Title: Ensemble margin resampling approach for a cost sensitive credit scoring problem

Authors: Meryem Saidi; Nesma Settouti; Mostafa El Habib Daho; Mohammed El Amine Bechar

Addresses: Higher School of Management of Tlemcen, Biomedical Engineering Laboratory GBM, University of Tlemcen, Algeria ' Biomedical Engineering Laboratory GBM, University of Tlemcen, Algeria ' Biomedical Engineering Laboratory GBM, University of Tlemcen, Algeria ' Biomedical Engineering Laboratory GBM, University of Tlemcen, Algeria

Abstract: In the past few years, a growing demand for credit compel banking institution to contemplate machine learning techniques as an answer to obtain decisions in a reduced time. Different decision support systems were used to detect loans defaulters from good loans. Despite good results obtained by these systems, they still face some problems such as imbalanced class and imbalanced misclassification cost problems. In this work, we propose a cost sensitive credit scoring system, based on a two-step process. The first is a resampling step which handles the imbalance data problem followed by a cost sensitive classification step that recognises potential insolvent loans red in order to reduce financial loss. A resampling algorithm called ensemble margin for imbalanced instance (EM2I) is suggested to manage imbalanced datasets in cost sensitive learning. We compare our algorithm with other techniques from the state of the art and experimental results on German credit dataset demonstrate that EM2I leads to a significant reduction of the misclassification cost.

Keywords: cost sensitive learning; imbalanced problem; ensemble margin; credit scoring.

DOI: 10.1504/IJCEE.2021.118475

International Journal of Computational Economics and Econometrics, 2021 Vol.11 No.4, pp.323 - 348

Received: 13 Sep 2019
Accepted: 20 Feb 2020
Published online: 27 Oct 2021 *

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