fraud_oracle.csv

This study leverages advanced methods and algorithms to create an automated insurance fraud detection system, using real insurance fraud data. The system consists of four phases: data resampling (Over, Under, and hybrid), feature selection (Filtering, Wrapping, and Embedding), binary classification (Bagging and Boosting), and explanatory model analysis (Shapley Additive Explanations, Break-down plots, and variable-importance Measures). Results show that not all resampling techniques improve algorithm performance, but all feature selection methods do. Notably, the Boosting algorithm, incorporating the Neighborhood Cleaning Rule for resampling and Tree-based feature selection, excels in detecting insurance claim fraud.