Banking applications of FCM models

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http://hdl.handle.net/10138/314650

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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

Titel: Banking applications of FCM models
Författare: Hatwágner, Miklós F.; Vastag, Gyula; Niskanen, Vesa A.; Kóczy, László T.
Medarbetare: Cornejo, María Eugenia
Kóczy, László T.
Medina, Jesús
De Barros Ruano, Antonio Eduardo
Upphovmannens organisation: Department of Economics and Management
Utgivare: Springer Verlag
Datum: 2019
Språk: eng
Sidantal: 12
Tillhör serie: Trends in Mathematics and Computational Intelligence
Tillhör serie: Studies in Computational Intelligence
ISBN: 978-3-030-00484-2
978-3-030-00485-9
ISSN: 1860-949X
DOI: https://doi.org/10.1007/978-3-030-00485-9_7
Permanenta länken (URI): http://hdl.handle.net/10138/314650
Abstrakt: 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.
Subject: Bacterial evolutionary algorithm
Banking
Fuzzy cognitive maps
Model uncertainty
Multiobjective optimization
113 Computer and information sciences
Referentgranskad: Ja
Användningsbegränsning: openAccess
Parallelpublicerad version: acceptedVersion


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