A class of categorization methods for credit scoring models

Diego M.B. Silva, Gustavo H.A. Pereira and Tiago M. Magalhães

European Journal of Operational Research, 2022, vol. 296, issue 1, 323-331

Abstract: Credit scoring models are usually developed using logistic regression. For several reasons, professionals of this area frequently categorize the quantitative covariates before using them in the model. In this work, we introduce a class of methods for covariate categorization in regression models for binary response variables. Applications to real data and a Monte Carlo simulation study suggest that one of the methods of this class has a better predictive performance and a smaller computational cost than other methods available in the literature.

Keywords: Risk analysis; Covariate categorization; Credit scoring models; Discretization; Logistic regression (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0377221721003593
Full text for ScienceDirect subscribers only

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:eee:ejores:v:296:y:2022:i:1:p:323-331

DOI: 10.1016/j.ejor.2021.04.029

Access Statistics for this article

European Journal of Operational Research is currently edited by Roman Slowinski, Jesus Artalejo, Jean-Charles. Billaut, Robert Dyson and Lorenzo Peccati

More articles in European Journal of Operational Research from Elsevier
Bibliographic data for series maintained by Catherine Liu ().