Performance of a Loan Repayment Status Model Using Machine Learning

Due to Islamic financial management, Islamic cooperative plays an important role in providing financial credit to people in the southernmost of Thailand. The current study aimed to investigate the model for predicting loan repayment status of As- Siddeek Islamic Cooperative Limited in Songkhla Province. Three feature selection techniques were used to examine the association between factors and loan repayment status including Information Gain, Gini Index, and Chi-Squared test. Machine Learning Algorithms namely Logistic Regression, Decision Tree, and Random Forest were used to fit the model. The results showed that the similar important factors from those three techniques were cooperative branch, loan objective, member age, and previous loan amount. The accuracy of prediction models for Decision Tree, Logistic Regression, and Random Forest were 63.1%, 60.9%, and 60.0%, respectively.