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 Provi...

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Bibliographic Details
Published in2022 19th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON) pp. 1 - 4
Main Authors Mueankoo, Anon, Eso, Mayuening, Musikasuwan, Salang
Format Conference Proceeding
LanguageEnglish
Published IEEE 24.05.2022
Subjects
Computational modeling
Decision Tree
Forestry
Indexes
Information technology
Loan Repayment Status
Logistic Regression
Machine Learning
Machine learning algorithms
Prediction Model
Predictive models
Random Forests
Telecommunications
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Summary: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.
DOI:10.1109/ECTI-CON54298.2022.9795570