Algorithmic Implementation for Insurance Fraud Detection

Abstract

In the insurance sector, spotting insurance fraud is crucial. Insurance is vital for finance and societal security. Frequent fraud causes losses to insurers and the financial system, impacting insurance companies' functioning and trust. Insurance fraud involves policyholders giving false information or creating incidents to claim compensation. This harms insurers and raises premiums for honest policyholders. To combat frauds, insurers must use methods to detect and prevent them. This study assesses popular ML algorithms like Gradient Boosting Decision Trees and XGBoost for fraud detection efficiency and verifiability. Metrics such as efficiency, recall rate, precision F1 score, and AUC score are calculated using these methods.