x-shaolim: novel feature selection framework for credit card fraud detection

Fraud in financial data is a significant concern for both businesses and individuals. credit card transactions involve numerous features, some of which may lack relevance for classifiers and could lead to overfitting. a pivotal step in the fraud detection process is feature selection, which profoundly impacts model accuracy and execution time. in this paper, we introduce an ensemble-based, explainable feature selection framework founded on shap and lime algorithms, called &x-shaolim&. we applied our framework to diverse combinations of the best models from previous studies, conducting both quantitative and qualitative comparisons with other feature selection methods. the quantitative evaluation of the &x-shaolim& framework across various model combinations revealed consistent accuracy improvements on average, including increases in precision (+5.6), recall (+1.5), f1-score (+3.5), and auc-pr (+6.75). beyond enhanced accuracy, our proposed framework, leveraging explainable algorithms like shap and lime, provides a deeper understanding of features’ importance in model predictions, delivering effective explanations to system users.