Claims Fraud Investigation

Fraud investigations are informal notes in adjuster files when something 'seems suspicious.' No standardized template exists for documenting fraud indicators, investigation steps, or conclusions. SIU investigators keep handwritten case logs. Whether a claim is referred to SIU depends entirely on individual adjuster judgment with no explicit referral criteria.

None — AI cannot systematically detect fraud patterns without structured investigation records or explicit fraud indicators. Fraud detection relies entirely on adjuster intuition, with no ability to learn from historical investigations or apply consistent screening logic.

SIU investigations are documented in the claims system with basic structure: fraud indicators are listed in free text, investigation notes capture activities performed, and final determination is recorded. However, fraud indicators lack standardized taxonomy — adjusters describe suspicious elements in natural language ('claimant nervous on phone,' 'damage doesn't match story'), making pattern detection difficult.

AI can search investigation text for keywords but cannot reliably identify fraud patterns or predict which claims warrant SIU referral because fraud indicators aren't categorized consistently. Machine learning models can't train effectively on unstructured narrative descriptions.

SIU investigations capture structured fraud indicators using standardized taxonomy: prior claim frequency scores, loss location consistency ratings, reporting delay flags, witness availability, injury treatment pattern anomalies. Each indicator has severity classification (low, medium, high risk). Investigation activities are recorded with timestamps and outcomes. Determinations include explicit rationale referencing which indicators were most significant.

AI can analyze structured fraud indicators to predict SIU referral likelihood, recommend investigation priority, and identify common fraud pattern clusters. However, AI cannot fully automate fraud determination because investigation logic (weighing conflicting evidence, assessing witness credibility) isn't formally specified — each investigator applies their own judgment.

SIU investigations follow formalized protocols: fraud indicator combinations trigger automatic SIU referral, investigation procedures are specified by fraud type (staged accidents require scene investigation, buildup injury claims require IME), and determination criteria are explicit (substantiated fraud requires corroborated witness statements, documented loss inconsistencies, or admission). Every determination references the specific evidence and criteria applied.

AI can automate initial fraud screening, prioritize investigations by fraud score, and recommend investigation approaches based on fraud type. Complex fraud determinations (assessing witness credibility, weighing conflicting evidence) still require investigator judgment. However, AI cannot learn from closed investigations to refine fraud models because investigation outcomes aren't linked back to initial AI screening scores.

SIU investigation outcomes feed back to fraud detection models. When investigations conclude, the system records whether initial AI fraud scores were accurate, which indicators proved most predictive, and what fraud patterns emerged. This feedback continuously refines fraud detection algorithms, improving screening accuracy and SIU referral precision. Fraud models learn from every investigation outcome, adapting to emerging fraud schemes.

AI fraud detection improves continuously through closed-loop learning, accurately screening 95%+ of claims and referring only high-probability fraud cases to SIU, optimizing investigator workload. However, AI operates reactively — it detects fraud indicators after claims are filed. Predictive fraud prevention (identifying high-risk policies before losses occur) isn't possible because fraud models don't integrate with underwriting.

Fraud detection operates across the entire policy lifecycle. At underwriting, AI screens applicants for fraud indicators (address inconsistencies, prior fraud history, coverage anomalies), generating fraud risk scores that inform underwriting decisions. At FNOL, claims are screened against evolving fraud patterns learned from SIU investigations. Post-claim, settlements and legal outcomes update fraud models. Fraud detection is formalized, proactive, and continuously learning across all insurance touchpoints.

Fully autonomous fraud prevention and detection across underwriting and claims. AI identifies high-risk applicants at policy inception, screens all FNOLs for fraud indicators, and refers only substantiated cases to SIU, reducing fraud losses by 70%+ while minimizing investigator workload on false positives.

Ceiling of the CMC framework for this dimension.