Application of Bayesian Network Analysis in risk management of banks (Case Study: Saderat Bank of Iran)

Nowadays, banks play a crucial role as the most important financial institution in the money market, contributing significantly to the growth and development of countries' economies. Banks are considered the primary sources of finance for real sectors of the economy (various production industries, agriculture, and service-providing companies), and they strive to equip businesses and individuals, allocate resources efficiently, and provide diverse services to customers with the motivation of generating income and earning profits. In this regard, due to the diverse nature of banking operations and the capital constraints of banks, the banking industry faces various types of risks. Therefore, the aim of the present study is to apply Bayesian Network Analysis in managing the risks of Saderat Bank of Iran. To this aim, after formulating the questionnaire and collecting research data, the researchers proceeded to estimate the relationships between liquidity, credit, and operational risks using the Bayesian network analysis approach. The research results indicate that systemic risk was identified as the most significant factor in operational risk, liquidity inventory was identified as the most significant factor in liquidity risk, and finally, the credit scoring index was identified as the most significant factor in credit risk. Ultimately, based on the VIF index, the most significant type of risk in Saderat Bank of Iran was recognized as operational risk with a share of 53 percent.