Application of the VNS heuristic for feature selection in credit scoring problems

Abstract

Credit scoring plays a major role for financial institutions when making credit-granting decisions. In this context, machine learning techniques have been used to develop credit scoring models, as they seek to recognize existing patterns in databases containing the credit history of borrowers to infer potential defaulters. However, these databases often contain a large number of variables, some of which can be noisy, leading to imprecise results and loss of performance/accuracy. In the present work, a feature selection technique is proposed based on a variable neighborhood concept, so-called VNS. The applicability of the method is assessed in conjunction with seven of the main techniques used to make default prediction in credit analysis problems. Its performance was compared to the feature selection obtained by the well-known PCA statistical method. The results indicate superior performance of the VNS in most of the applied tests, suggesting the robustness of the method.

Keywords

• Credit scoring
• Machine learning
• Feature selection