Motor Insurance Fraud Detection Model for Indian Claims Processing

Objective: To develop a predictive model that identifies potentially fraudulent motor insurance claims in the Indian market using real-world data and domain-specific features.

Motor insurance fraud is a significant challenge for insurers in India, resulting in increased premiums and financial losses. Detecting fraudulent claims is complicated due to the diversity of motor vehicles, claim types, and regional variations in fraud tactics.

The project involves gathering and analyzing historical claim data from Indian insurers, using statistical and machine learning techniques to identify patterns indicative of fraud. The team will design a supervised learning model, considering local claim processes, regulatory norms, and typical fraud schemes observed in India.

Deliverables include a cleaned dataset, feature engineering report, trained fraud detection model (with explainability), validation results, and an executive dashboard for claim investigators. The analysis will compare model performance with baseline manual detection rates.

The business impact is improved claims efficiency, reduced fraud-related losses, and actionable insights for risk managers. The solution will help insurers prioritize suspicious claims for investigation, supporting faster and more accurate claim settlements.