A new variable selection method applied to credit scoring

Dalila Boughaci () and Abdullah A.K. Alkhawaldeh
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Dalila Boughaci: LRIA/Computer Science Department, University of Sciences and Technology Houari Boumediene, Postal: USTHB - BP 32 El-Alia, Beb-Ezzoaur, Algiers, Algeria
Abdullah A.K. Alkhawaldeh: Department of Accounting, Faculty of Economics and Administrative Sciences, The Hashemite University, Postal: Zarqa, Jordan

Algorithmic Finance, 2018, vol. 7, issue 1-2, 43-52

Abstract: Credit scoring (CS) is an important process in both banking and finance. Lenders or creditors have to use CS to predict the probability that a borrower will default or become delinquent. CS is usually based on variables related to the applicant such as: his age, his historical payments, his behavior, etc. This paper first proposes a new method for variable selection. The proposed method (VS-VNS) is based on the variable neighborhood search meta-heuristic. VS-VNS allows us to select a set of significant variables for the data classification task. The VS-VNS is combined then with a Bayesian network (BN) to build models for CS and select counterparties. Further, six search methods are studied for BN on different sets of variables. The different techniques and combinations are evaluated on some well-known financial datasets. The numerical results are promising and show the benefits of the new proposed approach (VS-VNS) for data classification and credit scoring.

Keywords: Credit scoring; variable selection; variable neighborhood search; search technique; Bayesian network; Hill climbing; tabu search; simulated annealing; TAN; classification (search for similar items in EconPapers)
JEL-codes: C00 (search for similar items in EconPapers)
Date: 2018
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Persistent link: https://EconPapers.repec.org/RePEc:ris:iosalg:0066

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