Control Theory & Systems
Amal Kumar Adak; Nil Kamal
Abstract
The incorporation of Pythagorean fuzzy sets into credit risk assessment represents a relatively innovative approach for predicting loan defaults, offering a more precise and adaptable tool for financial institutions. Key customer information—such as credit history, credit ...
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The incorporation of Pythagorean fuzzy sets into credit risk assessment represents a relatively innovative approach for predicting loan defaults, offering a more precise and adaptable tool for financial institutions. Key customer information—such as credit history, credit mix, credit utilization, duration of credit history, income level, and employment stability—is obtained as linguistic variables. These linguistic assessments are then transformed into Pythagorean fuzzy numbers. The combined Pythagorean fuzzy information is subsequently processed using the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). This approach employs a modified accuracy function to determine the Pythagorean fuzzy positive ideal solution and the Pythagorean fuzzy negative ideal solution. For distance calculations within the TOPSIS framework, spherical distance measurements are utilized. Alternatives are ranked based on the relative closeness coefficient and an adjusted index, collectively facilitating decision-making. The practical applicability of the proposed model is demonstrated through an illustrative numerical example.
