Impact of Machine Learning–Based Credit Risk Modeling on Regulatory Compliance in Banking

Abstract

The increasing complexity of banking activities and regulatory requirements has intensified the need for more advanced approaches to credit risk modeling. Traditional credit risk assessment models, while widely used, often face limitations in capturing non-linear relationships and rapidly changing risk patterns. In response to these challenges, machine learning–based credit risk modeling has emerged as a promising tool to enhance risk identification and decision-making processes within banking institutions. This study conceptually examines the impact of machine learning–based credit risk modeling on regulatory compliance in the banking sector. Drawing on recent literature in banking risk management, financial regulation, and artificial intelligence, the paper analyzes how data-driven risk models can support compliance with prudential regulations, particularly in relation to capital adequacy, credit quality assessment, and supervisory expectations. The discussion highlights the potential benefits of machine learning techniques in improving model accuracy, risk sensitivity, and regulatory reporting, while also addressing key challenges such as model transparency, governance, and regulatory acceptance. The findings contribute to the growing discourse on the integration of advanced analytics into banking risk frameworks and provide insights for regulators and practitioners seeking to balance innovation with regulatory compliance.