IVIFCM-TOPSIS for bank credit risk assessment

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216275%3A25410%2F19%3A39914914" target="_blank" >RIV/00216275:25410/19:39914914 - isvavai.cz</a>

Výsledek na webu

<a href="https://link.springer.com/chapter/10.1007/978-981-13-8311-3_9" target="_blank" >https://link.springer.com/chapter/10.1007/978-981-13-8311-3_9</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-981-13-8311-3_9" target="_blank" >10.1007/978-981-13-8311-3_9</a>

Jazyk výsledku

angličtina

Název v původním jazyce

IVIFCM-TOPSIS for bank credit risk assessment

Popis výsledku v původním jazyce

Bank credit risk assessment is performed by credit rating agencies in order to reduce information asymmetry in financial markets. This costly process has been automated in earlier studies by using systems based on machine learning methods. However, such systems suffer from interpretability issues and do not utilize expert knowledge effectively. To overcome those problems, multi-criteria group decision-making (MCGDM) methods have recently been used to simulate the assessment process performed by the committee of multiple credit risk experts. However, standard MCGDM methods fail to consider high uncertainty inherently associated with the assessment and do not work effectively when the assessed credit risk criteria interact with each other. To address these issues, we propose MCGDM model for bank credit risk assessment that has two advantages: (1) The imprecise assessment criteria are represented by interval-valued intuitionistic fuzzy sets, and (2) the interactions among the criteria are modeled using fuzzy cognitive maps. When combined with traditional TOPSIS approach to ranking alternatives, we show that the proposed model can be effectively applied to assess bank credit risk.

Název v anglickém jazyce

IVIFCM-TOPSIS for bank credit risk assessment

Popis výsledku anglicky

Bank credit risk assessment is performed by credit rating agencies in order to reduce information asymmetry in financial markets. This costly process has been automated in earlier studies by using systems based on machine learning methods. However, such systems suffer from interpretability issues and do not utilize expert knowledge effectively. To overcome those problems, multi-criteria group decision-making (MCGDM) methods have recently been used to simulate the assessment process performed by the committee of multiple credit risk experts. However, standard MCGDM methods fail to consider high uncertainty inherently associated with the assessment and do not work effectively when the assessed credit risk criteria interact with each other. To address these issues, we propose MCGDM model for bank credit risk assessment that has two advantages: (1) The imprecise assessment criteria are represented by interval-valued intuitionistic fuzzy sets, and (2) the interactions among the criteria are modeled using fuzzy cognitive maps. When combined with traditional TOPSIS approach to ranking alternatives, we show that the proposed model can be effectively applied to assess bank credit risk.

Druh

D - Stať ve sborníku

CEP obor

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OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

<a href="/cs/project/GA16-19590S" target="_blank" >GA16-19590S: Analýza témat a sentimentu vícenásobných textových zdrojů pro finanční rozhodování podniků</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2019

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Intelligent Decision Technologies 2019 : Proceedings of the 11th KES International Conference on Intelligent Decision Technologies (KES-IDT 2019), Vol. 1

ISBN

978-981-13-8310-6

ISSN

2190-3018

e-ISSN

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Počet stran výsledku

10

Strana od-do

99-108

Název nakladatele

Springer Nature

Místo vydání

Heidelberg

Místo konání akce

St. Julians

Datum konání akce

17. 6. 2019

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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