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A multicriteria decision support tool for modelling bank credit ratings

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A multicriteria decision support tool for modelling bank credit ratings. / Gaganis, Chrysovalantis; Papadimitri, Giota; Tasiou, Menelaos.

In: Annals of Operations Research, 23.01.2020.

Research output: Contribution to journalArticlepeer-review

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@article{30e7cfcb9e87440f854737778be28d6e,
title = "A multicriteria decision support tool for modelling bank credit ratings",
abstract = "We introduce an application of the SMAA-Fuzzy-FlowSort approach to the case of modelling bank credit ratings. Its stochastic nature allows for imprecisions and uncertainty that naturally surround a decision-making exercise to be embedded into the proposed framework, whilst its output complements the ordinal nature of a crisp classification with cardinal information that shows the degree of membership to each rating category. Combined with the SMAA variant of GAIA that offers a visual of a bank{\textquoteright}s judgmental analysis, both recent approaches provide a holistic multicriteria decision support tool in the hands of a credit analyst and enable a rich inferential procedure to be conducted. To illustrate the assets of this framework, we provide a case study evaluating the credit risk of 55 EU banks according to their financial fundamentals.",
keywords = "Bank credit ratings, MCDA, SMAA, FlowSort, GAIA",
author = "Chrysovalantis Gaganis and Giota Papadimitri and Menelaos Tasiou",
year = "2020",
month = jan,
day = "23",
doi = "10.1007/s10479-020-03516-9",
language = "English",
journal = "Annals of Operations Research",
issn = "0254-5330",
publisher = "Springer Netherlands",

}

RIS

TY - JOUR

T1 - A multicriteria decision support tool for modelling bank credit ratings

AU - Gaganis, Chrysovalantis

AU - Papadimitri, Giota

AU - Tasiou, Menelaos

PY - 2020/1/23

Y1 - 2020/1/23

N2 - We introduce an application of the SMAA-Fuzzy-FlowSort approach to the case of modelling bank credit ratings. Its stochastic nature allows for imprecisions and uncertainty that naturally surround a decision-making exercise to be embedded into the proposed framework, whilst its output complements the ordinal nature of a crisp classification with cardinal information that shows the degree of membership to each rating category. Combined with the SMAA variant of GAIA that offers a visual of a bank’s judgmental analysis, both recent approaches provide a holistic multicriteria decision support tool in the hands of a credit analyst and enable a rich inferential procedure to be conducted. To illustrate the assets of this framework, we provide a case study evaluating the credit risk of 55 EU banks according to their financial fundamentals.

AB - We introduce an application of the SMAA-Fuzzy-FlowSort approach to the case of modelling bank credit ratings. Its stochastic nature allows for imprecisions and uncertainty that naturally surround a decision-making exercise to be embedded into the proposed framework, whilst its output complements the ordinal nature of a crisp classification with cardinal information that shows the degree of membership to each rating category. Combined with the SMAA variant of GAIA that offers a visual of a bank’s judgmental analysis, both recent approaches provide a holistic multicriteria decision support tool in the hands of a credit analyst and enable a rich inferential procedure to be conducted. To illustrate the assets of this framework, we provide a case study evaluating the credit risk of 55 EU banks according to their financial fundamentals.

KW - Bank credit ratings

KW - MCDA

KW - SMAA

KW - FlowSort

KW - GAIA

U2 - 10.1007/s10479-020-03516-9

DO - 10.1007/s10479-020-03516-9

M3 - Article

JO - Annals of Operations Research

JF - Annals of Operations Research

SN - 0254-5330

ER -

ID: 18003911