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Adak, Amal, Kamal, Nil. (1404). Pythagorean Fuzzy Sets for Credit Risk Assessment‎: ‎A Novel Approach to Predicting Loan Default. فصلنامه علمی زیست فناوری گیاهان زراعی, 10(1), 175-191. doi: 10.30473/coam.2025.73505.1287
Amal Kumar Adak; Nil Kamal. "Pythagorean Fuzzy Sets for Credit Risk Assessment‎: ‎A Novel Approach to Predicting Loan Default". فصلنامه علمی زیست فناوری گیاهان زراعی, 10, 1, 1404, 175-191. doi: 10.30473/coam.2025.73505.1287
Adak, Amal, Kamal, Nil. (1404). 'Pythagorean Fuzzy Sets for Credit Risk Assessment‎: ‎A Novel Approach to Predicting Loan Default', فصلنامه علمی زیست فناوری گیاهان زراعی, 10(1), pp. 175-191. doi: 10.30473/coam.2025.73505.1287
Adak, Amal, Kamal, Nil. Pythagorean Fuzzy Sets for Credit Risk Assessment‎: ‎A Novel Approach to Predicting Loan Default. فصلنامه علمی زیست فناوری گیاهان زراعی, 1404; 10(1): 175-191. doi: 10.30473/coam.2025.73505.1287

Pythagorean Fuzzy Sets for Credit Risk Assessment‎: ‎A Novel Approach to Predicting Loan Default

Control and Optimization in Applied Mathematics
مقاله 11، دوره 10، شماره 1 - شماره پیاپی 19، شهریور 2025، صفحه 175-191    اصل مقاله (648.03 K)
نوع مقاله: Research Article
شناسه دیجیتال (DOI): 10.30473/coam.2025.73505.1287
نویسندگان
Amal Kumar Adak* 1؛ Nil Kamal2
1Department of Mathematics‎, ‎Ganesh Dutt College‎, ‎Begusarai,‎ ‎India.
2Department of Mathematics‎, ‎Lalit Narayan Mithila University‎, ‎Darbhanga‎,‎ ‎India.
چکیده
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.

تازه های تحقیق
  • Presents a novel approach that integrates Pythagorean fuzzy sets (PFS) into credit risk assessment to enhance loan default predictions.
  • Converts linguistic variables—such as credit history, income, and employment stability—into Pythagorean fuzzy numbers to better manage uncertainty.
  • Utilizes the TOPSIS method with spherical distance metrics to rank borrowers according to their credit risk.
  • Demonstrates improved predictive accuracy and robustness compared to traditional credit scoring techniques.
  • Includes a practical numerical example demonstrating the effectiveness of the proposed PFS-based framework.
  • Outlines potential directions for future research, including model refinement, application across various financial sectors, and hybrid methods combining PFS with machine learning.
کلیدواژه‌ها
Pythagorean fuzzy sets‎؛ ‎Credit risk assessment‎؛ ‎Spherical distance measurement‎؛ ‎TOPSIS‎؛ ‎Revised index method
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