Strengthening FinTech Defenses: A Novel Approach with Federated Learning and Anomaly Identification

Abstract

The paper examines integrating federated learning and anomaly detection techniques to strengthen cybersecurity in the financial technology sector. Such financial data is highly significant, and the intrusion-related concern has brought them together. The paper focuses on developing a solution using data processing in distributed mode and timely identifications of anomalies to the current cyber threats, data breaches, and fraud schemes. This paper further discusses the most crucial cybersecurity problem in FinTech, which is keeping financial information non-public to be protected in a digital environment. The paper offers federated learning, a distributed machine learning paradigm that combines anomaly detection to detect any unusual event as a potential security breach. This approach can overcome delicate and private data protection issues by using distributed data processing and anomaly detection algorithms while guaranteeing data security and safety. At the same time, the writing points out other roles, consequences, and capabilities as a possible holistic solution, which could, at some point, achieve cybersecurity, regulatory compliance, and resilience in the FinTech ecosystem.