Federated Learning in Finance and Fraud Detection

Abstract

Federated Learning (FL) is a machine learning technique that helps safeguard data privacy by enabling several financial institutions to collaborate to train AI models without exchanging real data. In FL, the AI model is distributed to each institution, whereas in traditional machine learning, all the data is sent to a single location for training. To protect the data, each bank or institution uses its own private financial data to train the model and only transmits model updates to a central server. For activities including identifying fraud, evaluating credit risk, executing automated transactions, providing individualized services, and forecasting market trends, this method assists in identifying helpful patterns in financial data. Crucially, it adheres to significant laws and regulations and protects data privacy..Our research focuses on developing a privacy-protecting FL system that functions successfully in the financial sector, demonstrating how it can address the unique difficulties of handling data dispersed across several locations, guaranteeing security, and adhering to legal requirements. The basic thesis is that FL is the essential technology that enables responsible use of financial data to create intelligent, safe, and cooperative AI systems