Application of stochastic analysis methods in assessing the credit risk of banking institutions

Abstract

The current economic instability and volatility of financial markets have increased the need to use effective methods for quantifying credit risk in banking institutions. Traditional approaches to credit risk management cannot always adequately take into account the uncertainties and random deviations that affect the financial performance of companies and the likelihood of borrowers defaulting. This study considered the possibility of using stochastic analysis methods to improve the assessment of credit risks in the banking sector. The study used stochastic differential equations and the Black-Coles model to estimate changes in the value of borrowers' assets over time. The stochastic nature of financial indicators was modeled using Ito processes, which made it possible to take into account random fluctuations in the external economic environment. Monte Carlo methods were used to assess the probability of default, which made it possible to conduct a quantitative risk analysis under various scenarios. The analysis showed that the use of stochastic models can significantly improve the accuracy of forecasts compared to traditional deterministic methods. The models revealed a higher level of risk for a number of borrowers, which made it possible for banking institutions to adjust their loan portfolios and improve protection against potential losses. Monte Carlo algorithms have also demonstrated their effectiveness in assessing the probabilistic nature of credit events. The use of stochastic analysis methods in credit risk management can significantly improve the predictive reliability and flexibility of the assessment. This is especially important in an uncertain economic environment. In the future, it is planned to introduce these approaches into the operational processes of large banks to improve the quality of risk management.