بررسی شاخص‌های اعتبارسنجی مشتریان بانکی با استفاده از روش هوش مصنوعی و دلفی

قنبری, سلیمه; نظام آبادی‌پور, حسین; جلایی, سید عبدالمجید

doi:10.22054/ims.2022.15520

نوع مقاله : مقاله پژوهشی

نویسندگان

¹ مربی، گروه گردشگری و هتلداری، مجتمع آموزش عالی بم، بم، ایران نویسنده مسئول : s.ghanbari@bam.ac.ir

² استاد، گروه مهندسی برق، دانشگاه شهید باهنر، کرمان، ایران

³ استاد، گروه اقتصاد، دانشگاه شهید باهنر، کرمان، ایران

https://doi.org/10.22054/ims.2022.15520

چکیده

با توجه به اهمیت اعطای اعتبار در نظام بانکی، استفاده از عوامل مؤثر بر اعتبارسنجی جهت تصمیم‌گیری در اعتباردهی، بسیار مهم است. با توجه به این مهم، تحقیق حاضر به شناسایی و اولویت‌بندی ویژگی‌های تأثیرگذار در اعتبارسنجی مشتریان با استفاده از دیدگاه متخصصان و کارشناسان بانکی شهر کرمان و تطابق آن با شاخص‌های موجود در مدل‌های استخراج‌شده از روش هوش مصنوعی پرداخته است. هدف این است که آیا بین نظرات انسانی که ناشی از دانش و تجربه است و نظرات هوش مصنوعی که به مسئله به‌صورت مدل‌سازی جعبه سیاه نگاه می‌کنند، تطابق وجود دارد یا خیر. داده‌های موردنیاز به روش پرسشنامه و الگوریتم باینری کوانتومی جمعیت ذرات، جمع‌آوری‌شده و به ترتیب به روش دلفی و فرا ابتکاری موردبررسی قرارگرفته‌اند. نتایج حاکی از آن است که شاخص‌های منتخب دو روش 80 درصد همپوشانی داشته‌اند. با توجه به نتایج تحقیق و دقت بالای تکنیک‌های هوش مصنوعی، پیشنهاد می‌شود جهت اعطای اعتبار به مشتریان در بانک‌ها و مؤسسات مالی و اعتباری، وزن بالاتری برای شاخص‌های مذکور لحاظ شود.

کلیدواژه‌ها

عنوان مقاله [English]

Study of Banking Customers Credit Scoring Indicators Using Artificial Intelligence and Delphi Method

نویسندگان [English]

Salimeh Ghanbari ¹
Hossein Nezamabadi-pour ²
Sayyed Abdolmajid Jalaee ³

¹ Instructor of Entrepreneurship, Faculty of Tourism and Hospitality Management, Higher Education Complex of Bam, Bam, Iran, Corresponding Author: s.ghanbari@bam.ac.ir

² Professor of Elec Eng(EE), Faculty of Engineering, Shahid Bahonar University of Kerman, Kerman, Iran.

³ Professor of Economics, Faculty of Management and Economics, Shahid Bahonar University of Kerman, Kerman Iran.

چکیده [English]

With the importance of lending in the banking industry, it is very important to use the indicators affecting credit to decide on lending. The purpose of the present study is to identify and prioritize the effective features in customer accreditation using the viewpoints of bank experts in Kerman and to compare them with existing indicators in models extracted from Meta-Heuristic and Artificial Intelligence methods. The aim is to find out whether there is a match between the human views that arise from knowledge and experience and the views of artificial intelligence that look at the problem as black-box modeling. Required data were collected by questionnaire method and Quantum Binary particle swarm optimization algorithm and analyzed by Delphi. The results show that the selected indices have 80% overlap between the two methods. Due to the results of research and high accuracy of artificial intelligence techniques, it is suggested that in order to give credit to customers in banks and financial and credit institutions, to consider a higher weight for these indicators.

کلیدواژه‌ها [English]

Keywords: Credit Scoring
Delphi
Meta-Heuristic Algorithm
Pattern Recognition
Feature Selection

مراجع

آذر، عادل.، فرجی، حجت. (1381). علم مدیریت فازی، تهران، اجتماع.

تقوی فرد، محمدتقی.، نادعلی، احمد. (1391). طبقه‌بندی متقاضیان تسهیلات اعتباری بانکی با استفاده از داده‌کاوی و منطق فازی، فصلنامه مطالعات مدیریت صنعتی، 25، 85 - 108.

جلیلی، محمد.، خدایی وله زاقرد، محمد.، کنشلو، مهدیه. (1389). اعتبارسنجی مشتریان حقیقی در سیستم بانکی کشور، مطالعات کمی در مدیریت، 1 (3)، 127-148.

راعی، رضا.، فلاح‌پور، سعید (1387). کاربرد ماشین بردار پشتیبان در پیش‌بینی درماندگی مالی شرکت‌ها با استفاده از نسبت‌های مالی، بررسی‌های حسابداری و حسابرسی، 15 (53)، 17-34.

رجب‌زاده قطری، علی.، میرزایی آرش، بهرام.، احمدی، پرویز. (1388). طراحی سیستم هوشمند ترکیبی رتبه‌بندی اعتباری مشتریان بانک‌ها با استفاده از مدل‌های استدلالی فازی ترکیبی، پژوهشنامه بازرگانی، 14 (53)، 159 - 201. ‌

صالحی، مجتبی.، کرد کتولی، علیرضا. (1396). انتخاب ویژگی‌های بهینه به‌منظور تعیین ریسک اعتباری مشتریان بانکی. مطالعات مدیریت کسب‌وکار هوشمند،6 (22)، 129-154.‌ https://doi.org/10.22054/ims.2018.8523

قدسی پور، حسن.، سالاری، میثم .، دلاوری، وحید. (1391). ارزیابی ریسک اعتباری شرکت‌های وام‌گیرنده از بانک با استفاده از تحلیل سلسله مراتبی فازی و شبکه عصبی ترکیبی درجه بالا، نشریه بین‌المللی مهندسی صنایع و مدیریت تولید، 23 (1)، 43- 54.

نظام‌آبادی‌پور، حسین. (1392). الگوریتم وراثتی: مفاهیم پایه و مباحث پیشرفته، انتشارات دانشگاه شهید باهنر کرمان.

هاشمی تیله نویی، مصطفی.، حسین‌زاده، صبا. (1399). بررسی برتری مدل هیبریدی نسبت به سایر مدل‌ها در فرایند اعتبارسنجی بانک‌های کشور (مورد مطالعاتی برخی شرکت‌های پذیرفته‌شده در بورس اوراق بهادار تهران). نشریه اقتصاد و بانکداری اسلامی، 9 (31)، 173-204.

References

Abdou, H, Pointon, J, & El-Masry, A. (2008). Neural nets versus conventional techniques in credit scoring in Egyptian banking. Expert systems with applications, 35 (3) 1275-1292. https://doi.org/10.1016/j.eswa.2007.08.030

Abellán, J, Castellano, J. G. (2017). A comparative study on base classifiers in ensemble methods for credit scoring. Expert Systems with Applications, 73, 1-10. https://doi.org/ 10.1016/j.eswa.2016.12.020

Ala'raj, M, Abbod, M. F. (2016). Classifiers consensus system approach for credit scoring. Knowledge-Based Systems, 104, 89-105. https://doi.org/10.1016/j.knosys.2016.04.013

Anderson, R. (2007). The credit scoring toolkit: theory and practice for retail credit risk management and decision automation. Oxford University Press.

Barani, F, Mirhosseini, M, & Nezamabadi-Pour, H. (2017). Application of binary quantum-inspired gravitational search algorithm in feature subset selection. Applied Intelligence, 47, 304-318. https://doi.org/10.1007/s10489-017-0894-3

Bellotti, T, Crook, J. (2009). Support vector machines for credit scoring and discovery of significant features. Expert systems with applications, 36 (2), 3302-3308. https://doi.org/10.1016/j.eswa.2008.01.005

Cao, A, He, H, Chen, Z, & Zhang, W. (2018). Performance Evaluation of Machine Learning Approaches for Credit Scoring. International Journal of Economics, Finance and Management Sciences, 6 (6), 255.

Chen, M. C, Huang, S. H. (2003). Credit scoring and rejected instances reassigning through evolutionary computation techniques. Expert Systems with Applications, 24 (4), 433-441. https://doi.org/10.1016/S0957-4174(02)00191-4

Chen, W, Ma, C, & Ma, L. (2009). Mining the customer credit using hybrid support vector machine technique. Expert systems with applications, 36 (4), 7611-7616. https://doi.org/10.1016/j.eswa.2008.09.054

Chen,W, Xiang, G, Liu, Y, & Wang, K. (2012). Credit risk Evaluation by hybrid data mining technique. Systems Engineering Procedia, 3, 194-200. https://doi.org/10.1016/j.sepro.2011.10.029

Chi, B. W, Hsu, C. C. (2012). A hybrid approach to integrate genetic algorithm into dual scoring model in enhancing the performance of credit scoring model. Expert Systems with Applications, 39 (3), 2650-2661. https://doi.org/10.1016/j.eswa.2011.08.120

Chuang, C. L, Lin, R. H. (2009). Constructing a reassigning credit scoring model. Expert Systems with Applications, 36 (2), 1685-1694. https://doi.org/10.1016/j.eswa.2007.11.067

Clayton, M. J. (1997). Delphi: a technique to harness expert opinion for critical decision‐making tasks in education. Educational psychology, 17 (4), 373-386. https://doi.org/10.1080/0144341970170401

Dalkey, N, Helmer, O. (1963). An experimental application of the Delphi method to the use of experts. Management science, 9 (3), 458-467. https://doi.org/10.1287/mnsc.9.3.458

Dowlatshahi, M. B, Derhami, V, & Nezamabadi-pour, H. (2018). A novel three-stage filter-wrapper framework for miRNA subset selection in cancer classification. In Informatics. 5 (1), p. 13. MDPI. https://doi.org/10.3390/informatics5010013

Edla, D. R, Tripathi, D, Cheruku, R, & Kuppili, V. (2018). An efficient multi-layer ensemble framework with BPSOGSA-based feature selection for credit scoring data analysis. Arabian Journal for Science and Engineering, 43 (12), 6909-6928. https://doi.org/10.1007/s13369-017-2905-4

Fernandes, G. B, Artes, R. (2016). Spatial dependence in credit risk and its improvement in credit scoring. European Journal of Operational Research, 249 (2), 517-524. https://doi.org/10.1016/j.ejor.2015.07.013

Goh, R. Y, Lee, L. S. (2019). Credit scoring: a review on support vector machines and metaheuristic approaches. Advances in Operations Research, 2019. https://doi.org/10.1155/2019/1974794

Goh, R. Y, Lee, L. S, Seow, H. V, & Gopal, K. (2020). Hybrid Harmony Search–Artificial Intelligence Models in Credit Scoring. Entropy, 22 (9), 989. https://doi.org/10.3390/e22090989

Harris, T. (2015). Credit scoring using the clustered support vector machine. Expert Systems with Applications, 42 (2) 741-750. https://doi.org/10.1016/j.eswa.2014.08.029

He, H, Zhang, W, & Zhang, S. (2018). A novel ensemble method for credit scoring: Adaption of different imbalance ratios. Expert Systems with Applications, 98, 105-117. https://doi.org/10.1016/j.eswa.2018.01.012

Huang, C. L, Chen, M. C, & Wang, C. J. (2007). Credit scoring with a data mining approach based on support vector machines. Expert systems with applications, 33 (4), 847-856. https://doi.org/10.1016/j.eswa.2006.07.007

Huang, C. L, Dun, J. F. (2008). A distributed PSO–SVM hybrid system with feature selection and parameter optimization. Applied soft computing, 8 (4), 1381-1391. https://doi.org/10.1016/j.asoc.2007.10.007

Huang, X, Liu, X, & Ren, Y. (2018). Enterprise credit risk evaluation based on neural network algorithm. Cognitive Systems Research, 52, 317-324. https://doi.org/10.1016/j.cogsys.2018.07.023

Hung, C, Chen, J. H. (2009). A selective ensemble based on expected probabilities for bankruptcy prediction. Expert systems with applications, 36 (3), 5297-5303. https://doi.org/10.1016/j.eswa.2008.06.068

Jadhav, S, He, H, & Jenkins, K. (2018). Information gain directed genetic algorithm wrapper feature selection for credit rating. Applied Soft Computing, 69, 541-553. https://doi.org/10.1016/j.asoc.2018.04.033

Jeong, Y. W, Park, J. B, Jang, S. H, & Lee, K. Y. (2010). A new quantum-inspired binary PSO: application to unit commitment problems for power systems. IEEE Transactions on Power Systems, 25 (3), 1486-1495. https://doi.org/10.1109/TPWRS.2010.2042472

Kashef, S, Nezamabadi‐pour, H, & Nikpour, B. (2018). Multilabel feature selection: A comprehensive review and guiding experiments. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 8(2), e1240. https://doi.org/10.1002/widm.1240

Koutanaei, F. N, Sajedi, H, & Khanbabaei, M. (2015). A hybrid data mining model of feature selection algorithms and ensemble learning classifiers for credit scoring. Journal of Retailing and Consumer Services, 27, 11-23. https://doi.org/10.1016/j.jretconser.2015.07.003

Lee, T. S, Chen, I. F. (2005). A two-stage hybrid credit scoring model using artificial neural networks and multivariate adaptive regression splines. Expert Systems with Applications, 28 (4) 743-752. https://doi.org/10.1016/j.eswa.2004.12.031

Liang, D, Tsai, C. F, & Wu, H. T. (2015). The effect of feature selection on financial distress prediction. Knowledge-Based Systems, 73, 289-297. https://doi.org/10.1016/j.knosys.2014.10.010

Marqués, A. I, García, V, & Sánchez, J. S. (2012). Exploring the behaviour of base classifiers in credit scoring ensembles. Expert Systems with Applications, 39 (11) 10244-10250. https://doi.org/10.1016/j.eswa.2012.02.092

Mohammadi, F, Fathi, Z. (2016). The Study of the Effect of 5C Factors on the Credit Risk of Natural Customer of Refah e Kargaran Bank and Credit assessment. European Online Journal of Natural and Social Sciences: Proceedings, 4 (1 (s)), pp-1651.

Nanni, L, & Lumini, A. (2009). An experimental comparison of ensemble of classifiers for bankruptcy prediction and credit scoring. Expert systems with applications, 36(2), 3028-3033. https://doi.org/10.1016/j.eswa.2008.01.018

‌Oreski, S, Oreski, G. (2014). Genetic algorithm-based heuristic for feature selection in credit risk assessment. Expert systems with applications, 41 (4), 2052-2064. https://doi.org/10.1016/j.eswa.2013.09.004

Ozturk, H, Namli, E, & Erdal, H. I. (2016). Modelling sovereign credit ratings: The accuracy of models in a heterogeneous sample. Economic Modelling, 54, 469-478. https://doi.org/10.1016/j.econmod.2016.01.012

Ping, Y, Yongheng, L. (2011). Neighborhood rough set and SVM based hybrid credit scoring classifier. Expert Systems with Applications, 38 (9), 11300-11304. https://doi.org/10.1016/j.eswa.2011.02.179

Powell, C. (2003). The Delphi technique: myths and realities. Journal of advanced nursing, 41 (4), 376-382. https://doi.org/10.1046/j.1365-2648.2003.02537.x

Sánchez, J. F. M, Lechuga, G. P. (2016). Assessment of a credit scoring system for popular bank savings and credit. Contaduría y Administración, 61 (2), 391-417. https://doi.org/10.1016/j.cya.2015.11.004

Schebesch, K. B, Stecking, R. (2005). Support vector machines for classifying and describing credit applicants: detecting typical and critical regions. Journal of the operational research society, 56 (9), 1082-1088. https://doi.org/10.1057/palgrave.jors.2602023

Şen, D, Dönmez, C. Ç, & Yıldırım, U. M. (2020). A hybrid bi-level metaheuristic for credit scoring. Information Systems Frontiers, 22 (5), 1009-1019. https://doi.org/10.1007/s10796-020-10037-0

Siami, M, Hajimohammadi, Z. (2013). Credit scoring in banks and financial institutions via data mining techniques: A literature review. Journal of AI and Data Mining, 1 (2) 119-129. https://doi.org/10.22044/jadm.2013.124

Singh, S, Murthi, B. P. S, & Steffes, E. (2013). Developing a measure of risk adjusted revenue (RAR) in credit cards market: Implications for customer relationship management. European Journal of Operational Research, 224 (2) 425-434. https://doi.org/10.1016/j.ejor.2012.08.007

Tomczak, J. M, Zięba, M. (2015). Classification restricted Boltzmann machine for comprehensible credit scoring model. Expert Systems with Applications, 42 (4) 1789-1796. https://doi.org/10.1016/j.eswa.2014.10.016

West, D. (2000). Neural network credit scoring models. Computers & Operations Research, 27 (11-12), 1131-1152. https://doi.org/10.1016/S0305-0548(99)00149-5

Xia, Y, Liu, C, Da, B, & Xie, F. (2018). A novel heterogeneous ensemble credit scoring model based on bstacking approach. Expert Systems with Applications, 93, 182-199. https://doi.org/10.1016/j.eswa.2017.10.022

Xiao, H, Xiao, Z, & Wang, Y. (2016). Ensemble classification based on supervised clustering for credit scoring. Applied Soft Computing, 43, 73-86. https://doi.org/10.1016/j.asoc.2016.02.022

Yu, L, Wang, S, & Lai, K. K. (2008). Credit risk assessment with a multistage neural network ensemble learning approach. Expert systems with applications, 34 (2) 1434-1444. https://doi.org/10.1016/j.eswa.2007.01.009

Yu, L, Wang, S, & Lai, K. K. (2009). An intelligent-agent-based fuzzy group decision making model for financial multicriteria decision support: The case of credit scoring. European journal of operational research, 195 (3) 942-959. https://doi.org/10.1016/j.ejor.2007.11.025

Yu, L, Yue, W, Wang, S, & Lai, K. K. (2010). Support vector machine based multiagent ensemble learning for credit risk evaluation. Expert Systems with Applications, 37 (2) 1351-1360. https://doi.org/10.1016/j.eswa.2009.06.083

Yu, L, Zhou, R, Tang, L, & Chen, R. (2018). A DBN-based resampling SVM ensemble learning paradigm for credit classification with imbalanced data. Applied Soft Computing, 69, 192-202. https://doi.org/10.1016/j.asoc.2018.04.049

Zhang, X, Yang, Y, & Zhou, Z. (2018). A novel credit scoring model based on optimized random forest. In 2018 IEEE 8th Annual Computing and Communication Workshop and Conference (CCWC) (pp. 60-65). IEEE. https://doi.org/10.1109/CCWC.2018.8301707

Zhao, Z, Xu, S, Kang, B. H, Kabir, M. M. J, Liu, Y, & Wasinger, R. (2015). Investigation and improvement of multi-layer perceptron neural networks for credit scoring. Expert Systems with Applications, 42 (7), 3508-3516. https://doi.org/10.1016/j.eswa.2014.12.006

References [In Persian]

Azar, A, Faraji, H. (2002). Fuzzy Management science, Ejtema. [In Persian].

Taghavifard, M. T, Nadali, A. (2012). Classification of Credit Applicants of Banking Systems Using Data Mining and Fuzzy Logic. Industrial Management Studies, 9 (25), 85- 107. [In Persian].

Jalili, M, Khodaei, M, & Koneshlou, M. (2010). Validation of real customers in the country's banking system. Quantitative Researches in Management, 1 (3), 127-148. [In Persian].

Raei, R, Fallahpour, S. (2009). Support Vector Machines Application in Financial Distress Prediction of Companies Using Financial Ratios. Accounting and Auditing Review, 15(4), 17- 34. [In Persian].

Rajabzadeh, A, bahrammirzaei, A, & Ahmadi, P. (2010). Hybrid Intelligent Credit Ranking System Using Fuzzy Hybrid-Reasoning Models. Iranian Journal of Trade Studies, 14 (54), 159- 201. [In Persian].

Salehi, M, Korde Katooli, A. (2018). Optimal Feature Selection in order to Bank Customer Credit Risk Determination. BI Management Studies, 6(22), 129-154. [In Persian].

Ghodsipour, S. H, Salari, M, & Delavari, V. (2012). Using Fuzzy Analytic Hierarchy Process and Hybrid of Higher Order Neural Network for Evaluation Credit Risk of Corporate. International Journal of Industrial Engineering & Production Research, 23 (1), 43-54. [In Persian].

Nezamabadi-pour, H. (2013). Inheritance Algorithm: Basic Concepts and Advanced Topics, Publications of Shahid Bahonar University of Kerman. [In Persian].

Hashemi Tilehnouei, M, Hosseinzadeh, S. (2020). Investigating the Efficiency of Hybrid Model in Comparison with Logistic Regression and Artificial Neural Network in Credit Risk Evaluation of Companies Listed in Tehran Stock Exchange. Islamic Economics and Banking, 9 (31), 173-204. [In Persian].

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‌ ‌استناد به این مقاله: قنبری، سلیمه.، نظام‌آبادی‌پور، حسین.، جلایی، سید عبدالمجید. (1401). بررسی شاخص‌های اعتبارسنجی مشتریان بانکی با استفاده از روش هوش مصنوعی و دلفی، مطالعات مدیریت کسب وکار هوشمند، 11(42)، 237-265.

DOI: 10.22054/IMS.2021.49698.1669

Journal of Business Intelligence Management Studies is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License..

مطالعات مدیریت کسب و کار هوشمند

بررسی شاخص‌های اعتبارسنجی مشتریان بانکی با استفاده از روش هوش مصنوعی و دلفی

مراجع

مراجع

دوره 11، شماره 42
دی 1401
صفحه 237-265

بررسی شاخص‌های اعتبارسنجی مشتریان بانکی با استفاده از روش هوش مصنوعی و دلفی

مراجع

مراجع

دوره 11، شماره 42دی 1401صفحه 237-265

دوره 11، شماره 42
دی 1401
صفحه 237-265