MODEL AND TOOLS OF AN AUTOMATED CREDIT RISK MANAGEMENT SYSTEM BASED ON ARTIFICIAL INTELLIGENCE

Abstract

In the context of the rapid digital transformation of the financial sector, the problem of effective credit risk management has become increasingly important. The growing volume of heterogeneous data, the high dynamics of customer behavior, and stricter regulatory requirements stimulate the search for intelligent automated solutions that can support informed and transparent financial decision-making. Artificial intelligence and machine learning offer fundamentally new capabilities for analyzing financial, behavioral, and transactional characteristics of borrowers, enabling the development of adaptive and explainable credit scoring models.

The article presents the concept and implementation of an automated credit risk management system based on AI methods. The proposed architecture integrates several interconnected modules: data collection and validation, calculation of risk indicators, machine-learning-based scoring, model interpretation, and analytical reporting. Special attention is paid to constructing feature sets relevant for financial risk assessment and to training ensemble models such as XGBoost and LightGBM. The system incorporates explainable AI techniques (SHAP-based interpretation) to ensure transparency and regulatory compliance in decision-making. Experimental evaluation demonstrates the advantages of the proposed approach over traditional statistical scoring models in terms of predictive accuracy, adaptability, and reduction of false-positive classifications.

The developed system can be integrated into the internal IT infrastructure of financial institutions without significant modifications to existing processes. It supports automated risk assessment of loan applications, model retraining on new data, monitoring of feature drift, and generation of detailed analytical reports for risk managers and auditors. The results confirm that AI-based credit risk management significantly improves the quality, speed, and objectivity of credit decisions, creating a solid foundation for building more resilient and data-driven financial ecosystems.