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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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