Affiliations: [a] LRIA/Computer Science Department, University of Sciences and Technology Houari Boumediene, USTHB - BP 32 El-Alia, Beb-Ezzoaur, Algiers, Algeria
| [b] Department of Accounting, Faculty of Economics and Administrative Sciences, The Hashemite University, Zarqa, Jordan
Correspondence:
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Corresponding author: Dalila Boughaci, LRIA/Computer Science Department, University of Sciences and Technology Houari Boumediene, USTHB - BP 32 El-Alia, Beb-Ezzoaur, 16111, Algiers, Algeria. E-mails: dalila_info@yahoo.fr and dboughaci@usthb.dz.
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.
