A Scale of Credit Risk Evaluations Assessed by Ordered Fuzzy Numbers

Abstract

Banks faced many difficulties related to lax credit standards. The effective management of credit risk is a critical component of a comprehensive approach to risk management and it should maintain credit risk exposure within acceptable parameters. However, the problem arises when standards are not strictly quantitative as managers often depend on various approaches – also on experts’ techniques. Each bank has the credit assessment department and a specific credit assessment committee. The committee is provided with the analysts’ recommendation based on ratios from financial statements and internal rating system. However, the final decision belongs to the committee members who do not solely rely on financial data and take into consideration factors of a wider spectrum, e.g. the prospects of the line of business or the experience of board members etc. Those factors are often considered on the linguistic scale which includes imprecise and inaccurate quantifiers such as: more/less, better/worse etc. which for the experts are justified and result from their personal experience.

The paper presents the approach of the decision-making techniques and scales of imprecise phrases commonly used in the process of credit risk assessment based on experts’ preferences. Due to the imprecision, ordered fuzzy numbers are a useful tool. It also focuses on a question how, a human judgement approach, based on prioritizing and ranking prospect borrowers, affects the decision-making process.