Abstract
Over the years, banks have faced many difficulties, related mainly to lax credit standards for borrowers and counterparties. The goal of credit risk management is to maintain the volume of credit risk at acceptable level as it is a vital feature in risk management. Credit analysts take into consideration factors of a wider spectrum, e.g., the prospects of the line of business, the experience of board members, credibility of suppliers, etc. Those factors are often considered on the linguistic scale, which includes such imprecise and inaccurate phrases, for instance, such as: more/less experienced, better/worse prospects, etc., which, for the experts and decision makers, are justified and result from their personal experience, preferences and human nature. The paper presents the approach of supporting methods in the credit risk decision-making process. It presents evaluation scales of imprecise phrases commonly used during the process of credit risk assessment based on experts’ preferences. Due to the imprecision, the oriented fuzzy numbers are a useful tool. For such described evaluation scales, we use a scoring function determined with the use of an adapted Simple Additive Weighting (SAW) method.
Highlights
IntroductionWe can encounter new approaches and methods
The correct classification of debtors is an important topic
It is important to stress that the concept of granular computing is presented in [30], where it is compared to human ability to come up with a solution and decision basing it on imprecise information and only partial truth, certainty and knowledge
Summary
We can encounter new approaches and methods. Those methods, which are to aid the decision makers, are continuously improved to succeed in a better classification of potential banks’ clients. International institutions, such as, for instance, Basel Committee on Banking Supervision, recognize the significance of risk management and express their opinion, as well as some guidelines, in published documents. One of those documents is Basel III, which refers to risk management and supervision. In a subsequent forth document, the focus is on a standardized and internal rating-based (IRB) credit risk approach
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