A study on credit scoring modeling with different feature selection and machine learning approaches

A bit hurdle for financial institutions is to decide potential candidates to give a line of credit identifying the right people without any credit risk. For such a crucial decision, past demographic and financial data of debtors is important to build an automated artificial intelligence credit score prediction model based on machine learning classifier. In addition, for building robust and accurate machine learning models, important input predictors (debtor's information) must be selected. The present computational work focuses on building a credit scoring prediction model. A publicly available German credit data is incorporated in this study. An improvement in the credit scoring prediction has been shown with the use of different feature selection techniques (such as Information-gain, Gain-Ratio and Chi-Square) and machine learning classifiers (Bayesian, Naïve Bayes, Random Forest, Decision Tree (C5.0) and SVM (support Vector Machine)). Further, a comparative analysis is performed between different machine learning classifiers and between different feature selection techniques. Different evaluation metrics are considered for analyzing performance of the models (such as accuracy, F-measure, false positive rate, false negative rate and training time). After analysis, a best combination of machine learning classifier and feature selection technique are identified. In this study, a combination of random forest (RF) and Chi-Square (CS) is found good, among other combinations, with respect to good performance accuracy, F-measure and low false positive and false negative rates. However, training time for this particular combination was found to be slightly higher. Result of C5.0 with chi-square was comparable with the best one. This study provides an opportunity to financial institutions to build an automated model for better credit scoring.