Logistic Model of Credit Risk during the COVID-19 Pandemic

Authors: Bin Zhao* and Jinming Cao

DOI: 10.23880/ijbp-16000193

Volume 6, Issue 1

Abstract

In this paper, the Markov Chain Monte Carlo (MCMC) method is used to estimate the parameters of Logistic distribution, and this method is used to classify the credit risk levels of bank customers. OpenBUGS is bayesian analysis software based on MCMC method. This paper uses OpenBUGS software to give the bayesian estimation of the parameters of binomial logistic regression model and its corresponding confidence interval. The data used in this paper includes the values of 20 variables that may be related to the overdue credit of 1000 customers. First, the “Boruta” method is adopted to screen the quantitative indicators that have a significant impact on the overdue risk, and then the optimal segmentation method is used for subsection processing. Next, we filter three most useful qualitative variable According to the WOE and IV value, and treated as one hot variable. Finally, 10 variables were selected, and OpenBU-GS has been used to estimat the parameters of all variables. We can draw the following conclusions from the results: customer’s credit history and existing state of the checking account have the greatest impact on a customer’s delinquent risk, the bank should pay more attention to these two aspects when evaluating the risk level of the customer during the COVID-19 pandemic.

Keywords: Data Analysis; Monte Carlo Model; OpenBUGS; Overdue Risk

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