An Approach to Loan Approval prediction Using Boosting Ensemble Learning

Now a days banks are receiving lakhs of loan applications daily. This is delay in approval of loan to the customer as humans need to verify each of them manually. So even after having enough resources to approve loans it is big problem for banks to approve applications in time. In order to resolve this problem, we are planning to build an ML application which can reduce the time required to approve a loan using ML based prediction model to approve the loan with minimal human intervention by filtering huge number of applications and forward very few applications for human verification. In this work used several popular machine learning models are Random Forest tree, Logistic Regression, Decision tree, Support Vector machine and XGB. The results demonstrated that XGB has provided higher accuracy in the presenting assessment of the models than all other models combined.