AI Fraud Detection & Risk Management for Financial Services in Singapore

The programme is designed to align with MAS's 2025 proposed Guidelines on AI Risk Management, covering risk oversight, life cycle controls, and responsible AI use expectations. We integrate MAS FEAT (Fairness, Ethics, Accountability, Transparency) principles throughout all practical exercises and governance frameworks.

Yes. The programme is eligible for SFEC (S$10,000 per employer at up to 90% offset) and ETSS for qualifying SMEs. Additionally, MAS allocated S$100 million under the National AI Strategy 2.0 specifically for AI and quantum capabilities in the financial sector, which may support qualifying AI transformation initiatives.

Our programme covers multi-jurisdiction compliance including MAS (Singapore), OJK (Indonesia), BNM (Malaysia), and BSP (Philippines) requirements. Singapore-based institutions operating across ASEAN learn to build AI governance frameworks that satisfy the most stringent regulator while being adaptable to each jurisdiction.

All AI workflows covered in this programme comply with PDPA obligations. The Personal Data Protection Act (PDPA) imposes financial penalties of up to 10% of an organisation's annual turnover in Singapore for organisations with annual local turnover exceeding S$10 million. The PDPA requires mandatory data breach notification to the PDPC within 3 calendar days of assessing that a notifiable breach has occurred. We build PDPA-compliant data handling, consent management, and audit trails into every practical exercise.

This programme is eligible for the SkillsFuture Enterprise Credit (SFEC) providing S$10,000 per employer at up to 90% offset, and the Enhanced Training Support for SMEs (ETSS) offering up to 90% course fee subsidies. The Productivity Solutions Grant (PSG) may also cover pre-approved AI solutions at up to 50% funding.

Timeline varies by complexity. Simple transaction scoring models can go live in 4-8 weeks. More complex behavioral analytics or network fraud detection may take 3-6 months including model training, testing, and regulatory validation. We'll help you build a phased roadmap during the training programme.

All AI fraud models require calibration. We teach threshold tuning, champion-challenger testing, and customer impact analysis to optimise the balance between fraud detection and customer experience. Expect 2-3 months of iterative refinement post-deployment.

Yes, through unsupervised anomaly detection. AI identifies unusual patterns even without labelled fraud examples. However, supervised models (trained on known fraud) are more accurate for established tactics. We teach hybrid approaches combining both methods for comprehensive coverage.

Not necessarily. Many AI fraud platforms offer no-code interfaces for fraud analysts. The training focuses on using AI tools, interpreting outputs, tuning models, and managing vendors — not building algorithms from scratch. Technical depth is adjusted to your team's background.

We teach explainability frameworks including reason codes (e.g., 'transaction declined due to unusual location'), customer communication scripts, and appeal processes. You'll learn to balance fraud prevention with customer transparency and regulatory expectations for algorithmic fairness.