THE ROLE OF ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING IN CREDIT RISK FORECASTING AND MANAGEMENT

Abstract

The rapid development of Artificial Intelligence and Machine Learning technologies has fundamentally reshaped credit risk forecasting and management within modern financial systems. Traditional credit risk assessment methods, largely dependent on statistical modeling and expert judgment, have proven insufficient in addressing the growing complexity, scale, and velocity of contemporary financial data. This article examines the economic and operational significance of AI and ML in credit risk management, focusing on their capacity to enhance predictive accuracy, improve operational efficiency, and support data-driven decision-making. The study highlights key application areas, including credit scoring, default prediction, fraud detection, operational risk management, and regulatory compliance. Particular attention is given to the role of advanced machine learning algorithms, such as ensemble methods and deep learning models, in addressing non-linearity, data imbalance, and alternative data integration. The article also discusses critical challenges associated with AI adoption, including interpretability, data privacy, algorithmic bias, and regulatory constraints. Finally, emerging trends and future research directions are explored, emphasizing explainable artificial intelligence, generative models, blockchain integration, and early warning systems. The findings demonstrate that while AI and ML offer substantial benefits for credit risk management, their sustainable and ethical implementation requires balanced governance, transparency, and robust regulatory alignment.