A multicriteria approach to manage credit risk under strict uncertainty

David Pla-Santamaria (), Mila Bravo (), Javier Reig-Mullor () and Francisco Salas-Molina
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David Pla-Santamaria: Universitat Politècnica de València, Ferràndiz y Carbonell
Mila Bravo: Universitat Politècnica de València, Ferràndiz y Carbonell
Javier Reig-Mullor: Universitas Miguel Hernandez

TOP: An Official Journal of the Spanish Society of Statistics and Operations Research, 2021, vol. 29, issue 2, No 11, 494-523

Abstract: Abstract Assessing the ability of applicants to repay their loans is generally recognized as a critical task in credit risk management. Credit managers rely on financial and market information, usually in the form of ratios, to estimate the quality of credit applicants. However, there is no guarantee that a given set of ratios contains the information needed for credit classification. Decision rules under strict uncertainty aim to mitigate this drawback. In this paper, we propose the use of a moderate pessimism decision rule combined with dimensionality reduction techniques and compromise programming. Moderate pessimism ensures that neither extreme optimistic nor pessimistic decisions are taken. Dimensionality reduction from a set of ratios facilitates the extraction of the relevant information. Compromise programming allows to find a balance between quality of debt and risk concentration. Our model produces two critical outputs: a quality assessment and the optimum allocation of funds. To illustrate our multicriteria approach, we include a case study on 29 firms listed in the Spanish stock market. Our results show that dimensionality reduction contributes to avoid redundancy and that quality-diversification optimization is able to produce budget allocations with a reduced number of firms.

Keywords: Moderate pessimism decision-making; Finance and banking; Financial modeling; Compromise programming; 90B50; 90C29; 91B30 (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (2)

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DOI: 10.1007/s11750-020-00571-0

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