Insurance Claim Fraud Analysis Using Supervised and Unsupervised Learning

Original price was: ₹6,999.00.Current price is: ₹3,999.00. inc GSTs*

Coordinated insurance fraud, such as staged accidents or inflated claims, costs the industry billions annually. These patterns are often hidden within large volumes of legitimate data, necessitating a hybrid approach that combines supervised historical learning with unsupervised anomaly detection to identify novel fraud tactics.

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This analysis system utilizes a hybrid model combining supervised classifiers (Gradient Boosting) for known fraud patterns and unsupervised clustering (K-Means/DBScan) to discover new anomalies. It processes claim data, including claimant history, medical reports, and vehicle damage assessments. The system assigns a “fraud risk score” to each claim, allowing investigators to prioritize high-risk files. This implementation helps insurance providers reduce leakage and administrative costs by automating the initial vetting process and highlighting complex, non-obvious fraudulent relationships.

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Insurance Claim Fraud Analysis Using Supervised and Unsupervised Learning

Insurance Claim Fraud Analysis Using Supervised and Unsupervised Learning

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