Organization of a modern credit risk analysis and control system for a commercial bank

the article examines the transformation of the credit risk analysis and control system in a commercial bank under the influence of various factors of modern conditions. The relevance of the work is due to the need to move from lagging traditional risk management methods to proactive, predictive approaches based on big data analysis. The purpose of the article is to develop the concept of a modern credit risk analysis system that integrates advanced artificial intelligence technologies at all stages of the credit lifecycle. The scientific novelty lies in the proposal of a comprehensive architecture for a hybrid intelligent system combining predictive analytics and generative models to automate decision-making processes and improve their accuracy. The conducted research demonstrates that the organization of a modern credit risk analysis and control system in a commercial bank is inextricably linked with the integration of advanced artificial intelligence technologies. The architecture of a hybrid system proposed in the article, combining predictive machine learning analytics and the operational capabilities of generative AI, allows us to move from reactive to proactive risk management. The greatest synergistic effect is achieved with the end-to-end application of AI at all stages of the credit lifecycle: from scoring using alternative data to automating monitoring and collection using generative AI.

credit risk, artificial intelligence, machine learning, data analysis, financial risks, banking, risk management