Online Banking Fraud Detection Model: Decentralized Machine Learning Framework to Enhance Effectiveness and Compliance with Data Privacy Regulations

In the rapidly evolving landscape of digital finance, the increasing sophistication of fraudulent activities has created significant challenges for traditional detection systems. This research paper investigates the integration of federated learning with unsupervised deep learning techniques to meet the dual demands of data privacy and robust fraud detection. Using two real-world datasets, the Credit Card Fraud dataset and the NeurIPS 2022 Bank Account Fraud dataset, we developed a federated framework based on deep autoencoders. The framework simulates decentralized model training across multiple financial nodes while ensuring that raw data remains local. The methodology includes detailed data pre-processing steps, the construction of a compact autoencoder architecture and a threshold-based approach to anomaly detection. Experimental outcomes demonstrate the model’s ability to distinguish between legitimate and fraudulent transactions by the use of performance evaluation through the use of Receiver Operating Characteristic (ROC) curves, confusion matrices, and reconstruction error distributions. Despite the challenges of class imbalance and data heterogeneity, the proposed model achieved promising results by maintaining competitive discrimination capabilities. Overall, the research study establishes the potential of federated learning combined with anomaly detection to provide scalability, privacy preservation, and interpretable fraud detection solutions suitable for real-world financial environments.