  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.
