IDEAS home Printed from https://ideas.repec.org/a/gam/jjrfmx/v15y2022i6p269-d840414.html
   My bibliography  Save this article

A Machine Learning Framework towards Bank Telemarketing Prediction

Author

Listed:
  • Stéphane Cédric Koumétio Tékouabou

    (Center of Urban Systems (CUS), Mohammed VI Polytechnic University (UM6P), Hay Moulay Rachid, Ben Guerir 43150, Morocco
    Laboratory LAROSERI, Department of Computer Science, Faculty of Sciences, Chouaib Doukkali University, El Jadida 24000, Morocco)

  • Ştefan Cristian Gherghina

    (Department of Finance, Bucharest University of Economic Studies, 6 Piata Romana, 010374 Bucharest, Romania)

  • Hamza Toulni

    (EIGSI, 282 Route of the Oasis, Mâarif, Casablanca 20140, Morocco
    LIMSAD Laboratory, Faculty of Sciences Ain Chock, Hassan II University of Casablanca, Casablanca 20100, Morocco)

  • Pedro Neves Mata

    (ISCAL-Instituto Superior de Contabilidade e Administraçäo de Lisboa, Instituto Politécnico de Lisboa, Avenida Miguel Bombarda 20, 1069-035 Lisboa, Portugal
    Microsoft (CSS-Microsoft Customer Service and Support Department), Rua Do Fogo de Santelmo, Lote 2.07.02, 1990-110 Lisboa, Portugal)

  • Mário Nuno Mata

    (ISCAL-Instituto Superior de Contabilidade e Administraçäo de Lisboa, Instituto Politécnico de Lisboa, Avenida Miguel Bombarda 20, 1069-035 Lisboa, Portugal)

  • José Moleiro Martins

    (ISCAL-Instituto Superior de Contabilidade e Administraçäo de Lisboa, Instituto Politécnico de Lisboa, Avenida Miguel Bombarda 20, 1069-035 Lisboa, Portugal
    Business Research Unit (BRU-IUL), Instituto Universitário de Lisboa (ISCTE-IUL), 1649-026 Lisboa, Portugal)

Abstract

The use of machine learning (ML) methods has been widely discussed for over a decade. The search for the optimal model is still a challenge that researchers seek to address. Despite advances in current work that surpass the limitations of previous ones, research still faces new challenges in every field. For the automatic targeting of customers in a banking telemarketing campaign, the use of ML-based approaches in previous work has not been able to show transparency in the processing of heterogeneous data, achieve optimal performance or use minimal resources. In this paper, we introduce a class membership-based (CMB) classifier which is a transparent approach well adapted to heterogeneous data that exploits nominal variables in the decision function. These dummy variables are often either suppressed or coded in an arbitrary way in most works without really evaluating their impact on the final performance of the models. In many cases, their coding either favours or disfavours the learning model performance without necessarily reflecting reality, which leads to over-fitting or decreased performance. In this work, we applied the CMB approach to data from a bank telemarketing campaign to build an optimal model for predicting potential customers before launching a campaign. The results obtained suggest that the CMB approach can predict the success of future prospecting more accurately than previous work. Furthermore, in addition to its better performance in terms of accuracy (97.3%), the model also gives a very close score for the AUC (95.9%), showing its stability, which would be very unfavourable to over-fitting.

Suggested Citation

  • Stéphane Cédric Koumétio Tékouabou & Ştefan Cristian Gherghina & Hamza Toulni & Pedro Neves Mata & Mário Nuno Mata & José Moleiro Martins, 2022. "A Machine Learning Framework towards Bank Telemarketing Prediction," JRFM, MDPI, vol. 15(6), pages 1-19, June.
  • Handle: RePEc:gam:jjrfmx:v:15:y:2022:i:6:p:269-:d:840414
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1911-8074/15/6/269/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1911-8074/15/6/269/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Georgios Marinakos & Sophia Daskalaki, 2017. "Imbalanced customer classification for bank direct marketing," Journal of Marketing Analytics, Palgrave Macmillan, vol. 5(1), pages 14-30, March.
    Full references (including those not matched with items on IDEAS)

    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. Omar H. Fares & Irfan Butt & Seung Hwan Mark Lee, 2023. "Utilization of artificial intelligence in the banking sector: a systematic literature review," Journal of Financial Services Marketing, Palgrave Macmillan, vol. 28(4), pages 835-852, December.
    2. Marco Vriens & Nathan Bosch & Chad Vidden & Jason Talwar, 2022. "Prediction and profitability in market segmentation typing tools," Journal of Marketing Analytics, Palgrave Macmillan, vol. 10(4), pages 360-389, December.
    3. Jinping Hu, 2023. "Customer feature selection from high-dimensional bank direct marketing data for uplift modeling," Journal of Marketing Analytics, Palgrave Macmillan, vol. 11(2), pages 160-171, June.
    4. Stéphane C. K. Tékouabou & Ștefan Cristian Gherghina & Hamza Toulni & Pedro Neves Mata & José Moleiro Martins, 2022. "Towards Explainable Machine Learning for Bank Churn Prediction Using Data Balancing and Ensemble-Based Methods," Mathematics, MDPI, vol. 10(14), pages 1-16, July.
    5. Pritha Ghosh & Subrata Saha & Shamindra Nath Sanyal & Swati Mukherjee, 2021. "Positioning of private label brands of men’s apparel against national brands," Journal of Marketing Analytics, Palgrave Macmillan, vol. 9(3), pages 210-227, September.

    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:gam:jjrfmx:v:15:y:2022:i:6:p:269-:d:840414. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.