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Modelling bank customer behaviour using feature engineering and classification techniques

Author

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  • 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
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    References listed on IDEAS

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    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. 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).
    3. 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.
    4. 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.
    5. 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).
    6. 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).
    7. 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.
    8. 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.
    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. 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.
    11. 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.
    12. 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.
    13. 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.
    14. 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).
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