IDEAS home Printed from https://ideas.repec.org/a/eee/riibaf/v65y2023ics0275531923000399.html
   My bibliography  Save this article

Modelling bank customer behaviour using feature engineering and classification techniques

Author

Listed:
  • Abedin, Mohammad Zoynul
  • Hajek, Petr
  • Sharif, Taimur
  • Satu, Md. Shahriare
  • Khan, Md. Imran

Abstract

This study investigates customer behaviour and activity in the banking sector and uses various feature transformation techniques to convert the behavioural data into different data structures. Feature selection is then performed to generate feature subsets from the transformed datasets. Several classification methods used in the literature are applied to the original and transformed feature subsets. The proposed combined knowledge mining model enable us to conduct a benchmark study on the prediction of bank customer behaviour. A real bank customer dataset, drawn from 24,000 active and inactive customers, is used for an experimental analysis, which sheds new light on the role of feature engineering in bank customer classification. This paper’s detailed systematic analysis of the modelling of bank customer behaviour can help banking institutions take the right steps to increase their customers’ activity.

Suggested Citation

  • Abedin, Mohammad Zoynul & Hajek, Petr & Sharif, Taimur & Satu, Md. Shahriare & Khan, Md. Imran, 2023. "Modelling bank customer behaviour using feature engineering and classification techniques," Research in International Business and Finance, Elsevier, vol. 65(C).
    • Handle: RePEc:eee:riibaf:v:65:y:2023:i:c:s0275531923000399
    DOI: 10.1016/j.ribaf.2023.101913
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0275531923000399
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ribaf.2023.101913?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Zhang, Hao & Shi, Yuxin & Yang, Xueran & Zhou, Ruiling, 2021. "A firefly algorithm modified support vector machine for the credit risk assessment of supply chain finance," Research in International Business and Finance, Elsevier, vol. 58(C).
    2. Abbas Keramati & Hajar Ghaneei & Seyed Mohammad Mirmohammadi, 2016. "Developing a prediction model for customer churn from electronic banking services using data mining," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 2(1), pages 1-13, December.
    3. Yuan, Kunpeng & Chi, Guotai & Zhou, Ying & Yin, Hailei, 2022. "A novel two-stage hybrid default prediction model with k-means clustering and support vector domain description," Research in International Business and Finance, Elsevier, vol. 59(C).
    4. Fahmida E. Moula & Chi Guotai & Mohammad Zoynul Abedin, 2017. "Credit default prediction modeling: an application of support vector machine," Risk Management, Palgrave Macmillan, vol. 19(2), pages 158-187, May.
    5. D. Kalaivani & P. Sumathi, 2019. "Factor based prediction model for customer behavior analysis," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 10(4), pages 519-524, August.
    6. Vjosa Fejza & Ramiz Livoreka & Hykmete Bajrami, 2017. "Analyzing Consumer Behavior In Banking Sector Of Kosovo," Eurasian Journal of Business and Management, Eurasian Publications, vol. 5(4), pages 33-48.
    7. Amin, Adnan & Al-Obeidat, Feras & Shah, Babar & Adnan, Awais & Loo, Jonathan & Anwar, Sajid, 2019. "Customer churn prediction in telecommunication industry using data certainty," Journal of Business Research, Elsevier, vol. 94(C), pages 290-301.
    8. Alam, Nurul & Gao, Junbin & Jones, Stewart, 2021. "Corporate failure prediction: An evaluation of deep learning vs discrete hazard models," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 75(C).
    9. Nicholas Clerkin & Andrew Hanson, 2021. "Debit Card Incentives and Consumer Behavior: Evidence Using Natural Experiment Methods," Journal of Financial Services Research, Springer;Western Finance Association, vol. 60(2), pages 135-155, December.
    10. Berggrun, Luis & Salamanca, Juan & Díaz, Javier & Ospina, Juan David, 2020. "Profitability and money propagation in communities of bank clients: A visual analytics approach," Finance Research Letters, Elsevier, vol. 37(C).
    11. De Caigny, Arno & Coussement, Kristof & De Bock, Koen W. & Lessmann, Stefan, 2020. "Incorporating textual information in customer churn prediction models based on a convolutional neural network," International Journal of Forecasting, Elsevier, vol. 36(4), pages 1563-1578.
    12. Liu, Yi & Yang, Menglong & Wang, Yudong & Li, Yongshan & Xiong, Tiancheng & Li, Anzhe, 2022. "Applying machine learning algorithms to predict default probability in the online credit market: Evidence from China," International Review of Financial Analysis, Elsevier, vol. 79(C).
    13. Mohammad Zoynul Abedin & Chi Guotai & Fahmida–E– Moula & A.S.M. Sohel Azad & Mohammed Shamim Uddin Khan, 2019. "Topological applications of multilayer perceptrons and support vector machines in financial decision support systems," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 24(1), pages 474-507, January.
    14. Aslam, Faheem & Hunjra, Ahmed Imran & Ftiti, Zied & Louhichi, Wael & Shams, Tahira, 2022. "Insurance fraud detection: Evidence from artificial intelligence and machine learning," Research in International Business and Finance, Elsevier, vol. 62(C).
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Peón, David & Antelo, Manel & Sun, Yanfei, 2024. "Market competition and strategic interaction in the Spanish FinTech industry," Research in International Business and Finance, Elsevier, vol. 70(PB).
    2. Bouteska, Ahmed & Sharif, Taimur & Abedin, Mohammad Zoynul, 2023. "Volatility spillovers and other dynamics between cryptocurrencies and the energy and bond markets," The Quarterly Review of Economics and Finance, Elsevier, vol. 92(C), pages 1-13.
    3. Bouteska, Ahmed & Sharif, Taimur & Hajek, Petr & Abedin, Mohammad Zoynul, 2024. "Aversion and ambiguity: On the robustness of the macroeconomic uncertainty measure framework," Technological Forecasting and Social Change, Elsevier, vol. 203(C).
    4. Ha, Le Thanh & Bouteska, Ahmed & Sharif, Taimur & Abedin, Mohammad Zoynul, 2024. "Dynamic interlinkages between carbon risk and volatility of green and renewable energy: A TVP-VAR analysis," Research in International Business and Finance, Elsevier, vol. 69(C).
    5. Chi, Guotai & Dong, Bingjie & Zhou, Ying & Jin, Peng, 2024. "Long-horizon predictions of credit default with inconsistent customers," Technological Forecasting and Social Change, Elsevier, vol. 198(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Li, Zhe & Liang, Shuguang & Pan, Xianyou & Pang, Meng, 2024. "Credit risk prediction based on loan profit: Evidence from Chinese SMEs," Research in International Business and Finance, Elsevier, vol. 67(PA).
    2. Chen, Dangxing & Ye, Jiahui & Ye, Weicheng, 2023. "Interpretable selective learning in credit risk," Research in International Business and Finance, Elsevier, vol. 65(C).
    3. Chen, Yan & Zhang, Lei & Zhao, Yulu & Xu, Bing, 2022. "Implementation of penalized survival models in churn prediction of vehicle insurance," Journal of Business Research, Elsevier, vol. 153(C), pages 162-171.
    4. Liu, Zhenkun & Jiang, Ping & De Bock, Koen W. & Wang, Jianzhou & Zhang, Lifang & Niu, Xinsong, 2024. "Extreme gradient boosting trees with efficient Bayesian optimization for profit-driven customer churn prediction," Technological Forecasting and Social Change, Elsevier, vol. 198(C).
    5. Lewlisa Saha & Hrudaya Kumar Tripathy & Tarek Gaber & Hatem El-Gohary & El-Sayed M. El-kenawy, 2023. "Deep Churn Prediction Method for Telecommunication Industry," Sustainability, MDPI, vol. 15(5), pages 1-21, March.
    6. Chi, Guotai & Dong, Bingjie & Zhou, Ying & Jin, Peng, 2024. "Long-horizon predictions of credit default with inconsistent customers," Technological Forecasting and Social Change, Elsevier, vol. 198(C).
    7. Lamrhari, Soumaya & Ghazi, Hamid El & Oubrich, Mourad & Faker, Abdellatif El, 2022. "A social CRM analytic framework for improving customer retention, acquisition, and conversion," Technological Forecasting and Social Change, Elsevier, vol. 174(C).
    8. Sun, Weixin & Zhang, Xuantao & Li, Minghao & Wang, Yong, 2023. "Interpretable high-stakes decision support system for credit default forecasting," Technological Forecasting and Social Change, Elsevier, vol. 196(C).
    9. Aslam, Faheem & Hunjra, Ahmed Imran & Ftiti, Zied & Louhichi, Wael & Shams, Tahira, 2022. "Insurance fraud detection: Evidence from artificial intelligence and machine learning," Research in International Business and Finance, Elsevier, vol. 62(C).
    10. Louis Geiler & Séverine Affeldt & Mohamed Nadif, 2022. "A survey on machine learning methods for churn prediction," Post-Print hal-03824873, HAL.
    11. Christopher Gerling & Stefan Lessmann, 2023. "Multimodal Document Analytics for Banking Process Automation," Papers 2307.11845, arXiv.org, revised Nov 2023.
    12. Matthias Bogaert & Lex Delaere, 2023. "Ensemble Methods in Customer Churn Prediction: A Comparative Analysis of the State-of-the-Art," Mathematics, MDPI, vol. 11(5), pages 1-28, February.
    13. Liu, Zhenkun & Zhang, Ying & Abedin, Mohammad Zoynul & Wang, Jianzhou & Yang, Hufang & Gao, Yuyang & Chen, Yinghao, 2024. "Profit-driven fusion framework based on bagging and boosting classifiers for potential purchaser prediction," Journal of Retailing and Consumer Services, Elsevier, vol. 79(C).
    14. Gobinda Roy & Rajarshi Debnath & Partha Sarathi Mitra & Avinash K. Shrivastava, 2021. "Analytical study of low-income consumers’ purchase behaviour for developing marketing strategy," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 12(5), pages 895-909, October.
    15. Zhao, Yang & Goodell, John W. & Dong, Qingli & Wang, Yong & Abedin, Mohammad Zoynul, 2022. "Overcoming spatial stratification of fintech inclusion: Inferences from across Chinese provinces to guide policy makers," International Review of Financial Analysis, Elsevier, vol. 84(C).
    16. Hossein Havaeji & Thien-My Dao & Tony Wong, 2023. "Supervised Learning by Evolutionary Computation Tuning: An Application to Blockchain-Based Pharmaceutical Supply Chain Cost Model," Mathematics, MDPI, vol. 11(9), pages 1-19, April.
    17. Shi, Yong & Qu, Yi & Chen, Zhensong & Mi, Yunlong & Wang, Yunong, 2024. "Improved credit risk prediction based on an integrated graph representation learning approach with graph transformation," European Journal of Operational Research, Elsevier, vol. 315(2), pages 786-801.
    18. Muhammad Zafran Muhammad Zaly Shah & Anazida Zainal & Taiseer Abdalla Elfadil Eisa & Hashim Albasheer & Fuad A. Ghaleb, 2023. "A Semisupervised Concept Drift Adaptation via Prototype-Based Manifold Regularization Approach with Knowledge Transfer," Mathematics, MDPI, vol. 11(2), pages 1-30, January.
    19. Salman Bahoo & Marco Cucculelli & Xhoana Goga & Jasmine Mondolo, 2024. "Artificial intelligence in Finance: a comprehensive review through bibliometric and content analysis," SN Business & Economics, Springer, vol. 4(2), pages 1-46, February.
    20. Zaid Ahmad Ansari, 2021. "Measuring Consumer Behavior in Banking: Scale Development and Validation," International Journal of Business and Management, Canadian Center of Science and Education, vol. 14(11), pages 263-263, July.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:riibaf:v:65:y:2023:i:c:s0275531923000399. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/ribaf .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.