Neural Network-based Model for Detecting Credit Card Fraud: A Comparative Study of Oversampling Techniques and Feature Selection

Abstract

Credit card fraud detection is complicated by the severe class imbalance typical of transaction data, because fraudulent cases represent only a small proportion of observations. This study develops a neural-network-based model for classifying transactions as legitimate or fraudulent and compares combinations of two oversampling techniques and two feature-selection approaches. The dataset contains 20,000 observations and 26 variables, with 339 fraudulent transactions (1.7%). After removing the transaction identifier and transforming categorical variables, class imbalance was addressed using the Synthetic Minority Oversampling Technique (SMOTE) and Adaptive Synthetic Sampling (ADASYN). Relevant features were then selected using Pearson’s correlation coefficient or XGBoost, producing four configurations: SMOTE + Pearson, SMOTE + XGBoost, ADASYN + Pearson, and ADASYN + XGBoost. The models were evaluated using accuracy, precision, recall, specificity, F1 score, loss, mean squared error, training and validation curves, and receiver operating characteristic analysis. Although the ADASYN + Pearson configuration produced high nominal accuracy and recall, its zero specificity and lower F1 score indicated poor identification of legitimate transactions. In contrast, the ADASYN + XGBoost configuration showed a more balanced performance across the evaluation criteria, with accuracy, precision, recall, specificity, and F1 score each reported at 79%. These results support ADASYN + XGBoost as the best-performing configuration among the four models evaluated in this study.