A Review of Customer Loan Eligibility Prediction Using Machine Learning Algorithms

In Loan status prediction is an effective tool for investment decisions in peer-to-peer (P2P) lending market. In P2P lending market, most borrowers full fill the repayment plan; however, some of them fail to pay back their loans. Therefore, a classification method can be utilized to discriminate such default borrowers. In this context, the aim of this dissertation is to propose an investment decision model in P2P lending market which consists of fully paid loans classified via the instance-based machine learning model. Customers who want any loan they apply for that loan. Company validates the customers eligibility for the loan. Company wants to automate the loan eligibility process. For validating such automatic process Gender, Marital Status Number of Dependents in their family, income of family members & finally credit score. To enhance their business in better way these types of facilities, may enhance business as well as customer satisfaction. We also used multiple Machine learning model to generate an investment portfolio based on non-default loans that are predicted to yield high returns. A comparison has been done between the actual and predicted expenses of the prediction premium and eventually, a graph has been plotted on this basis which will enlighten us to choose the best-suited algorithm. The selected algorithm will be applied for our proposed work i.e., Loan Prediction. For prediction, correctness has been measured by the Coefficient of determination. Gradient Boosting Classifier gives the best result in terms of Accuracy i.e. 0.9125 which can be used in its best possible way for the correct prediction of the Loan Prediction Guarantee for companies as well as Customers.