A Framework for Fraud Detection in Banking Transactions Using Machine Learning and Federated Learning

Abstract

The digital banking revolution has transformed financial services to make payment faster, more convenient, and borderless. But with this revolution came an abrupt increase in fraudulent transactions through credit cards that threatening both the financial institutions and the customers. While conventional fraud detection mechanisms are not capable of addressing new-generation fraud patterns, there is an increasing demand for intelligent, adaptive, and secure solutions with high precision without any data privacy compromise. Proposed model leverages four machine learning models, Linear Regression, Decision Tree, Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN). LSTM and CNN are used due to their power in learning complicated sequential and feature-based patterns, with Decision Tree and Linear Regression added due to their ease, quick execution, and interpretability. Every model is locally trained on partitioned banking datasets for each simulated client. Model parameters are combined with the Federated Averaging (FedAvg) algorithm to create a globally shared fraud detection system. Experimental testing was conducted on a real-world banking transaction data set published in a non-IID manner to mimic real-world client situations. The federated learning paradigm achieved encouraging results: CNN and LSTM models achieved detection accuracy rates of over 95%, with outstanding performance in the detection of hidden or time-series-based fraud patterns. The Decision Tree model also achieved steady performance at 91% accuracy, and Linear Regression achieved a reasonable baseline at 88%. These results indicate that even simple models, when used in a collaborative federated environment, can contribute meaningfully to fraud detection. This research contributes to the body of research supporting federated banking solutions and fills a significant gap by demonstrating how several ML models can coexist and collaborate in a decentralized setup for fraud detection through credit card transactions.