Using Bayesian Analysis Assessing Individual Consumers Loan Repayment Capacity: A Case Study at BIDV South Sai Gon Branch

Abstract

The research objective of the article is to assess the loan repayment capacity of individual customers at Joint Stock Commercial Bank for Investment and Development of Vietnam (BIDV) - Saigon South Branch. A data sample of 1051 individual customers with outstanding credit balances at BIDV - South Saigon Branch, was randomly selected from customers who have a credit relationship with the bank in the period 2010 - 2021 and used the Bayesian statistical method to evaluate the influence of a group of factors related to customer characteristics and a group of factors related to loan characteristics affecting loan repayment capacity. The research results show that customers' age, loan term, loan balance, and collateral have a negative effect on loan repayment capacity. Borrower's income, loan interest rate has a positive effect on repayment capacity. In addition, with the dominant internal advantage of the Bayesian method, the probability of the above effects ranges from 91.13% to 99.88%.