Hybrid Feature Engineering Based on Customer Spending Behavior for Credit Card Anomaly and Fraud Detection

For financial institutions, credit card fraud detection is a critical activity where the accuracy and efficiency of detection models are important. Traditional methods often use standard feature selection techniques that may ignore refined patterns in transaction data. This paper presents a new approach that combines feature aggregation with Exhaustive Feature Selection (EFS) to enhance the performance of credit card fraud detection models. Through feature aggregation, higher-order characteristics are created to capture complex relationships within the data, then find the most relevant features by evaluating all possible subsets of features systemically using EFS. Our method was tested using a public credit card fraud dataset, PaySim. Four popular learning classifiers-random forest (RF), decision tree (DT), logistic regression (LR), and deep neural network (DNN)-are used with balanced datasets to evaluate the techniques. The findings show a large improvement in detection accuracy, F1 score, and AUPRC compared to other approaches. Specifically, our method had improved F1 score, precision, and recall measures, which underlines its ability to handle fraudulent transactions' nuances more effectively as compared to other approaches. This article provides an overall analysis of this method's impact on model performance, giving some insights for future studies regarding fraud detection and related fields.