Predicting Bank Loan Default with Extreme Gradient Boosting

TL;DR

This paper demonstrates the effectiveness of the XGBoost algorithm in predicting bank loan defaults by analyzing application and demographic data, providing valuable metrics for credit risk assessment.

Contribution

It introduces the application of XGBoost for loan default prediction using combined application and demographic data, enhancing predictive accuracy over traditional methods.

Findings

XGBoost achieved high accuracy, recall, and F1-score in predicting defaults.

The model effectively identifies risky customers from large datasets.

Evaluation metrics confirm the model's robustness and practical utility.

Abstract

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.