THE ROLE OF CREDIT SCORING SYSTEMS IN ENHANCING BANK CREDIT PORTFOLIO QUALITY

Abstract

This scientific article explores the role of credit scoring systems in improving the quality and performance of bank credit portfolios. The study analyzes traditional and modern scoring methods, including statistical models, machine learning algorithms, and big data applications, in assessing borrower creditworthiness. It highlights how effective credit scoring facilitates risk-based decision-making, reduces the likelihood of non-performing loans, and optimizes portfolio allocation. The research also emphasizes the importance of integrating credit scoring systems into bank risk management frameworks to enhance operational efficiency and financial stability.