Singapore's financial services sector operates under one of the most rigorous regulatory frameworks in Asia. The Monetary Authority of Singapore (MAS) Technology Risk Management (TRM) Guidelines set clear expectations for how financial institutions adopt, deploy, and govern technology — including artificial intelligence. Any AI training programme for financial services teams in Singapore must be built around these requirements.
The MAS TRM Guidelines require financial institutions to establish robust governance over technology adoption. For AI specifically, this means documented risk assessments before deployment, clear accountability structures for AI-driven decisions, ongoing monitoring of model performance, and audit trails that regulators can review. Your training programme must equip teams to meet these obligations from day one.
Financial institutions using AI must demonstrate:
MAS published the Fairness, Ethics, Accountability, and Transparency (FEAT) principles to guide financial institutions on responsible AI adoption. These are not optional guidelines — they represent the regulatory expectation for how AI should be implemented in Singapore's financial sector.
AI models used in lending, insurance underwriting, and investment advisory must not discriminate against protected groups. Training should cover how to identify proxy variables that introduce bias (e.g., postal code as a proxy for ethnicity), statistical methods for measuring disparate impact, and remediation strategies when bias is detected.
Financial institutions must establish ethical boundaries for AI use. This includes defining which decisions AI can make autonomously versus which require human oversight, setting guardrails around the use of personal data, and ensuring AI systems do not manipulate customer behaviour.
Every AI-driven decision must have a clear chain of accountability. Training should establish who is responsible when an AI model rejects a loan application, flags a transaction as suspicious, or recommends a portfolio rebalancing. The answer cannot be "the algorithm decided."
Customers affected by AI-driven decisions have the right to understand why. Your teams need to know how to generate explanations from complex models and communicate them in language customers can understand.
AI-powered credit scoring is one of the highest-impact applications in Singapore banking. Models can incorporate alternative data sources — transaction patterns, employment stability indicators, and behavioural signals — to make more accurate lending decisions. However, these models carry significant regulatory risk if not properly governed.
Training covers how to build compliant credit scoring workflows: data sourcing and consent under PDPA, feature selection that avoids discriminatory proxies, model validation protocols aligned with MAS expectations, and ongoing monitoring for model drift that could degrade accuracy or introduce bias over time.
Singapore's robo-advisory market has matured rapidly. Firms offering AI-driven investment advice must comply with MAS Guidelines on Provision of Digital Advisory Services. Training covers the specific requirements: suitability assessments, algorithm transparency, disclosure obligations, and the human oversight mechanisms that must be in place.
Participants learn how to implement compliant robo-advisory workflows, including how to document the logic behind investment recommendations, how to handle edge cases where the algorithm's recommendation conflicts with the client's stated risk tolerance, and how to maintain audit trails for regulatory examinations.
AI has transformed AML operations in Singapore. Machine learning models can identify suspicious transaction patterns with far greater accuracy than rule-based systems, reducing false positives that burden compliance teams. However, MAS requires financial institutions to demonstrate that AI-based AML systems are at least as effective as traditional approaches.
Training covers the practical implementation: how to train AML models on historical suspicious activity reports, how to validate detection rates, how to explain model decisions to compliance officers who must file Suspicious Transaction Reports, and how to maintain the human judgement layer that MAS requires.
Insurers in Singapore are deploying AI for risk assessment, pricing, and claims processing. Training addresses the specific governance requirements: how to ensure underwriting models comply with the Insurance Act, how to handle automated claims decisions that customers may dispute, and how to implement the MAS requirement for human review of significant AI-driven decisions.
Participants leave with:
Singapore's financial services firms can access several SkillsFuture funding mechanisms for AI training:
Eligible employers receive a one-off S$10,000 credit to defray up to 90% of out-of-pocket training costs. AI training programmes from approved providers qualify. This significantly reduces the per-participant cost for financial institutions running cohort-based programmes.
Employees aged 40 and above receive higher subsidies — up to 90% of course fees. Given the seniority profile of many compliance, risk, and audit teams, this can substantially reduce programme costs.
The Institute of Banking and Finance (IBF) certifies AI-related courses under its standards framework. IBF-accredited programmes are eligible for additional subsidies of up to 90% of course fees, with a cap of S$3,000 per participant per programme. Financial institutions should prioritise IBF-accredited AI training to maximise funding.
For a cohort of 20 participants from a Singapore bank:
| Cost Component | Amount |
|---|---|
| Programme fee (3-day workshop) | S$4,500 per participant |
| Total before subsidies | S$90,000 |
| SFEC credit applied | -S$10,000 |
| IBF subsidy (70-90%) | -S$63,000 to -S$81,000 |
| Estimated net cost | S$0 to S$17,000 |
The economics of AI training for financial services in Singapore are compelling. With available subsidies, the per-participant cost can be reduced to a fraction of the headline rate.
Financial institutions need to demonstrate return on investment for AI training initiatives. Establish baseline metrics before training and track improvements at 30, 60, and 90 days post-programme.
Financial services AI training requires a provider with specific capabilities:
Pertama Partners delivers MAS-aligned AI training programmes for financial institutions across Singapore. Our workshops are designed by professionals who understand both the regulatory landscape and the practical realities of AI implementation in banking, insurance, and capital markets.
Yes. AI training programmes from approved providers qualify for SkillsFuture Enterprise Credit (up to S$10,000), IBF Standards Training Scheme subsidies (up to 90% of course fees), and SkillsFuture Mid-Career Enhanced Subsidy for employees aged 40 and above. Financial institutions can combine multiple funding sources to significantly reduce or eliminate out-of-pocket training costs.
MAS regulates AI through the Technology Risk Management (TRM) Guidelines and the FEAT (Fairness, Ethics, Accountability, Transparency) principles. Financial institutions must implement model risk management, demonstrate explainability of AI-driven decisions, maintain data governance under PDPA, and ensure board-level oversight of AI initiatives. Any AI training programme must equip teams to meet these specific obligations.
The highest-impact AI applications for Singapore banks include credit scoring with alternative data, anti-money laundering and transaction monitoring, robo-advisory services, regulatory reporting automation, and customer service chatbots. Each application carries specific regulatory requirements under MAS guidelines that must be addressed during implementation.
A comprehensive MAS-aligned AI training programme typically runs over 3 days for the core workshop, covering regulatory requirements, hands-on implementation, and advanced applications. This is followed by a 90-day implementation phase with periodic check-ins. Shorter 1-day awareness sessions are available for board members and senior management who need strategic oversight rather than hands-on skills.
Absolutely. Insurance companies and banks operate under different regulatory provisions and have distinct AI use cases. Insurance-focused training covers underwriting models, claims automation, and Insurance Act compliance, while banking-focused training emphasises credit risk, AML, and MAS Notice requirements. Pertama Partners customises every programme to the specific sub-sector and institution.
