Predicting Bank Loan Default with Extreme Gradient Boosting
This addresses the critical issue of loan default prediction for banks and financial institutions to reduce bad loans, but it is incremental as it uses an existing method on new data.
The paper tackled the problem of predicting bank loan defaults by applying the XGBoost algorithm to loan application and demographic data, achieving results evaluated with metrics like accuracy, recall, precision, F1-score, and ROC area.
Loan default prediction is one of the most important and critical problems faced by banks and other financial institutions as it has a huge effect on profit. Although many traditional methods exist for mining information about a loan application, most of these methods seem to be under-performing as there have been reported increases in the number of bad loans. In this paper, we use an Extreme Gradient Boosting algorithm called XGBoost for loan default prediction. The prediction is based on a loan data from a leading bank taking into consideration data sets from both the loan application and the demographic of the applicant. We also present important evaluation metrics such as Accuracy, Recall, precision, F1-Score and ROC area of the analysis. This paper provides an effective basis for loan credit approval in order to identify risky customers from a large number of loan applications using predictive modeling.