Generative Distribution Modeling for Credit Card Risk Identification under Noisy and Imbalanced Transactions

This study addresses the challenges of highly concealed abnormal behaviors, scarce fraud samples, and complex high-dimensional feature structures in credit card transactions by proposing a fraud detection framework that integrates a generative prior. The method first uses a generative model to learn the latent distribution of normal transactions and builds a stable behavioral baseline, where distributional constraints strengthen the structural representation of normal patterns. The latent variables from the generative model are then embedded jointly with discriminative features, allowing the model to capture both local attributes and global behavioral structures within a unified representation space and thereby improving the detection of weak anomalies and structural deviations. To evaluate performance comprehensively, the study conducts multidimensional experiments, including hyperparameter sensitivity, environmental disturbance sensitivity, and data disturbance sensitivity, to analyze how the generative prior affects model stability and discriminative capability under different external conditions. The results show that the framework outperforms traditional methods across Accuracy, Precision, Recall, and AUC, and maintains higher robustness and consistency in noisy, imbalanced, and dynamic transaction environments. The findings demonstrate that integrating generative distribution modeling with discriminative risk representation significantly enhances the model's sensitivity to abnormal behaviors and provides a more structured and reliable solution for risk identification in complex financial settings.