Counterparty Risk Inspection Using Machine Learning Techniques

Lending money to those in need is a significant activity of the banking industry. The bank collects the interest paid by the principal borrowers in exchange for the principal borrowed from the depositor. ("Credit risk analysis using machine learning classifiers ...") Credit risk analysis is turning into a significant field in monetary danger for the executives. The interest payments on the loan are used by the lender to mitigate the risk of losses caused by the borrower's failure to make principal and interest payments. ("Purpose of Credit Risk Analysis - Overview, How It Works ...") In this case, the lender is exposed to the risk of loss due to the inability of the borrower. Lenders experience cash flow interruptions when borrowers default on their obligations. Lenders can perform a credit risk analysis to determine how easily the borrower will be able to meet its obligations to cushion itself from losses and reduce the severity of losses. Borrowers with an elevated level of credit risk are charged a higher interest rate on loans as compensation for the risk of default. A real-life data set for consumer lending is used in this paper to compare the performances of different classification algorithms. Several types of classifiers (logistic regression, random forest, decision tree, svm, xgbm, lgbm, etc) are evaluated and their performance compared.