Research On Measurement Of Credit Operational Risk

As an important part of commercial banks' risk management,credit operational risk management has always been one of the central tasks of commercial banks,since it is not only related to the utilization of commercial bank credit funds,but also has an important influence on the bank's own development.In particular,a series of cases of bank losses caused by operational risk in recent years shocked the entire financial community and posed a severe challenge to the management of operational risk of commercial banks.Therefore,how to strengthen the control of operational risk,measure and evaluate all aspects of the credit operation process,and put forward suggestions on bank credit operational risk management are important to the healthy and sustainable development of commercial banks.As an important financial institution in Fuzhou,Fuzhou branch of Postal Savings Bank of China(PSBC FUZHOU)has seen rapid growth in various financial businesses in recent years,and the credit business has become an important pillar.However,the pressure on banking risk management has also gradually increased.Therefore,how to strengthen credit operational risk management,improve the quality of credit business and reduce the risk of banks has been the realistic demand of PSBC FUZHOU.In this paper,we focus on the measurement of personal credit operational risk in PSBC FUZHOU.First of all,according to the bank's characteristics,this paper analyzes and summarizes the personal credit operation process,and establishes a credit operation process model.Secondly,through the Bayesian network,the credit operation process model is formalized,and using static Bayesian network inference techniques to measure the possibilities of credit defaults caused by the credit operations.Thirdly,according to the dynamic characteristics of credit operation,a dynamic model of credit operation process is established by using dynamic Bayesian network,which the measurement is based on.Finally,using the well-known software and data in the industry to validate the method proposed in this paper,and analyzing the application scenarios of the two measurement methods and giving suggestions on improvement of credit risk management.