Insurance fraud detection has always required pattern recognition across large data sets — the same claim structure submitted by different policyholders, the same garage handling a suspicious concentration of motor claims, the same hospital billing the same procedure at above-market rates for PMJAY beneficiaries. AI amplifies both sides of this: fraudsters increasingly use AI to generate synthetic documentation, and insurers must use AI to detect patterns at scale.
IRDAI's 2025 framework recognizes this explicitly by requiring dynamic — not static — red flag indicators. An insurer that reviews its fraud red flags once a year and updates them based on manual review of past cases is not meeting the standard. The framework requires indicators that evolve as fraud patterns evolve — which, in practice, means AI and analytics-driven fraud monitoring, not rule engines.
The IIB Network Effect
The most significant feature of the 2025 framework is the mandatory IIB participation. India's IIB is a genuine insurance sector AI infrastructure — no equivalent exists in the US (NAIC does not run a cross-insurer AI fraud analytics platform), the UK (FCA does not operate one), or the EU (EIOPA does not). An Indian insurer that participates in IIB gains access to industry-wide fraud intelligence that no individual insurer's own AI can replicate. The mandatory enrollment creates a sector-wide AI fraud governance layer above individual insurer systems.
IRDAI Insurance Fraud Monitoring AI Governance Checklist
- ☐ IIB enrollment: formal enrollment in IIB Fraud Monitoring Technology Framework completed; data interface between claims management system and IIB platform operational; fraud data reporting cadence established
- ☐ Dynamic red flag indicator system: AI/analytics-driven fraud indicator system operational; update process defined for incorporating IIB intelligence and internally detected fraud trends; static rule set replaced or supplemented with dynamic analytics
- ☐ Fraud Monitoring Committee: FMC constituted with defined membership; technology mandate documented; meeting cadence and escalation procedures defined; technology controls review scope includes AI fraud detection systems
- ☐ Board-approved Anti-Fraud Policy: board-approved policy in place; covers AI fraud detection methodology, IIB platform participation governance, technology vendor due diligence; annual review by board completed
- ☐ FMR-1 annual return: fraud incident tracking system capable of generating FMR-1 format data; filing process defined; April 30 deadline (30 days after March 31 FY close) calendared
- ☐ AI fraud detection vendor governance: for third-party AI fraud detection vendors (claim assessment AI, biometric verification AI), vendor due diligence on model accuracy, data handling, and DPDP compliance completed; contractual obligations for IIB data sharing consistent with vendor terms
- ☐ DPDP Act intersection: data shared with IIB assessed for DPDP purposes; privacy notice updated to cover fraud-prevention data sharing with IIB; purpose-limitation policy for IIB data use documented
- ☐ IRDAI examination readiness: IIB enrollment records, FMC minutes, board-approved policy, FMR-1 filings — packaged for IRDAI inspection