International Journal of Scientific Research and Engineering Development

Small and Medium Enterprises (SMEs) in the U.S. face significant challenges in managing financial risks, primarily due to the lack of large-scale datasets, privacy issues, and the need for robust decision-making frameworks. This paper introduces a data-driven financial risk management system leveraging Federated Learning (FL) and privacy-preserving AI techniques to address these concerns. The system enables SMEs to collaboratively build financial risk models while maintaining the privacy of their sensitive data. By utilizing a federated learning framework, the system processes financial data from various distributed sources without sharing raw data, ensuring confidentiality. This paper discusses the design and implementation of this solution, which integrates privacy-preserving methods such as differential privacy and secure multi-party computation (SMPC). The evaluation of the system shows its potential to enhance financial risk analysis and decision-making in SMEs by providing accurate models without compromising data security. The system's performance is assessed in terms of accuracy and privacy, demonstrating its viability for real-world application in the SME sector. The results indicate that this framework can help reduce financial risks for SMEs, improve the quality of decision-making, and ensure compliance with data privacy regulations, offering a scalable solution for U.S. SMEs in managing their financial operations securely and efficiently.