I Evaluated Half a Million Credit Records with Federated Learning. Here’s What I Found
AI Summary: This article presents research findings on the challenges of balancing privacy, fairness, and accuracy in credit scoring models, particularly under the constraints of GDPR and Fair Lending laws. The study, which analyzed 500,000 credit records, reveals that achieving all three objectives simultaneously is difficult at a small scale due to the interference of privacy noise on fairness algorithms. However, at an enterprise scale involving 300 federated institutions, the research demonstrates that it is possible to achieve 96.94% accuracy, a fairness gap of only 0.069%, and maintain moderate privacy (ε = 1.0) without compromising any of the objectives. The findings suggest that collaborative approaches can effectively navigate the regulatory tensions faced by credit risk managers.