A Machine Learning Framework towards Bank Telemarketing Prediction

Stéphane Cédric Koumétio Tékouabou, Ştefan Gherghina, Hamza Toulni, Pedro Neves Mata, Mário Nuno Mata and José Moleiro Martins
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Stéphane Cédric Koumétio Tékouabou: Center of Urban Systems (CUS), Mohammed VI Polytechnic University (UM6P), Hay Moulay Rachid, Ben Guerir 43150, Morocco
Hamza Toulni: EIGSI, 282 Route of the Oasis, Mâarif, Casablanca 20140, 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
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

JRFM, 2022, vol. 15, issue 6, 1-19

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.

Keywords: artificial intelligence; data mining; heterogeneous data; machine learning; performance optimisation; predictive modelling; targeted marketing; bank telemarketing (search for similar items in EconPapers)
JEL-codes: C E F2 F3 G (search for similar items in EconPapers)
Date: 2022
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