Logistic regression analysis in personal loan bankruptcy

Abstract

Personal loan bankruptcy is defined as a person who had been declared as a bankrupt due to failure to repay their personal loan. Personal loan bankruptcy is a serious problem that will affect an individual financial stability. This study focused on personal loan bankruptcy in Kedah only. For this study, the concept of logistic regression model was applied in order to determine the most predictive factor of personal loan bankruptcy problem. There are four main factors considered in this analysis, that is age, gender, race and job profession. Logistic regression emphasizes the nature of relationship between the dependent variable and another independent variables. The outcomes are predicted by using odd ratio. The odds ratio interpretation of the estimated regression coefficients makes the logistic regression model especially attractive for modelling and interpreting the studies. In this study, the data consist of 576 person declared as bankrupt due to personal loan bankruptcy and non-personal loan bankruptcy. The response variable is binary, denoting whether a person is personal loan bankruptcy or non-personal loan bankruptcy. The data are analysed by using SPSS 22. Based on the analysis, gender, race and job profession are the significant factors that lead to personal loan bankruptcy.