A Prediction of Modernized Loan Approval System Based on Machine Learning Approach

A Predicting the probability of loan defaults is essential for financial institutes and banks, as a major part of their income is dependent on the interest & EMIs generated on the repayment of the loans issued by them to their customers. The application approved or not approved depends upon the historical data of the candidate by the system. Every day lots of people apply for the loan in the banking sector but the Bank would have limited funds. A Bank's profit and loss depend on the amount of the loansthat is whether the Client or customer is paying back the loan.Recovery of loans is the most important for the banking sector. Hence, having a model that could predict loan defaulters would be very beneficial for the financial institutes and banks for notifying them to approve a customer's loan or not. Such a model will evaluate their customer's data based on certain parameters and generate an accurate result based on that evaluation. The improvement process plays an important role in the banking sector. Data set was collected from the kaggle. The historical data of candidates was used to build a machine learning model using Ensemble learning with algorithms like naive bayes , decision tree and MLP. Using our methodology, we anticipate with a 90%accuracy.