The financial industry continues to face significant challenges due to credit card fraud, which causes significant financial losses worldwide. Traditional machine learning techniques usually depend on centralized data aggregation, raising issues with inter-institutional data sharing, user privacy, and regulatory compliance. This paper presents a privacy-aware framework that uses Federated Learning (FL) to identify fraudulent transactions in order to overcome these constraints. This setup maintains anonymity by having several simulated financial organizations (clients) train models privately on their own private datasets without disclosing raw data. To address the class imbalance common in fraud detection, each client employs a Random Forest classifier in combination with the Synthetic Minority Over-sampling Technique (SMOTE). Using the Flower architecture, the federated system is constructed and assessed throughout a number of communication cycles. According to the results, the FL- based strategy preserves data privacy while achieving accuracy on par with centralized methods that use models like Random Forest, Decision Tree, and Logistic Regression. The feasibility of federated learning for safe and scalable fraud detection in dispersed situations is highlighted by this study.
Koppula Dennis (Wed,) studied this question.