A Novel Federated Transfer Learning Framework for Credit Card Fraud Detection Under Heterogeneous Data Conditions

Abstract

The exponential growth of e-commerce and advancements in financial technology have escalated credit card fraud into a major threat, resulting in billions of dollars in global losses annually. This necessitates the development of sophisticated fraud detection systems capable of real-time anomaly interception to safeguard financial activities. While federated learning frameworks have been employed to address data privacy concerns in financial applications, existing approaches often fail to account for the heterogeneity in data distributions across different institutions, such as banks, which hinders collaborative model training. In response, this paper introduces the FED-SPFD model, an innovative federated learning framework designed to detect credit card fraud amidst multi-party heterogeneous data. The model employs a share–private segmentation approach to distinguish shared from private data attributes, facilitating unified feature representation learning. It aligns disparate shared features through local sufficient statistics, thus preventing privacy breaches without directly sharing sample data. Additionally, the integration of a “private autoencoder + standard Gaussian alignment” mechanism stabilizes the training process by ensuring consistent private feature distributions. The efficacy of the FED-SPFD model is demonstrated using a real-world dataset from Kaggle, showcasing significant improvements in recall rate compared to state-of-the-art methodologies. Comprehensive evaluation through ablation studies further validates the framework’s robust contributions to accurate and privacy-preserving fraud detection. Practically, this work offers policymakers a compliant cross-institutional risk collaboration paradigm and provides financial institutions with a privacy-protective solution to enhance fraud detection without data sharing violations.