Think of it as a 24/7 digital detective that reviews every insurance claim, compares it against mountains of past cases and patterns, and flags the ones that look suspicious so your human investigators only focus on the riskiest claims.
Reduces losses and operational cost from fraudulent or inflated insurance claims by automatically detecting suspicious behavior and patterns that are hard for manual teams or basic rule engines to catch.
If VAARHAFT trains and continually refines models on proprietary fraud patterns and claims data across clients, that cross-carrier fraud intelligence plus workflow integration into claims systems can create a defensible data and workflow moat.
Classical-ML (Scikit/XGBoost)
Feature Store
High (Custom Models/Infra)
Model retraining and feature engineering at scale across multiple insurers and rapidly changing fraud patterns; potential latency if models are applied synchronously on every claim in real time.
Early Majority
Positioned as an AI-native, advanced fraud prevention system tailored for insurance rather than a generic rule-based fraud engine, likely emphasizing modern ML models, pattern detection on complex data, and tighter integration with digital claims workflows.