Imbalance‐Aware Credit Card Fraud Detection Using Multi‐Autoencoders and Generative Ensemble Learning

Credit card fraud detection remains a challenging research problem due to the class imbalance issue caused by the rarity of fraudulent transactions. Classical oversampling techniques such as SMOTE, ADASYN and their variants help balance data but do not reflect the nonlinear structure of real‐world fraud, leading to poor generalization. Recent state‐of‐the‐art hybrid frameworks that combine deep generative models and ensemble learning improve performance but treat representation learning, augmentation and fusion as disconnected stages. To address these limitations, we propose a unified multistage framework that integrates representation learning, generative augmentation and intelligent ensemble fusion. Our framework first extracts autoencoder‐based latent representations to capture discriminative and interpretable features; then, a label‐conditioned VAE‐GAN uses these embeddings to generate realistic synthetic fraud samples; finally, the enriched features are projected into a fusion space and classified using a pool of diverse learners, whose outputs are consolidated through an embedding‐aware intelligent ensemble and a meta‐ensemble layer. We benchmark the framework against two categories of baselines: oversampling‐based methods and state‐of‐the‐art hybrid fraud detection systems. Experiments on the European cardholder dataset show that our approach achieves a macro F1‐score of 95.15% and balanced accuracy of 92.85%, outperforming both baseline categories by 2.8%. Additional experiments on the IEEE‐CIS Fraud Detection dataset further validate the generalizability of the proposed framework on large‐scale, heterogeneous and feature‐rich fraud data. The results demonstrate that the proposed framework not only improves detection accuracy under severe imbalances but also maintains interpretability, offering a robust and scalable foundation for reliable financial risk control.