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Author: Zhang Feng
Today, as artificial intelligence technology sweeps the financial industry, the “Consultation Document on Artificial Intelligence Risk Management Guidelines” released by the Monetary Authority of Singapore (MAS) on November 17, 2025, is like a timely map, pointing out a safe course for financial institutions sailing in the wave of innovation.This document is not only the world’s first full life cycle risk management framework for AI applications in the financial field, but also represents a key shift in regulatory thinking from “principle advocacy” to “operational implementation.”For any company related to the Singapore market, in-depth understanding and systematic implementation of this “Guide” has changed from “optional” to “must-answer”.
1. Insight into the core of the “Guidelines”: seeking a delicate balance between innovation incentives and risk prevention
The birth of the “Guide” stems from a profound regulatory understanding: AI is a double-edged sword.While technologies such as generative AI and AI agents shine in scenarios such as credit, investment consulting, and risk control, they also bring unprecedented risks such as model “illusion”, data poisoning, supply chain dependence, and out-of-control autonomous decision-making.If these risks are left unchecked, they could trigger chain reactions far beyond traditional financial crises.
Therefore, the regulatory logic of MAS is not a “one-size-fits-all” suppression;“Risk-based”with“Principle of proportionality”the essence of.This means that the focus of supervision and the resources invested by enterprises should strictly match the risk level of the AI application itself.A high-stakes AI model used for loan approval will naturally require stricter governance than an AI tool used for internal document analysis.This differentiated approach recognizes the uniqueness of different institutions and different scenarios, and aims to build a“Innovation does not exceed the rules”A healthy ecosystem will ultimately consolidate Singapore’s leading position as a global financial technology hub.
2. Build three layers of defense: governance, risk system and full life cycle closed loop
The “Guide” has built a solid three-tier risk management structure for enterprises, which progresses layer by layer to form a closed loop.
The first level is governance and supervision, which aims to clarify “who is responsible”.The “Guide” clearly assigns the final supervision responsibility for AI risks to the board of directors and senior management, requiring them not only to approve AI strategies, but also to improve their own AI literacy to conduct effective supervision.For institutions with extensive AI applications and high risk exposure, set up a team across risk, compliance, technology, and business departments.“AI Committee”, becoming the core hub for reporting directly to the board of directors and a key recommendation to ensure the implementation of governance.
The second level is the risk management system, which addresses “what to manage” and “what to manage first.”Enterprises first need to establish a mechanism to comprehensively identify and register all AI applications, like taking inventory of tangible assets, whether they are self-developed, outsourced or based on open source tools, to form a dynamically updated“AI Checklist”.On this basis, every AI application needs to start from“Level of Impact”, “Technical Complexity” and “External Dependencies”The three dimensions undergo a “physical examination” and are assigned high, medium and low risk ratings.This risk heat map is the scientific basis for enterprises to allocate management and control resources.
The third level is full life cycle management and control, which stipulates “how to manage”.This is the most operational part of the Guidelines, which integrates regulatory requirements into every aspect of AI from inception to retirement.From ensuring the legality and fairness of the training data, to the interpretability verification in the model development stage; from the security testing against “illusion” and prompt word injection attacks before going online, to the manual supervision interface that must be retained during operation; and even the strict management of third-party suppliers and the specifications for model retirement,A management chain with no dead ends has been formed.
3. Distinctive characteristics: forward-looking, operability and differentiated regulatory wisdom
Throughout the entire text, the Guide shows several distinctive features that make it stand out among many regulatory texts.Its forward-lookingness is reflected in the fact that for the first time in the world, generative AI and AI agents have been explicitly included in the scope of supervision, facing the most cutting-edge technological risks.Its operability is far more than a principled initiative. It is like a detailed “operation manual” that deconstructs abstract principles such as fairness, ethics, accountability, and transparency (FEAT) into specific actions such as AI checklist elements and quantitative evaluation indicators.What is more noteworthy is its differentiated regulatory gradient design, which sets a simple to complex compliance path for small institutions, medium-sized institutions and large/high-risk institutions, reflecting a pragmatic spirit.
In addition, the “Guide” is not an island. It synergizes and complements Singapore’s existing “Artificial Intelligence Governance Model Framework”, “Personal Data Protection Act” (PDPA) and other regulations, and promotes the preparation of industry best practice manuals through projects such as Project MindForge, jointly building a“Hard supervision + soft guidance”three-dimensional ecological system.
4. Step-by-step implementation path: comprehensive embedding of domestic enterprises and precise compliance of cross-border enterprises
In the face of the Guidelines, different types of enterprises need to adopt completely different response strategies.
For inFinancial institutions operating in Singapore, the implementation work should be systematically promoted in three steps:
Before the consultation deadline on January 31, 2026, companies should complete the core “understanding” work-comprehensive inventory of AI assets and complete preliminary risk ratings, and actively participate in feedback.Entering the 12-month transition period starting in the second half of 2026, it will be a comprehensive construction period: improving the governance structure, establishing a full life cycle management process, strengthening the management of third-party suppliers, and conducting compliance training for all employees.By the second half of 2027 and the normalization stage thereafter, the focus will shift to dynamic optimization, internal auditing and industry collaboration to continue to revitalize the risk management system.
For those companies that have not established entities in Singapore but have extended their business reach to the country’s market (such as providing cross-border financial services and providing AI technology to Star City financial institutions), the core of the strategy is“Precise compliance” and “risk isolation”.First, it is necessary to clearly sort out which businesses and AI applications fall within the regulatory scope of the Guidelines.Subsequently, a special compliance process and files need to be established for this part of the “new business” to ensure that it can respond to inspections by partners or MAS at any time.Technically, it is recommended to properly isolate the AI system facing the Singapore market, and proactively and transparently communicate the compliance status with Singapore partners, so as to transform compliance capabilities into market trust and cooperation advantages.
5. Beyond compliance: transform risk management into core competitiveness
The key to implementing the Guidelines is to deeply embed its requirements into specific business scenarios and operational processes to achieve “seamless integration” of risk management and daily operations.
tocredit approvalTake this high-risk scenario as an example,Enterprises should set up multiple compliance control points in their business processes.inrequirements design stage, the business and technical teams need to jointly evaluate the potential bias of the model, and explicitly prohibit the use of sensitive characteristics such as race and gender as a basis for decision-making; inModel under development, introducing independent verification and fairness testing to ensure its interpretability;After going online, the system needs to force manual review of “high-risk” or “borderline” cases, and fully record the decision-making trajectory for audit traceability.At the same time, for the application of generative AI in intelligent customer service, it is necessary to build “hallucination” detection and real-time monitoring into the dialogue process to prevent misleading answers, and set up clear manual takeover nodes for operations involving transactions or sensitive information.
Enterprises should transform the “full life cycle management and control” in the “Guide” into each business department’sSOP(Standard Operating Procedure).For example, in the marketing recommendation business process, user authorization and data representativeness need to be ensured from the data collection stage; model iteration requires not only technical testing, but also joint review by the business and compliance departments based on the latest regulatory requirements; A/B test results in operations must include fairness impact assessment.By structurally embedding AI risk control points into business processes, companies can not only systematically meet compliance requirements, but also improve the quality and robustness of business decisions, truly turning regulatory frameworks into operational advantages.
The implementation of the Guidelines is by no means a simple cost center or compliance burden.The key to its success lies in whether the company can elevate it to a strategic level.Genuine attention from senior management and continued resource investment are the cornerstones. The board of directors must incorporate AI risks into the overall risk appetite of the institution for comprehensive consideration.In-depth collaboration between business departments and technical departments is the bloodline. AI risk management must not be a solo dance of the technical team, but must be a linked closed loop of business requirements, technology implementation, and compliance supervision.In addition, in today’s era of rapid iteration of technology and supervision, establishing a mechanism for dynamic adaptation and continuous optimization, and making good use of automated monitoring and evaluation tools to improve efficiency are the keys for enterprises to remain agile.
Eventually, leading companies will realize that robust, transparent, and credible AI risk management capabilities have become a powerful brand asset and competitive advantage in themselves.It can not only meet regulatory requirements, but also win the long-term trust of customers and the market, and build the most reliable moat for enterprises in the digital era full of uncertainty.As the final version takes effect in 2026, those companies that take the lead in completing the systematic layout will undoubtedly gain valuable first-mover advantages on the new financial technology track in Singapore and even the world.
