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

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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DOI: 10.22054/IMS.2021.49698.1669

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