A Hybrid Anomaly Detection Framework Combining Supervised and Unsupervised Learning for Credit Card Fraud Detection

Background Credit card fraud detection remains a major challenge because of the highly imbalanced nature of transaction data. Conventional supervised models often suffer from low recall or high false positive rates, whereas unsupervised methods lack precision. Methods In this study, we propose a hybrid anomaly detection framework that combines an unsupervised autoencoder trained on normal transactions to capture reconstruction error patterns with a supervised XGBoost classifier trained on the same dataset. The hybrid system integrates both scores via an optimized thresholding mechanism to balance sensitivity and specificity. We evaluated the model on the publicly available Kaggle creditcard.csv dataset comprising 284,807 transactions, with only 492 labelled fraudulent. Results The proposed model achieved superior performance, with a recall of 0.9250, precision of 0.9569, F1-score of 0.9407, and Matthews Correlation Coefficient (MCC) of 0.9407, with an accuracy of 0.9998, surpassing the results of similar published models using the same dataset. Conclusions This framework provides a practical, reproducible, high-performance solution for detecting financial fraud. The code, model configuration, and data-processing pipeline were made available to support transparency and future research.