This is like a super-attentive fraud detective that reads every claim, checks all the data behind it, and flags anything suspicious in seconds instead of days.
Reduces insurance losses and investigation costs from fraudulent or exaggerated claims by automatically scanning large claim volumes and highlighting high‑risk cases for human review.
Proprietary fraud patterns and labeled claims data accumulated across customers, plus workflow integration into insurers’ claims systems that makes switching costly.
Hybrid
Feature Store
High (Custom Models/Infra)
Model training and refresh cycles on large, imbalanced claims datasets; integration with legacy claims/Policy Admin systems; and potential latency if LLM-based text analysis is used inline for every claim.
Early Majority
Positions AI as a dedicated fraud scanner for insurers, likely combining supervised fraud scoring with anomaly detection and possibly LLM-based document/text analysis, delivered as a focused solution rather than a full core-claims platform.