Feature selection in credit risk modeling: an international evidence

This paper aims to discover a suitable combination of contemporary feature selection techniques and robust prediction classifiers. As such, to examine the impact of the feature selection method on classifier performance, we use two Chinese and three other real-world credit scoring datasets. The utilized feature selection methods are the least absolute shrinkage and selection operator (LASSO), multivariate adaptive regression splines (MARS). In contrast, the examined classifiers are the classification and regression trees (CART), logistic regression (LR), artificial neural network (ANN), and support vector machines (SVM). Empirical findings confirm that LASSO’s feature selection method, followed by robust classifier SVM, demonstrates remarkable improvement and outperforms other competitive classifiers. Moreover, ANN also offers improved accuracy with feature selection methods; LR only can improve classification efficiency through performing feature selection via LASSO. Nonetheless, CART does not provide any indication of improvement in any combination. The proposed credit scoring modeling strategy may use to develop policy, progressive ideas, operational guidelines for effective credit risk management of lending, and other financial institutions. The finding of this study has practical value, as to date, there is no consensus about the combination of feature selection method and prediction classifiers

Customer Churn Prediction

Churned customers identification plays an essential role for the functioning and growth of any business. Identification of churned customers can help the business to know the reasons for the churn and they can plan their market strategies accordingly to enhance the growth of a business. This research is aimed at developing a machine learning model that can precisely predict the churned customers from the total customers of a Credit Union financial institution. A quantitative and deductive research strategies are employed to build a supervised machine learning model that addresses the class imbalance problem handled feature selection and efficiently predict the customer churn. The overall accuracy of the model, Receiver Operating Characteristic curve and Area Under the Receiver Operating Characteristic Curve is used as the evaluation metrics for this research to identify the best classifier. A comparative study on the most popular supervised machine learning methods – Logistic Regression, Random Forest, Support Vector Machine (SVM) and Neural Network were applied to customer churning prediction in a CU context. In the first phase of our experiments, the various feature selection techniques were studied. In the second phase of our study, all models were applied on the imbalance dataset and results were evaluated. SMOTE technique is used to balance the data and then the same models were applied on the balanced dataset and results were evaluated and compared. The best over-all classifier was Random Forest with accuracy almost 97%, precision 91% and recall as 98%