Abstract
Country X, a developing country in Latin America, has a large number of citizens who are late in repaying their loans. Customers who are late on their loans are very likely to default, and companies are unable to recover their manufacturing costs. This paper analyzes loan data for motorcycle sales in country X. Based on models using logistic regression, Random Forest, and XGBoost, we propose a credit risk model that can appropriately identify customers who are late in repaying their loans. In addition, logistic regression analysis was used to identify the characteristics of customers who are late in repaying their loans. Factors that influence customers who delay loan repayment are customers with low credit scores of financial institutions and customers who take out loans beyond their ability to repay, both for the rich and the poor. Factors influencing customers who did not delay loan repayment were customers' income stability and high income. The prediction accuracy of the Random Forest model was found to be the most accurate.
