ارائه مدلی مبتنی بر الگوریتم جنگل تصادفی و بهینه‌سازی جایا برای پیش‌بینی ریزش مشتریان بانکی

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانشجوی دکترا رشته مهندسی فناوری اطلاعات گرایش سیستم‌های چند رسانه‌ای، دانشکده فنی و مهندسی، دانشگاه قم، قم، ایران.

2 دانشجوی دکترای مهندسی فناوری اطلاعات(IT) گرایش تجارت الکترونیک، گروه مهندسی کامپیوتر و فناوری اطلاعات، دانشکده فنی و مهندسی، دانشگاه قم

چکیده

ریزش مشتری یک اصطلاح مالی است که به از دست دادن مشتری اشاره دارد؛ امروزه با توجه به تعداد زیاد بانک‌ها، ریزش مشتریان از یک بانک به بانک دیگر تبدیل به دغدغه جدی برای بانک‌های مختلف شده است. بنابراین در این مقاله که برای مشتریان یک بانک گردآوری شده است، می‌توان با توجه به رفتار و ویژگی‌های مشتریان قبل از وقوع ریزش، به شناسایی مشتریانی که احتمال ریزش بالایی دارند پرداخت و با ارائه پیشنهادهایی آن‌ها را حفظ نمود. در بازاریابی همه بر این امر توافق دارند که حفظ یک مشتری از جذب یک مشتری جدید بسیار کم هزینه‌تر است، از این رو این مقاله به معرفی فازهای مختلف رویکرد پیش‌بینی مشتری ریزشی با کمک یادگیری ماشین پرداخته است. روش پیشنهادی ترکیبی از الگوریتم‌های جنگل تصادفی و بهینه سازی جایا می‌باشد و ریزش مشتری را بر اساس ویژگی‌های مختلف مشتری مانند سن، جنسیت، جغرافیا و موارد دیگر پیش-بینی می‌کند. نتایج حاصل از مدل پیشنهادی در مقاله به ترتیب در معیارهای Precision ، Recall و Accuracy برابر مقادیر91.41 درصد، 95.66 درصدو 93.35 درصد می‌باشد.

کلیدواژه‌ها

موضوعات

عنوان مقاله [English]

A model based on random forest algorithm and Jaya optimization to predict bank customer churn

نویسندگان [English]

  • Sepideh Chehreh 1
  • Ali Sarabadani 2
1 Ph.D. Student in information technology engineering specializing in multimedia systems, Faculty of Engineering and Technology, Qom University, Qom, Iran.
2 Phd student of Information Technology (IT) Engineering, e-commerce , Department of Computer Engineering and Information Technology, Faculty of Technology and Engineering, Qom University
چکیده [English]

Customer churn is a financial term that refers to the loss of a customer; Today, due the large number of banks , the loss of customers from one bank to another ‌has become a serious concern for different banks. Therefore, in this article, which has been compiled for the customers of a bank , it is possible ‌to identify customers who have a high probability of falling by considering the behavior and characteristics of the customers before the fall occurs and ‌to keep them by providing suggestions. In marketing, everyone agrees that keeping a customer ‌is much less expensive than attracting a new customer, this article introduces the different phases of the approach of predicting customer churn with the help of machine learning. The proposed method is a combination of random forest algorithms and Jaya optimization, and customer dropout is based on different characteristics. Customer like age, Gender, graphs and cases It predicts more ‌. The results of model in the article are 91.41%, 95.66% and 93.35% respectively in Precision , Recall and Accuracy criteria.

کلیدواژه‌ها [English]

  • customer churn
  • machine learning
  • random forest algorithm
  • site optimization

مراجع

Ahmed, A. Khan, S. H. Khan, A. Basit, I. U. Haq, and Y. S. Lee, “Transfer Learning and Meta Classification Based Deep Churn Prediction System for Telecom Industry,” pp. 1–10, 2022. https://doi.org/10.1016/j.apmrv.30214.02.003Amin., “Customer churn prediction in the telecommunication sector using a rough set approach,” Neurocomputing, vol. 237, 2017. https://doi.org/10.1016/j.apmrv.2018.02.056H. A. Kandel, “A comparative study of tree-based models for churn prediction: a case study in the telecommunication sector.” 2019. DOI:10.1007/s00170-013-5021-yAmin, F. Al-Obeidat, B. Shah, A. Adnan, J. Loo, and S. Anwar, “Customer churn prediction in telecommunication industry using data certainty,” J. Bus. Res., vol. 94, pp. 290–301, 2019. https://doi.org/10.1016/j.amc.2005.01.081
 Baran, R.R. and Strunk, D.P. Principles of Customer Relationship Management. Australia: Thomson Southwest., PP: 131-134, 2019. DOI: https://10.1016/S0305-0548(03)00095-9
Baliga, A. J., Chawla, V., Sunder M, V., & Kumar, R. Barriers to service recovery in B2B markets: a TISM approach in the context of IT-based services. Journal of Business & Industrial Marketing. 1(11), 202-226, 2021. DOI: https://10.1016/S0305-0548(03)00095-9
Chauhan, S.; Akhtar, A.; Gupta, A. Customer experience in digital banking: A review and future research directions. IJQSS 14, 311–348, 2022.  https://doi.org/10.1016/j.jmse.2020.10.001
Christy, A.J.; Umamakeswari, A.; Priyatharsini, L.; Neyaa, A. RFM ranking-An effective approach to customer segmentation. J. King. Saud. Univ. Sci., 33, 1251–1257, 2021. DOI: https://doi.org/10.1016/j.apm.2012.04.041
De Lima Lemos, R.A.; Silva, T.C.; Tabak, B.M. Propension to customer churn in a financial institution: A machine learning approach. Neural Comput. Appl, 1–18, 2022.  (DOI): https://doi.org/10.22059/IMJ.2016.61711
Spanoudes and T. Nguyen, “Deep Learning in Customer Churn Prediction: Unsupervised Feature Learning on Abstract Company Independent Feature Vectors,” pp. 1–22, 2017. https://doi.org/10.1016/j.fss.2011.03.003
Elena Dumitrescu et al., "Machine learning for credit scoring: Improving logistic regression with non-linear decision-tree effects", European Journal of Operational Research, 2021. https://doi.org/10.1016/S1874-8651(10)75329-4
S. Halibas, A. C. Matthew, I. G. Pillai, J. H. Reazol, E. G. Delvo, and L. B. Reazol, “Determining the intervening effects of exploratory data analysis and feature engineering in telecoms customer churn modelling,” in 2018 4th MEC International Conference on Big Data and Smart City (ICBDSC), pp. 1–7, 2018. https://doi.org/10.1016/S1874-8651(10)56951-4Zhang, W. Li, T. Mo, and W. Tan, “Deep and Shallow Model for Insurance Churn Prediction Service,” 2019, doi: 10.1109/SCC.2017.
Hastie, Trevor. The elements of statistical learning : data mining, inference, and prediction : with 200 full-color illustrations. Springer. ISBN 0-387-95284-5. OCLC 46809224, 2022. doi.org/10.1016/S1874-8651(10)74596-4
Hosseini, M.; Shajari, S.; Akbarabadi, M. Identifying multi-channel value co-creator groups in the banking industry. J. Retail. Consum. Serv, 5, 102312, 2022.  https://doi.org/10.1016/S1874-8651(10)60853-4
iahou, X.; Harada, Y. B2C E-Commerce Customer Churn Prediction Based on K-Means and SVM. J. Theor. Appl. Electron. Commer. Res, 17, 458–475,2022. https://doi.org/10.1016/S1874-8651(10)60874-4
Jain, H.; Yadav, G.; Manoov, R. Churn prediction and retention in banking, telecom and IT sectors using machine learning techniques. In Advances in Machine Learning and Computational Intelligence; Springer: Singapore, pp. 137–156, 2022.  https://doi.org/10.1016/S1874-8651(10)60001-4
Li, M.; Wang, Q.W.; Shen, Y.Z.; Zhu, T.Y. Customer relationship management analysis of outpatients in a Chinese infectious disease hospital using drug-proportion recency-frequency-monetary model. Int. J. Med. Inform, 147, 104373, 2021.  https://doi.org/10.1016/S1874-8651(10)60384-4
 Li, Y.; Chu, X.Q.; Tian, D.; Feng, J.Y.; Mu, W.S. Customer segmentation using K-means clustering and the adaptive. Appl. Soft Comput, 113, 107924, 2021.Silveira, L.J.; Pinheiro, P.R.; Junior, L.S.D.M. A Novel Model Structured on Predictive Churn Methods in a Banking Organization. J. Risk Financ. Manag. 14, 481, 2022. https://doi.org/10.1016/S1874-8651(10)60852-4
Matuszela ´nski, K.; Kopczewska, K. Customer Churn in Retail E-Commerce Business: Spatial and Machine Learning Approach. J. Theor. Appl. Electron. Commer. Res, 17, 165–198,2022.  https://doi.org/10.1016/S1874-8651(10)906874-4
Muneer, A.; Ali, R.F.; Alghamdi, A.; Taib, S.M.; Almaghthawi, A.; Ghaleb, E.A.A. Predicting customers churning in banking industry: A machine learning approach. Indones. J. Electr. Eng. Comput. Sci, 26, 539–549, 2022. https://doi.org/10.1016/S1874-8651(10)60374-4
 Piryonesi, S. M.; El-Diraby, T. E. "Data Analytics in Asset Management: Cost-Effective Prediction of the Pavement Condition Index". Journal of Infrastructure Systems. doi:10.1061/(ASCE)IS.1943- 55X.0000512, 2022.  https://doi.org/10.1016/S1874-8651(10)90854-4
Rao, "Jaya: A simple and new optimization algorithm for solving constrained and unconstrained optimization problems," International Journal of Industrial Engineering Computations, vol. 7, no. 1, pp. 19-34, 2016. https://doi.org/10.1016/S1874-8651(10)60874-4Maldonado, J. López, and C. Vairetti, “Profit-based churn prediction based on Minimax Probability Machines,” Eur. J. Oper. Res., vol. 284, no. 1, pp. 273–284, doi: 10.1016/j.ejor.2020.12.007, 2020. https://doi.org/10.1016/S1874-8651(10)60932-4
Sunday, K., Ocheja, P., Hussain, S., Oyelere, S., Samson, B., Agbo, F.: Analyzing student performance in programming education using classification techniques. Int. J. Emerg. Technol. Learn. (iJET) 15(2), 127–144, 2020. https://doi.org/10.1016/S1874-8651(10)60037-4
Calzada-Infante, M. Óskarsdóttir, and B. Baesens, “Evaluation of customer behavior with temporal centrality metrics for churn prediction of prepaid contracts,” Expert Syst. Appl., vol. 160, p. 113553, doi: 10.1016/j.eswa.2020.113553, 2020. https://doi.org/10.1016/S1874-8651(10)60035-4
Tékouabou, S.C.K.; Chabbar, I.; Toulni, H.; Cherif, W.; Silkan, H. Optimizing the early glaucoma detection from visual fields by combining preprocessing techniques and ensemble classifier with selection strategies. Expert Syst. Appl, 189, 115975, 2022.  https://doi.org/10.1016/S1874-8651(10)67841-4
Tien-Yu. Hsu, "Machine learning applied to stock index performance enhancement", Journal of Banking and Financial Technology, pp. 1-13, 2021. (  https://doi.org/10.1016/S1874-8651(10)61324-4
Amin et al., “Just-in-time customer churn prediction in the telecommunication sector,” J. Supercomput., vol. 76, no. 6, pp. 3924–3948, doi: 10.1007/s11227-017-2149-9, 2020. https://doi.org/10.1016/S1874-8651(10)62134-4M. Kostić, M. I. Simić, and M. V Kostić, “Social Network Analysis and Churn Prediction in Telecommunications Using Graph Theory,” Entropy, vol. 22, no. 7, p. 753, 2020. https://doi.org/10.1016/S1874-8651(10)60001-4Özmen, E. K. Aydoğan, Y. Delice, and M. D. Toksarı, “Churn prediction in Turkey’s telecommunications sector: A proposed multiobjective–cost-sensitive ant colony optimization,” Wiley Interdiscip. Rev. Data Min. Knowl. Discov., vol. 10, no. 1, p. e1338, doi: 10.1002/widm.1338, 2020. https://doi.org/10.1016/S1874-8651(10)60001-4
Valluri, C.; Raju, S.; Patil, V.H. Customer determinants of used auto loan churn: Comparing predictive performance using machine learning techniques. J. Mark. Anal. 2021.  https://doi.org/10.1016/S1874-8651(10)60001-4
Veningston, K.; Rao, P.V.; Selvan, C.; Ronalda, M. Investigation on Customer Churn Prediction Using Machine Learning Techniques. In Proceedings of International Conference on Data Science and Applications; Springer: Singapore, pp. 109–119, 2022.  https://doi.org/10.1016/S1874-8651(10)60001-4
Ahmed, H. Afzal, I. Siddiqi, M. F. Amjad, and K. Khurshid, “Exploring nested ensemble learners using overproduction and choose approach for churn prediction in telecom industry,” Neural Comput. Appl., vol. 32, no. 8, pp. 3237–3251, doi: https://10.1007/s00521-018-3678-8, 2020.Jain, A. Khunteta, and S. Srivastava, “Churn Prediction in Telecommunication using Logistic Regression and Logit Boost,” Procedia Comput. Sci., vol. 167, pp. 101–112, doi: https://10.1016/j.procs.2020.03.187,2020.
Wu, J.; Shi, L.; Yang, L.P.; Niu, X.X.; Li, Y.Y.; Cui, X.D.; Tsai, S.B.; Zhang, Y.B. User Value Identification Based on Improved RFM Model and K-Means++ Algorithm for Complex Data Analysis. Wirel Commun. Mob.Com, 9982484, 1–8, 2021.  https://doi.org/10.1016/S1874-8651(10)60001-4
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