DEVELOPMENT OF ASSESSMENT TOOLS IN THE BANK'S CREDIT MANAGEMENT SYSTEM

Abstract

The issue of credit risk management in modern banking is increasingly significant, as it affects both the stability of financial institutions and the broader economy. Rapidly changing market conditions and emerging financial technologies require banks to adopt flexible, multifaceted, and analytically grounded approaches to risk identification and mitigation. The paper emphasizes the importance of combining quantitative and qualitative assessment methods with digital technologies for automated data collection and rapid response. The study examines practices for assessing borrower solvency, highlighting financial, behavioral, and contextual indicators. It also considers credit portfolio monitoring through dynamic indicators, stress testing, and scenario analysis, as well as adaptive forecasting models that adjust to evolving market conditions. The research contributes by integrating adaptive analytical algorithms with digital platforms to create a more responsive risk management system. Practical applications include enhancing the efficiency of risk departments, detecting unfavorable credit trends early, and supporting preventive measures. The article also suggests directions for future research, such as real-time risk monitoring and improved predictive models. This analysis is intended for banking professionals, credit analysts, regulators, and researchers seeking to strengthen credit risk management and improve the stability and sustainability of the banking sector. Additionally, the paper highlights the growing role of big data analytics in improving the accuracy of credit risk evaluations. It demonstrates how integrating non-traditional data sources can enhance borrower profiling and predictive accuracy. The study also outlines the importance of regulatory compliance and supervisory expectations in shaping modern risk management frameworks. Furthermore, it emphasizes the need for continuous staff training to ensure the effective use of digital tools and analytical models. The paper argues that collaboration between financial institutions and fintech companies can accelerate innovation in credit risk assessment.