Unified Federated AI Framework for Credit Scoring

This research introduces a unified federated AI framework that jointly achieves privacy, fairness, and accuracy in credit scoring. The system enables collaboration across 300 institutions without sharing raw data, combining three components: Dirichlet Process Gaussian Mixture Model (DP-GMM)-based differential privacy, Exponentiated Gradient fairness constraints, and 8-bit model quantization. Evaluated from small-scale (German Credit, n = 1,000) to production-scale (LendingClub, n = 500,000), the framework achieves 96.94% accuracy with a 0.069% demographic parity gap, meeting stringent regulatory thresholds. Parameter sweeps quantify privacy–fairness trade-offs and are supported by theoretical guarantees, providing actionable guidance for deployment. Key contributions include the production-ready integration of privacy, fairness, and federated learning with formal guarantees; multi-dataset scalability across heterogeneous institutions; an 8× reduction in communication via quantization; and a compliance blueprint addressing the General Data Protection Regulation (GDPR), the California Consumer Privacy Act (CCPA), and fair lending standards. Results position federated AI as a practical and responsible solution for credit decisioning, delivering collaborative intelligence with strong privacy protection, algorithmic fairness, and regulatory compliance.