ABSTRACT
The Bank Loan Approval Repayment Prediction System has been formulated to help financial institutions such as banks to predict the odds of loan repayment by analyzing historical loan data and making informed decisions. The system employs machine learning (ML) algorithms to analyze various parameters, such as credit score, income, credit status, and loan amount, to make accurate predictions. By analyzing this data, the system comes up with a comprehensive risk assessment for the financial institutions that assists in making informed loan giving decision making. This system could prove to be a vital tool for banks and other institutions carrying forward financial decision making in reducing risks and increasing profits alongside ensuring that loans are received by the re-payers well within their limits of repayment. ML algorithms like Random Forest, Decision Tree, XGBoost, and Logistic Regression were put into use for prediction. Results show that Random Forest is the most accurate amongst all the algorithms used for prediction with an accuracy of 95%.
