Privacy-Preserving Artificial Intelligence for Financial and Organizational Risk Analytics Using Federated Learning and Differential Privacy

Abstract

This study investigates the integration of federated learning (FL) and formal differential privacy (DP) to resolve the fundamental trade-off between institutional data sovereignty and collaborative risk intelligence across banking, financial technology (FinTech), and enterprise cybersecurity domains. Utilizing a harmonized cross-silo dataset of 2,410,000 verified observations partitioned across 25 institutional nodes, the research systematically evaluated centralized non-private learning (CNPL), isolated local institutional learning (LIL), conventional federated learning (FL), and differentially private federated learning (DP-FL) across transactional fraud detection, credit-default forecasting, and enterprise intrusion identification. Empirical findings revealed that conventional FL achieved near-parity with centralized baselines, preserving over 97% of optimal predictive utility with a financial fraud F1-score of 0.847 and cybersecurity F1-score of 0.871, compared to isolated local learning baselines of 0.674 and 0.651, respectively. Enforcing formal differential privacy ($\epsilon = 4.0, \delta = 10^{-5}$) incurred non-linear utility penalties concentrated primarily on rare-event detection, yielding F1-scores of 0.759 for fraud and 0.775 for cyber intrusions, with false-negative rates rising to 25.90%. Moderation and regression analyses demonstrated that institutional volume ($\beta = 0.294, p < 0.001$) significantly buffered privacy-induced degradation, while severe class imbalance ($IR < 0.02$) compounded performance losses. Mediation modeling confirmed that stochastic gradient variance accounted for 40.8% of privacy-induced utility erosion. Adversarial evaluations established that conventional FL remained vulnerable to membership inference (71.40% accuracy; AUC = 0.785) and gradient reconstruction (cosine similarity = 0.842), whereas DP-FL effectively neutralized leakage to random chance (51.60% accuracy; AUC = 0.521; similarity = 0.084). Multi-objective Pareto optimization identified moderate privacy expenditures ($\epsilon = 4.0$) as the optimal configuration, achieving the highest Composite Utility-Privacy Index (CUPI = 0.844) while capping training extension at 44.5 wall-clock hours and 52.86 GB cumulative network egress. These findings validate privacy-preserving collaborative artificial intelligence as an empirically reliable, computationally viable architecture for institutional risk mitigation.