Modeling and identification of causal relationships between the main factors of credit risk in the banking system using the Dematel decision making technique
One of the consequences of financial intermediation activities in banks is credit risk, which is the oldest, largest and, at the same time, the most important banking risk. As the society is growing and developing, the amount of facilities and liquidity circulation in it increases, and the importance of credit health becomes more necessary. Therefore, evaluating and managing credit risk is a vital thing for banks and is also an important solution for implementing banking policies and business strategies. In addition, the existence of an evolving credit risk management framework indicates the financial prosperity of the banking system in general. It is also an important indicator for the stability and financial stability of each bank in particular.
In this research, the modeling and identification of causal relationships between the main factors of credit risk is done in order to predict the default repayment of customers by referring to credit experts and using the DEMATEL method. Structuring complex factors in the form of cause and effect groups is an important function of DEMATEL method in problem solving processes. Therefore, 22 variables describing credit risk are divided into the two categories of cause and effect.
The results show that the job status of the applicant has the most influence in the model. The variables of applicant's annual income, workplace ownership and marital status respectively have the next degrees of influence. The number of collaterals has the least influence in the model. The monthly repayment burden has the highest level of influence compared to other variables. Also, the variables of annual income and job status have the most interactions with the other studied variables.
Conclusions and policy implications:
Demographic and socio-economic indicators, along with financial and credit indicators, should be given more attention in credit bureau models, and different and comprehensive systems to measure credit should be linked to each other more quickly if they are to be used by banks and credit bureau institutions. Therefore, our findings allow providing an effective decision support system for banks in order to detect and reduce the rate of bad borrowers, thus reducing credit risks.
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