Ensemble margin resampling approach for a cost sensitive credit scoring problem

Meryem Saidi, Nesma Settouti, Mostafa El Habib Daho and Mohammed El Amine Bechar

International Journal of Computational Economics and Econometrics, 2021, vol. 11, issue 4, 323-348

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. (search for similar items in EconPapers)
Date: 2021
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.inderscience.com/link.php?id=118475 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:ids:ijcome:v:11:y:2021:i:4:p:323-348

Access Statistics for this article

More articles in International Journal of Computational Economics and Econometrics from Inderscience Enterprises Ltd
Bibliographic data for series maintained by Sarah Parker ().