Data Modelling for Health Insurance Claims Analytics

This paper aims at discussing the analysis of health insurance claim through risk classification, fraudulence and cost prediction models. Combined with state-of-art data preprocessing and modelling techniques, insurers can better drive decision, minimize fraud, and better plan for financials. Logistic regression, random forest, gradient boost and models of similar category help in pattern analysis and cost of claim forecasting. They further effectiveness, equity and customer relations for implementing sound insurance that is sustainable. This work therefore emphatically speaks to the Bar on the structural revolution that data modelling has brought on the current health insurance analysis.