Case Studies - BFSI & Public sector

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 Siloed systems were producing inconsistent records ahead of MAS and Basel III audits. Manual reconciliation was slow, error-prone, and not going to scale.

What we did: Built a data governance layer standardising metadata across transaction systems. Deployed an AI reconciliation model that auto-matched transactions and flagged anomalies. Integrated RAG-based compliance lookups so auditors could query by jurisdiction, reporting period, and product type.

30% — Reduction in reconciliation effort Hours — To answer regulator queries, down from weeks

7-month engagement · MAS & Basel III context

A rules-based fraud system was missing subtle patterns and generating too many false positives — slowing claims and frustrating customers who had done nothing wrong.

What we did: Built a data governance and stewardship layer aligned with APAC insurance regulatory requirements. Deployed an AI-driven underwriting and fraud intelligence platform combining supervised ML with RAG-based compliance lookups. Integrated jurisdiction-specific metadata filtering for regulator queries.

20% — Improvement in underwriting decision accuracy 25% — Reduction in claims cycle time

6–8 month engagement · APAC insurance context

Public Sector — Singapore

Explainable fraud detection for subsidy disbursement

Rising fraud in subsidy disbursement, compounded by high false positives that consumed investigative resources — and a system that couldn't explain its own decisions.

What we did: Designed a graph-based fraud analytics engine to surface relationships between applicants, providers, and claims. Layered RAG compliance checks against PDPA and Singapore's AI governance guidelines. Embedded explainability dashboards so every flagged case could be defended by investigators.

15% — Uplift in fraud detection rate 40% — Reduction in false positives

5–6 month engagement · Referenced as a Singapore AI governance pilot