Fraud Alert

There is no fraud detection at the underwriting stage. Applications are taken at face value. Fraud is discovered only after a claim is filed and the SIU investigates, by which point the carrier has already issued a policy and paid for losses on a fraudulently obtained contract.

None — AI cannot detect application fraud because no fraud alert mechanism exists during underwriting.

Some fraud screening exists but is inconsistent. The SIU reviews flagged submissions when a tipline call comes in or when an underwriter notices something suspicious. Known fraud databases are checked occasionally but not for every submission. Fraud alerts are informal — a phone call from SIU to the underwriter saying 'hold off on that one' — with no formal record in the underwriting system.

AI could search fraud databases for known indicators, but the inconsistent screening process means many submissions bypass fraud checks entirely. No structured alert records exist for AI to learn from.

Fraud screening runs automatically for every submission. Applications check against fraud databases and flag known fraud indicators (prior fraud convictions, suspicious loss patterns, identity discrepancies). Each triggered alert generates a structured record — alert type, severity, matching criteria, and source database. Underwriters see fraud alerts in the workbench and must document their disposition. But the screening is rule-based — it catches known patterns but not novel fraud schemes.

AI can apply rule-based fraud detection consistently across all submissions. Cannot detect novel fraud patterns because the screening relies on known indicators and predefined rules rather than anomaly detection.

Fraud alerts combine rule-based screening and predictive anomaly detection. Predictive models trained on historical fraud cases flag submissions with unusual patterns — application velocity anomalies, address clustering, suspiciously precise claim histories, or mismatches between declared and enriched property characteristics. Each alert records the triggering signals, model confidence score, and recommended investigation pathway. An SIU analyst can query 'show me all pending submissions where the fraud model score exceeds the investigation threshold and the primary flag is identity discrepancy' and get a prioritized worklist.

AI can detect both known fraud patterns and novel anomalies, prioritize investigation workload, and provide detailed alert context for SIU analysts. Cannot yet perform automated fraud disposition because the model outputs require human judgment for complex cases.

Fraud alerts are schema-driven with formal entity relationships. Each alert links to the specific application fields that triggered it, the enrichment discrepancies identified, the behavioral anomalies detected, and the historical fraud cases with similar patterns. Investigation workflows integrate directly — an AI agent can trace from an alert to the specific evidence, compare against resolved fraud cases, and recommend disposition with supporting reasoning. Fraud model performance metrics track false positive rates and detection accuracy.

AI can perform autonomous fraud triage — auto-clearing low-risk alerts, escalating high-confidence fraud signals, and assembling investigation packages for SIU. Automated disposition for clear-cut fraud indicators with human oversight for ambiguous cases.

Fraud detection operates in real-time throughout the application process. As the applicant enters information, identity verification, enrichment discrepancy detection, and behavioral analysis run continuously. Fraud alerts emerge dynamically as signals accumulate — early in the application process, not after submission. The fraud assessment evolves with every keystroke, every enrichment result, every behavioral signal.

Fully autonomous real-time fraud detection. AI identifies, assesses, and responds to fraud signals as they emerge throughout the application process.

Ceiling of the CMC framework for this dimension.