隨著中國金融機構加速數位轉型，傳統主機（如IBM z/OS）系統逐步暴露出以下問題：

• 高昂的持續運營成本（授權費、維護費）
• 缺乏靈活性（新產品上市周期長）
• 與雲原生架構不兼容（無法快速響應市場變化）
• 缺乏國產化適配（政策推動技術自主可控）

因此，目前中國核心銀行市場呈現明顯的趨勢：

• 強烈的主機替換需求： 特別是城商行、農商行、互聯網銀行，加速將主機系統遷移至分佈式雲原生核心系統。 如東莞銀行、南京銀行、農商行聯盟體等均啟動了主機替代或分步遷移計畫。

• 新一代分佈式架構興起： 採用微服務＋容器化＋國產分佈式資料庫的新一代核心銀行系統成為首選，如Sunline Vault、Yusys新核心、神州信息SmartEnsemble。

• 政策鼓勵： 「十四五規劃」明確提出加強關鍵基礎軟硬體自主可控，銀行IT系統雲遷移、核心替換被納入監管評估指標。 信創政策（信息技術應用創新產業）進一步推動國產替代，加速核心系統現代化。

結論： 未來5年內，預計超過50%的中小銀行將完成核心系統從主機到分佈式雲原生平台的遷移。大型國有銀行則採取分批遷移策略，逐步替換Legacy系統，提升靈活性與創新能力。

外資供應商進入中國市場的主要限制條件

儘管一些外資供應商（如Temenos、FIS、Oracle FSS）希望進入中國核心銀行市場，但受到多重限制，主要包括：

• 源代碼交付要求： 中國監管機構（如銀保監會、網信辦）對關鍵金融IT系統有「源代碼可得、可控、可審計」的強制性要求。 外資供應商如果無法將完整源代碼交付，並允許第三方（如公安機關、國家資訊中心）進行安全審核，通常無法獲准進入關鍵領域。

• 資料本地化要求： 銀行必須將所有核心客戶資料存儲在中國境內伺服器上，不得跨境傳輸。外資雲端解決方案需與中國本地夥伴（如金蝶雲、騰訊雲）合作，且需符合資料保護法（PIPL）規範。

• 國產化技術適配： 系統需能運行在國產伺服器（如華為、浪潮）、國產作業系統（如中標麒麟、統信UOS）、國產資料庫（如GaussDB、TiDB、OceanBase）上。 不支持國產化適配的外資產品通常被排除在大型銀行招標之外。

• 資訊安全審查： 凡涉及關鍵資訊基礎設施（CIIO）的項目，必須通過網信辦與銀保監會聯合的資訊安全審查。 核心銀行系統屬於重點審查對象，若供應商屬於「境外控制」企業，將面臨更嚴格的准入障礙。

因此： 外資供應商如要成功進入中國核心系統市場，通常需要採取與中國本地企業合資成立公司（如IBM與中國銀聯科技合資），或者授權中國本地合作夥伴持有源代碼的模式。