graduate student
The article considers banking activity as a multi-level system of financial services for households, corporate and public entities, which determines a complex and interconnected risk profile. Based on the analysis of theoretical and regulatory sources, the classification of banking risks is summarized: credit, market, non-financial and liquidity risks, as well as their transmission links that increase vulnerability in stress modes. Special attention is paid to the core of credit risk modeling, including PD, LGD, and CCF/EAD parameters, as well as methods for their estimation (from interpretable statistical models to machine learning ensembles) and their integration with macroeconomic scenarios for calculating expected/unexpected losses and capital buffers. It is shown that improving the accuracy of forecasts through automated learning methods requires strengthening the procedures for explainability, sustainability, and risk management associated with model use.
banking activities, risk modeling, banking risks, development cycle, and financial services
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