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dc.contributor.authorDahooie, Jalil Heidary
dc.contributor.authorRazavi Hajiagha, Seyed Hossein
dc.contributor.authorFarazmehr, Shima
dc.contributor.authorZavadskas, Edmundas Kazimieras
dc.contributor.authorAntuchevičienė, Jurgita
dc.date.accessioned2023-09-18T20:37:47Z
dc.date.available2023-09-18T20:37:47Z
dc.date.issued2021
dc.identifier.issn0305-0548
dc.identifier.urihttps://etalpykla.vilniustech.lt/handle/123456789/151469
dc.description.abstractCredit risk evaluation is always the most important factor in determining Customers’ credit status in financial institutions. Multi-Attribute Decision-Making (MADM) methods have been widely used in this field. But most of the studies neglect the undeniable impact of time and changes of the credit assessment criteria, their importance and evaluation data over time. On the other hand, developed Dynamic MADM (DMADM) methods often used subjective weighting methods and then applied some aggregation operators to rank alternatives. This paper proposes a new combination of Data Envelopment Analysis (DEA) as a powerful objective weighting method with DMADM as a novel dynamic decision-making method for credit performance evaluation. For this aim, the credit performance criteria were extracted using literature review and experts’ views. Criteria weights were calculated with dynamic DEA common set of weights approach. Then, the applicants are prioritized using five Grey MADM methods (including SAW-G, VIKOR-G, TOPSIS-G, ARAS-G and COPRAS-G). Finally, a new method called Correlation Coefficient and Standard Deviation (CCSD) was used to determine the final aggregated rank. This novel method is applied in order to credit ratings of the clients in the Beekeeping Industry Development Funding Institute IRAN The results indicate that the proposed MADM method, while eliminating the limitations of previous methods, has been able to maintain robustness. Also, the results are highly correlated with the results of previous methods.eng
dc.formatPDF
dc.format.extentp. 1-18
dc.format.mediumtekstas / txt
dc.language.isoeng
dc.relation.isreferencedbyScopus
dc.relation.isreferencedbyScience Citation Index Expanded (Web of Science)
dc.relation.isreferencedbySocial Sciences Citation Index (Web of Science)
dc.rightsLaisvai prieinamas internete
dc.source.urihttps://www.sciencedirect.com/science/article/pii/S0305054821000150?via%3Dihub
dc.source.urihttps://doi.org/10.1016/j.cor.2021.105223
dc.source.urihttps://talpykla.elaba.lt/elaba-fedora/objects/elaba:83005860/datastreams/MAIN/content
dc.subjectN900 - Verslas ir vadyba / Business and administrative studies
dc.titleA novel dynamic credit risk evaluation method using data envelopment analysis with common weights and combination of multi-attribute decision-making methods
dc.typeStraipsnis Web of Science DB / Article in Web of Science DB
dcterms.references176
dc.type.pubtypeS1 - Straipsnis Web of Science DB / Web of Science DB article
dc.contributor.institutionUniversity of Tehran
dc.contributor.institutionKhatam University
dc.contributor.institutionVilniaus Gedimino technikos universitetas
dc.contributor.facultyStatybos fakultetas / Faculty of Civil Engineering
dc.subject.researchfieldT 007 - Informatikos inžinerija / Informatics engineering
dc.subject.researchfieldS 003 - Vadyba / Management
dc.subject.researchfieldS 004 - Ekonomika / Economics
dc.subject.studydirectionB04 - Informatikos inžinerija / Informatics engineering
dc.subject.vgtuprioritizedfieldsFM0101 - Fizinių, technologinių ir ekonominių procesų matematiniai modeliai / Mathematical models of physical, technological and economic processes
dc.subject.ltspecializationsL104 - Nauji gamybos procesai, medžiagos ir technologijos / New production processes, materials and technologies
dc.subject.enCredit risk evaluation
dc.subject.endynamic multi-attribute decision-making
dc.subject.endata envelopment analysis
dc.subject.enCCSD
dcterms.sourcetitleComputers and operations research
dc.description.volumevol. 129
dc.publisher.namePergamon-Elsevier
dc.publisher.cityOxford
dc.identifier.doi000630329100016
dc.identifier.doi10.1016/j.cor.2021.105223
dc.identifier.elaba83005860


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