AI Training for Singapore Financial Services — MAS-Aligned Workshops

MAS Technology Risk Management and AI Training Requirements

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.

What the MAS Expects from AI Governance

Financial institutions using AI must demonstrate:

• Model risk management — documented processes for validating AI models before production deployment, including bias testing, accuracy benchmarking, and stress testing under adverse conditions
• Explainability — the ability to explain AI-driven decisions to customers, auditors, and regulators in plain language
• Data governance — clear policies on what data feeds into AI models, how it is sourced, validated, and protected under the Personal Data Protection Act (PDPA)
• Incident management — defined escalation procedures when AI systems produce erroneous or biased outputs
• Board oversight — senior management and board-level awareness of AI risks and strategic direction

FEAT Principles: The Foundation for Responsible AI in Finance

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.

Fairness

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.

Ethics

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.

Accountability

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."

Transparency

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 Applications in Singapore Financial Services

Credit Scoring and Lending

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.

Robo-Advisory Compliance

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.

Anti-Money Laundering (AML) and Transaction Monitoring

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.

Insurance Underwriting and Claims

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.

Workshop Structure and Deliverables

Day 1: Regulatory Landscape and Governance Framework

• MAS TRM Guidelines deep-dive with specific AI provisions
• FEAT principles: practical implementation for your institution
• PDPA requirements for AI data processing in financial services
• Building your AI governance committee: roles, responsibilities, and reporting lines
• AI risk assessment template customised for financial services

Day 2: Hands-On Implementation

• Credit scoring model governance: validation, monitoring, and documentation
• Prompt engineering for financial analysis: earnings summaries, risk reports, and client communications
• AI-assisted compliance workflows: regulatory reporting, policy review, and training documentation
• Building AI acceptable use policies for your institution
• Vendor assessment: evaluating AI tools for financial services use (data residency, encryption, audit capabilities)

Day 3: Advanced Applications and Measurement

• AML and fraud detection with AI: implementation and governance
• Robo-advisory compliance workshop
• Measuring ROI on AI investments: productivity metrics, risk reduction, and customer satisfaction
• 90-day implementation roadmap for your institution
• Executive briefing template for board and MAS reporting

Workshop Deliverables

Participants leave with:

• Customised AI governance framework aligned to MAS TRM Guidelines
• FEAT principles compliance checklist for your institution
• AI risk assessment templates for credit, AML, and advisory applications
• Prompt libraries for financial analysis, compliance reporting, and client communications
• 90-day implementation roadmap with milestones and accountability assignments
• Board-ready executive summary template for AI initiatives

SkillsFuture Funding for Financial Sector AI Training

Singapore's financial services firms can access several SkillsFuture funding mechanisms for AI training:

SkillsFuture Enterprise Credit (SFEC)

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.

SkillsFuture Mid-Career Enhanced Subsidy

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.

IBF Standards Training Scheme

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.

Practical Funding Application

For a cohort of 20 participants from a Singapore bank:

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.

Measuring AI Training Impact in Financial Services

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.

Efficiency Metrics

• Compliance reporting time — measure the hours required to produce regulatory reports before and after AI training. Institutions typically see 30-50% reduction in reporting preparation time
• Due diligence turnaround — track the elapsed time for standard due diligence processes. AI-trained teams complete due diligence 25-40% faster
• Customer response time — measure average response times for customer enquiries handled by AI-trained staff. Response times typically improve by 40-60%
• False positive reduction — for AML and fraud detection teams, measure the reduction in false positive alerts after implementing AI-enhanced screening processes

Quality Metrics

• Audit findings — track whether AI-assisted work products receive fewer audit queries and findings
• Error rates — measure error rates in compliance submissions, reporting, and customer communications
• Customer satisfaction — survey customers on service quality before and after AI implementation in customer-facing processes

Strategic Metrics

• AI adoption rate — percentage of trained staff actively using AI tools in their daily work (target: 80% within 60 days)
• Use case expansion — number of new AI use cases identified and implemented by trained staff beyond the initial training scope
• Governance compliance — percentage of AI use that complies with the governance framework established during training

Selecting the Right Training Partner

Financial services AI training requires a provider with specific capabilities:

• Regulatory knowledge — the provider must understand MAS TRM, FEAT, and PDPA requirements in depth, not just at a surface level
• Financial services experience — generic AI training will not address the specific governance requirements of banking, insurance, and capital markets
• Practical focus — the programme must produce tangible outputs (governance frameworks, risk assessments, policy documents) that your institution can implement immediately
• IBF accreditation — to maximise SkillsFuture funding, select a provider whose programmes are IBF-accredited
• Post-training support — implementation support after the workshop is critical for translating learning into operational change

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.