Development of Predictive Model for Health Insurance Risk and Premium in India

Objective: To design and validate a predictive analytics model for assessing customer risk and suggesting optimal health insurance premiums in the Indian market.

Health insurers in India face challenges in accurately pricing premiums due to limited underwriting data, varied health profiles, and regulatory constraints. Many insurers struggle to balance affordable premiums with claim risks, leading to adverse selection and unprofitable portfolios.

The project involves collecting and integrating anonymized datasets such as the IRDAI public disclosures, sample health insurance claim data, and government health datasets. The team will research actuarial principles, risk factors prevalent in India, and use machine learning techniques to develop a risk scoring and premium prediction model. Feature engineering will include socio-demographic, lifestyle, and medical history variables relevant to the Indian population.

Deliverables include a detailed business case, data exploration report, a documented predictive model (with code and explanation), and a dashboard for premium recommendation. The team will conduct robust validation using Indian insurance data and provide actionable insights for pricing strategies.

This model will help insurers improve risk assessment, enable data-driven premium setting, and support regulatory compliance, thus enhancing profitability and customer satisfaction in the competitive Indian health insurance market.