Abstract

Existing fraud detection studies frequently report inflated performance due to information leakage arising from improper handling of class imbalance techniques. This study presents a leakage-free evaluation of a one-dimensional Convolutional Neural Network (CNN) for credit card fraud detection under extreme class imbalance using the Kaggle Credit Card Fraud Detection dataset, which contains 284,807 anonymized real-world transactions. The CNN was evaluated against Logistic Regression, Decision Tree, Random Forest, and Gradient Boosting baselines. To prevent information leakage during model evaluation, the Synthetic Minority Over-sampling Technique (SMOTE) was restricted entirely to the training phase of the validation process. Model performance was assessed using the Matthews Correlation Coefficient (MCC) and ROC-AUC, as both provide a more reliable assessment of imbalanced classification tasks than conventional accuracy. Under these controlled conditions, the CNN achieved an MCC of 0.7081 and a ROC-AUC of 0.9659. The CNN produced the highest MCC among the evaluated models, reflecting stronger minority-class discrimination than the benchmark methods. The findings establish a reproducible benchmark for evaluating CNN-based fraud detection models under severe class imbalance while minimizing the risk of information leakage.

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