Intellectual scoring and credit risk

Abstract

This article explores innovative approaches to improving credit scoring systems and managing credit risks in the banking sector. The proposed system utilizes machine learning methods to perform a comprehensive analysis of clients' financial history, market conditions, and macroeconomic indicators, thereby enabling a more accurate assessment of credit risk. The study includes a comparative analysis of various models, demonstrating their practical applicability and efficiency in a rapidly changing financial environment. Experimental results indicate that the application of the proposed algorithms significantly enhances forecasting accuracy and reduces financial losses. The research highlights the potential for implementing adaptive analytical tools that support prompt and well-founded managerial decision-making in banks.