Federated machine learning for cross-bank credit card fraud detection: A privacy-preserving framework

Abstract

Federated machine learning is a solution to the problem of identifying instances of credit card fraud in any bank without violating privacy laws. The framework proposed in this paper any bank without violating privacy laws. The framework proposed in this paper introduces a privacy-preserving model that combines Federated Machine Learning (FML) with cutting-edge techniques such as differential privacy, homomorphic encryption, and secure multi-party computation (SMPC). The framework, which uses simulated financial transaction data sourced from publicly accessible datasets (IEEE-CIS and PaySim), trains local models at each financial organization and aggregates them into one global model. The system proposed can detect fraud 5–10% better than traditional single-bank models while at the same time providing high privacy standards. The usage of SMPC and homomorphic encryption avoids sensitive data sharing while differential privacy secures the system against data leakage in case of an attack. The nature of the results of the proposed system have encouraged regulatory authorities to pursue the route of the federated learning framework to detect fraud while simultaneously remaining on the right side of privacy laws like GDPR and DPDP. This framework is suitable for the banking sector and other industries that encounter similar privacy and security problems because of its adaptability to changing data protection legislation. The integration of multiple privacy-preserving technologies into a federated system for financial fraud detection is a significant contribution of this research.