Compliance-First Hybrid Framework for Automated Insurance Claim Verification

Checking health insurance claims is mandatory but it is time consuming process. If a claim is checked manually, then it will increase the operational cost and take more time for patient settlement. Many of the existing automated systems are based on either strict rulebased checks or data-driven models only, which decrease their capacity to process complicated logic of policy. We made a hybrid machine learning system that mixes structured claim information, rulebased validation, and domain-aware attributes of user behavior, document reliability, risk indicators, and policy compliance. After doing dataset cleaning and feature engineering, a gradient boosting then our model categorizes claims into acceptable, not acceptable, or needing more information. The model gained high consistency (around 87%) and scalability over conventional rule-based approaches.