Federated Learning for Cross‐Institution Credit Scoring under Privacy Constraints

Abstract

In recent years, credit scoring has become a critical tool for financial institutions to assess the creditworthiness of individuals and businesses. However, accurate credit scoring often requires access to large, diverse datasets from multiple institutions, which raises significant privacy and regulatory challenges. This paper proposes a federated learning framework for cross-institution credit scoring that enables collaborative model training without sharing raw data, thereby preserving user privacy under stringent regulatory constraints. Our approach incorporates privacy-enhancing techniques such as differential privacy and secure aggregation to mitigate data leakage risks. Experimental results on real-world credit datasets demonstrate that the proposed federated model achieves comparable accuracy to centralized training while adhering to privacy requirements. The study highlights the potential of federated learning to revolutionize credit scoring by enabling secure, scalable, and privacy-compliant cooperation among financial institutions.