A multicriteria approach to manage credit risk under strict uncertainty

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's 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.

Credit Risk Management of Property Investments through Multi-Criteria Indicators

“…The need, therefore, to synthesize the different expertise participating in the decision-making process through shared platforms, has encouraged the spread and application of MCDA in various disciplines, attracting attention from the scientific community. The MCDA, due to their flexibility, find application in many different complex decision problems, such as the regeneration of the urban peripheries (Locurcio et al 2019), the cultural heritage enhancement (Morano et al 2016), the analysis of the urban renewal projects (Morano et al 2015), and the management of the credit risk under strict uncertainty (Pla-Santamaria et al 2020).…”

Geometric compromise programming: application in portfolio selection

“…We believe that the GCP approach presented in this paper can be applied to any other field in which CP is used to evaluate solutions in terms of the distance to a reference point. A non‐comprehensive list of suitable fields of application include credit risk management (Pla‐Santamaria et al., 2021), cash management (Salas‐Molina, 2019), water management (Fattahi and Fayyaz, 2010), forestry and agricultural planning (Diaz‐Balteiro and Romero, 2008) and public policy design (André et al., 2010). In addition, we consider that the design of additional utility functions to characterise in a different way the behaviour of extreme seekers is a natural extension of this work.…”

“…The pioneering applications of CP for portfolio selection are due to Ballestero and Romero (1996). Since then, several works have extended the CP approach in this area of research (Ballestero and Pla‐Santamaria, 2003, 2004; Bilbao‐Terol et al., 2014; Pla‐Santamaria et al., 2021). In addition, Parra et al.…”

“…The pioneering applications of CP for portfolio selection are due to Ballestero and Romero (1996). Since then, several works have extended the CP approach in this area of research (Ballestero andPla-Santamaria, 2003, 2004;Bilbao-Terol et al, 2014;Pla-Santamaria et al, 2021). In addition, Parra et al (2005) and Bilbao-Terol et al (2006) proposed a new CP model for portfolio selection including the imprecision and subjectivity inherent to some data.…”