Analysis of Chi-square Automatic Interaction Detection (CHAID) for Segmentation and Customer Targeting to Minimize Non-performing Loan

Abstract

Banking plays an important role in the economy because it can increase growth and development, especially in the economic sector. Bank is one of the financial institutions or companies engaged in finance that carry out various kinds of services, such as providing loans, distributing currencies, controlling currencies, as a place to store valuable objects, finance companies, etc (Limbong et al. 2010). Credit is the most important thing in bank activities, considering that the largest income of a bank is obtained from the credit sector. CHAID analysis is conducted to identify potential customer segments based on the variables that have the most significant influence to credit status. SMOTE is conducted for managing unbalanced data with 600% oversampling and 117% undersampling. Based on CHAID analysis, the characteristics that characterize the variety of credit status were the loan term, the latest education, gender, age, and loan amount with 5% significant level. CHAID analysis produced 14 segments based on the independent variable associated with the response variable which in this case the response variable is credit status with a classification accuracy of 67.9%.