Informatica

Credit card fraud (CCF) poses a growing threat to the global economy, with payment card fraud losses reaching approximately USD 33.8 billion in 2023. While deep learning approaches such as CNNs, autoencoders, and Transformer-based models have improved detection accuracy, their “black box” nature restricts adoption in high-stakes financial domains where transparent and auditable reasoning is essential for trust and regulatory compliance. To address this challenge, we propose MTLFraudDetect, a multi-task framework that combines fraud detection with complementary model-analysis techniques. The model combines fraud classification with two auxiliary tasks—data reconstruction and amount prediction—while employing curriculum learning and PCGrad to stabilize multi-task optimization. For model analysis, the framework combines reconstruction-based anomaly-sensitivity signals with SHAP-based decision attribution. Reconstruction errors indicate transaction-level and feature-wise deviations from patterns learned by the reconstruction branch, whereas SHAP examines how the classifier’s shared latent representation and transaction amount contribute to its final prediction. Under an explicitly separated development-test protocol with training-fold-only preprocessing and out-of-fold hyperparameter selection, MTLFraudDetect achieves competitive F1-score and AUC-PR performance while providing complementary evidence at the levels of global anomaly detection, feature-wise error localization, and post-hoc decision attribution. These results do not by themselves establish formal explanatory faithfulness or complete interpretability.