Analyzing the results and performance of several machine learning algorithms for determining whether a loan applicant is a good or a bad credit.
Credit risk evaluation decisions are crucial for financial institutions due to high risks associated with inappropriate credit decisions. It is an even more important task today as financial institutions have been experiencing serious competition. Credit scoring has gained attention as the credit industry can benefit from improving cash flow, insuring credit collections and reducing possible risks. Hence, many different useful techniques, known as the credit scoring models, have been developed by the banks and researchers in order to solve the problems involved during the evaluation process.
The objective of credit scoring models is to assign credit applicants to either a “good credit” group that is likely to repay financial obligation or a “bad credit” group who has high possibility of defaulting on the financial obligation. Therefore, credit scoring problems are basically in the scope of the more general and widely discussed classification problems.
The German Credit data is used for the analysis. The dataset consists of observations on 21 variables for 1000 past applicants credit. Each applicant was rated as “good credit”(700 cases) / “bad credit” (300 cases). The data set is available at ftp.ics.uci.edu/pub/machine-learning-databases/statlog/.
New applicants for credit can also be evaluated on these 21 "predictor" variables. We want to develop a credit scoring rule that can be used to determine if a new applicant is a good credit risk or a bad credit risk, based on values for one or more of the predictor variables. Training data is used to build the model and testing data is used to test the accuracy of the model.