Factors Relating to the Non-Income Debt Amounts of Debtors as Controlled by A Bank’s the Business Office of Entrepreneurial Debtors in Nakhon Ratchasima

Abstract

             The study of the group of small and medium-sized enterprise debtors: a case study of the office for business banking customer in Nakhon Ratchasima Province; Aim to 1. study the level of factors affecting the amount of non-performing loans have and 2) To create a model for screening and categorizing customer groups based on factors studied that are related to the amount of non-performing loans of customers under the care of the office for business banking customer in Nakhon Ratchasima province. In this study, the secondary data were collected from the database of debtors of the office for business banking customer in Nakhon Ratchasima, which were both quantitative and qualitative data, especially the debtors currently under the care of the team.

            The results showed that 539 entrepreneurs, most of them were normal debtors at 79.41%,
a business group with a juristic person at 97.59%, with a bachelor's degree or higher at 78.29%, with over 10 years of experience at  59.37%, with a small business at 46.57%, without reserve funds at 73.65%, without competitors at  75.70%, were former customers of the bank at 82.75%, were the type B industry at 59.00%, located in the city at 91.65%, were classified as normal risk group at 41.56%. From the coefficient from the factor scores obtained from the regression analysis. The regression equation of each factor can be written as there are 2 factors or 2 factors affecting the amount of non-performing loans with unequal variables. Both elements explain the variance of all variables as 16.283 and 10.987 respectively. From multiple regression analysis, using Enter method, it is found that the initial variable that is related to the amount of non-performing loans (NPL) were the size of the business, industrial conditions organized by banks,  gross profit plus depreciation (%), net profit (%), debt collection period (days), and stable growth with statistically significant. The predominant variable with the best prediction power allows these 6 variables to explain the variance in the amount of non-performing loans (NPL) of the customers under the supervision of the office for business banking customer in Nakhon Ratchasima province at 26.9%