Advancing Credit Risk Management in Open Banking with Enhanced Federated Averaging Algorithm
Received: 16 January 2025 | Revised: 9 February 2025 and 12 February 2025 | Accepted: 14 February 2025 | Online: 7 April 2025
Corresponding author: Adil Oualid
Abstract
This paper addresses the pressing challenge of credit risk management in contemporary banking by integrating Federated Learning (FL) and Open Banking, employing an Enhanced Federated Averaging (FedEn) algorithm. Against Open Banking's transformative impact on financial services, the current research responds to the critical need for improved credit risk assessment in Non-Independently and Identically Distributed (Non- IID) data landscapes. The integration of FL and Open Banking is showcased by applying the Federated Averaging (FedAvg) algorithm, which offers a novel framework for credit risk management. The proposed methodology, grounded in theoretical foundations and validated through practical case studies, underscores the effectiveness of this integrated approach. The main contribution of the present work lies in demonstrating that the synergy of FL and Open Banking, facilitated by FedAvg, significantly enhances credit risk prediction accuracy while ensuring robust data privacy. Despite data security and regulatory compliance challenges, this integration presents a promising direction for financial institutions. The current research contributes through a comprehensive understanding of these technologies' confluence, providing valuable insights for banks, policymakers, and researchers navigating the dynamic landscape of credit risk management in the era of Open Banking.
Keywords:
federated learning, credit risk management, open banking, privacy preservation, non-IID data, model aggregationDownloads
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Copyright (c) 2025 Adil Oualid, Youssef Qasmaoui, Youssef Balouki, Bouzgarne Itri, Lahcen Moumoun
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