Think of this as a very fast, very experienced claims investigator that has read millions of past cases. Every time a new claim comes in, it quietly checks for patterns that previously signaled fraud, flags suspicious ones for humans to review, and lets straightforward claims sail through faster.
Reduces financial losses from fraudulent insurance claims while speeding up processing of legitimate claims by automatically detecting suspicious patterns and prioritizing investigator attention.
Access to large, proprietary historical claims and policy data combined with embedded workflows in the insurer’s claims systems can create a moat; over time models trained on this private data and integrated into existing processes become hard for competitors to replicate.
Classical-ML (Scikit/XGBoost)
Feature Store
Medium (Integration logic)
Model performance and data quality depend heavily on clean, well-labeled historical claims data and robust integration with legacy policy/claims systems; latency and cost become issues if LLMs are added for explanations or investigator assistance.
Early Majority
Positioned as an AI-first enhancement to the claims process, using automated pattern detection and scoring to augment human investigators rather than replace them, and likely combining classical fraud-scoring models with newer NLP/LLM components for documentation analysis and investigator support.