Document Type : Original Article


1 Department of Management, Ayandegan Institute of Higher Education, Tonekabon, Iran.

2 Department of Applied Mathematics, Ayandagn Institute of Higher Education, Tonkabon, Iran.


Bank facilities are the main outputs of the bank, through which the society's liquidity, which is placed in a wandering way at the level of the society, is injected into defined and targeted economic sources. In this regard, one of the major problems faced by decision-makers in banks is prioritizing loan applicants. Therefore, this research was conducted to identify the effective factors in developing a model for measuring customers' credit risk and determining a suitable algorithm for prioritizing bank applicants on a case study in Sepeh Bank. In this research, experts' intuitive and imprecise judgments were considered hesitant fuzzy data, and a simple distance-based algorithm was proposed. The output of the proposed algorithm is a detailed ranking of applicants for bank loans. The presented problem in this research is a decision-making one that is done intuitively, and so far, no research has been done to provide a prioritization algorithm in this field.


[1]   Sayadmanesh, S., & Sadeghi, Z. (In Press). Bank payments using blockchain technology: a new approach in the banking industry. Financial and banking strategic studies. (In Persian).
[2]   Kaviani, K., Kaviani, M., & Arfaie, M. (2023). Analysis of lending behavior of banks in Tehran capital market under non-performing loans. Financial and banking strategic studies, 1(2), 87–92. (In Persian).
[3]   Ghasemnia Arabi, N., & Safaei Ghadikolaei, A. (2019). Comparison of the performances of classical models and artificial intelligence in predicting bank customers’ credit status. Journal of business administration researches, 10(20), 51–69. (In Persian).
[4]   Hosseinzadeh Lotfi, F., Najafi, S. E., & Ghasemi Todeshki, H. (2023). Providing a comprehensive model of banking system performance evaluation using network data envelopment analysis model in non-deterministic space. Financial and banking strategic studies, 1(1), 1–21. (In Persian).
[5]   Xu, Z., & Xia, M. (2011). Distance and similarity measures for hesitant fuzzy sets. Information sciences, 181(11), 2128–2138.
[6]   Rodriguez, R. M., Martinez, L., & Herrera, F. (2012). Hesitant fuzzy linguistic term sets for decision making. IEEE transactions on fuzzy systems, 20(1), 109–119. DOI:10.1109/TFUZZ.2011.2170076
[7]   Rodríguez, R. M., Martínez, L., Torra, V., Xu, Z. S., & Herrera, F. (2014). Hesitant fuzzy sets: state of the art and future directions. International journal of intelligent systems, 29(6), 495–524.
[8]   Ye, J. (2015). Multiple-attribute decision-making method under a single-valued neutrosophic hesitant fuzzy environment. Journal of intelligent systems, 24(1), 23–36. DOI:10.1515/jisys-2014-0001
[9]   Şahin, R., & Liu, P. (2017). Correlation coefficient of single-valued neutrosophic hesitant fuzzy sets and its applications in decision making. Neural computing and applications, 28(6), 1387–1395. DOI:10.1007/s00521-015-2163-x
[10] Tang, M., & Liao, H. (2019). Managing information measures for hesitant fuzzy linguistic term sets and their applications in designing clustering algorithms. Information fusion, 50, 30–42. DOI:10.1016/j.inffus.2018.10.002
[11] Meng, F., Tang, J., Zhang, S., & Xu, Y. (2020). Public-private partnership decision making based on correlation coefficients of single-valued neutrosophic hesitant fuzzy sets. Informatica (Netherlands), 31(2), 359–397. DOI:10.15388/20-INFOR401
[12] Büyüközkan, G., Mukul, E., & Kongar, E. (2021). Health tourism strategy selection via SWOT analysis and integrated hesitant fuzzy linguistic AHP-MABAC approach. Socio-economic planning sciences, 74, 100929. DOI:10.1016/j.seps.2020.100929
[13] Das, A. K., & Granados, C. (2022). FP-intuitionistic multi fuzzy N-soft set and its induced Fp-Hesitant N-soft set in group decision-making. Decision making: applications in management and engineering, 5(1), 67–89. DOI:10.31181/dmame181221045d
[14] Ali, G., Afzal, A., Sheikh, U., & Nabeel, M. (2023). Multi-criteria group decision-making based on the combination of dual hesitant fuzzy sets with soft expert sets for the prediction of a local election scenario. Granular computing, 8(6), 2039–2066. DOI:10.1007/s41066-023-00414-w
[15] Edalatpanah, S. A. (2022). Using hesitant fuzzy sets to solve the problem of choosing a strategy in uncertain conditions. Journal of decisions and operations research, 7(2), 373–382. (In Persian).
[16] Barati, R., & Rashidi, S. F. (In Press). Fuzzy AHP and fuzzy TOPSIS synergy for ranking the factor influencing employee turnover intention in the Iran hotel industry. Journal of applied research on industrial engineering. (In Persian).
[17] Aghajani Mir, S. F., Rajabi Kafshgar, F. Z., & Arab, A. (2022). Identifying and prioritizing challenges of implementing blockchain technology in the supply chain: a Bayesian BWM group-based approach. Journal of decisions and operations research, 6(4), 464–483. (In Persian).
[18] Masoomi, B., Sahebi, I. G., Fathi, M., Yıldırım, F., & Ghorbani, S. (2022). Strategic supplier selection for renewable energy supply chain under green capabilities (fuzzy BWM-WASPAS-COPRAS approach). Energy strategy reviews, 40, 100815. DOI:10.1016/j.esr.2022.100815
[19] Saberhoseini, S. F., Edalatpanah, S. A., & Sorourkhah, A. (2022). Choosing the best private-sector partner according to the risk factors in neutrosophic environment. Big data and computing visions, 2(2), 61–68. DOI:10.22105/bdcv.2022.334005.1075
[20] Edalatpanah, S. A. (2020). Data envelopment analysis based on triangular neutrosophic numbers. CAAI transactions on intelligence technology, 5(2), 94–98.
[21] Edalatpanah, S. A., & Smarandache, F. (2019). Data envelopment analysis for simplified neutrosophic sets. Neutrosophic sets and systems, 29, 215–226. DOI:10.5281/zenodo.3514433