Banking applications of FCM models

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dc.contributor.author Hatwágner, Miklós F.
dc.contributor.author Vastag, Gyula
dc.contributor.author Niskanen, Vesa A.
dc.contributor.author Kóczy, László T.
dc.contributor.editor Cornejo, María Eugenia
dc.contributor.editor Kóczy, László T.
dc.contributor.editor Medina, Jesús
dc.contributor.editor De Barros Ruano, Antonio Eduardo
dc.date.accessioned 2020-05-04T12:24:01Z
dc.date.available 2020-05-04T12:24:01Z
dc.date.issued 2019
dc.identifier.citation Hatwágner , M F , Vastag , G , Niskanen , V A & Kóczy , L T 2019 , Banking applications of FCM models . in M E Cornejo , L T Kóczy , J Medina & A E De Barros Ruano (eds) , Trends in Mathematics and Computational Intelligence . Studies in Computational Intelligence , vol. 796 , Springer Verlag . https://doi.org/10.1007/978-3-030-00485-9_7
dc.identifier.other PURE: 133403674
dc.identifier.other PURE UUID: 4e3bb252-7dc7-4ae4-bcad-5e8084cbacf4
dc.identifier.other RIS: urn:E67225250E6AF8BB62AB2049F279B517
dc.identifier.other Scopus: 85054713528
dc.identifier.other WOS: 000684112300007
dc.identifier.uri http://hdl.handle.net/10138/314650
dc.description.abstract Fuzzy Cognitive Map (FCMs) is an appropriate tool to describe, qualitatively analyze or simulate the behavior of complex systems. FCMs are bipolar fuzzy graphs: their building blocks are the concepts and the arcs. Concepts represent the most important components of the system, the weighted arcs define the strength and direction of cause-effect relationships among them. FCMs are created by experts in several cases. Despite the best intention the models may contain subjective information even if it was created by multiple experts. An inaccurate model may lead to misleading results, therefore it should be further analyzed before usage. Our method is able to automatically modify the connection weights and to test the effect of these changes. This way the hidden behavior of the model and the most influencing concepts can be mapped. Using the results the experts may modify the original model in order to achieve their goal. In this paper the internal operation of a department of a bank is modeled by FCM. The authors show how the modification of the connection weights affect the operation of the institute. This way it is easier to understand the working of the bank, and the most threatening dangers of the system getting into an unstable (chaotic or cyclic state) can be identified and timely preparations become possible. © Springer Nature Switzerland AG 2019. en
dc.format.extent 12
dc.language.iso eng
dc.publisher Springer Verlag
dc.relation.ispartof Trends in Mathematics and Computational Intelligence
dc.relation.ispartofseries Studies in Computational Intelligence
dc.relation.isversionof 978-3-030-00484-2
dc.relation.isversionof 978-3-030-00485-9
dc.rights.uri info:eu-repo/semantics/openAccess
dc.subject Bacterial evolutionary algorithm
dc.subject Banking
dc.subject Fuzzy cognitive maps
dc.subject Model uncertainty
dc.subject Multiobjective optimization
dc.subject 113 Computer and information sciences
dc.title Banking applications of FCM models en
dc.type Chapter
dc.contributor.organization Department of Economics and Management
dc.description.reviewstatus Peer reviewed
dc.relation.doi https://doi.org/10.1007/978-3-030-00485-9_7
dc.relation.issn 1860-949X
dc.rights.accesslevel openAccess
dc.type.version acceptedVersion

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