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Modelling bank customer behaviour using feature engineering and classification techniques

Abedin Abedin, Petr Hajek, Taimur Sharif, Md. Shahriare Satu, Md. Imran Khan

Research in International Business and Finance, Volume: 65, Start page: 101913

Swansea University Author: Abedin Abedin

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DOI (Published version): 10.1016/j.ribaf.2023.101913

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 clas...

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Published in: Research in International Business and Finance
ISSN: 0275-5319 1878-3384
Published: Elsevier BV 2023
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URI: https://cronfa.swan.ac.uk/Record/cronfa64243
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first_indexed 2023-09-19T13:36:07Z
last_indexed 2023-09-19T13:36:07Z
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spelling v2 64243 2023-08-31 Modelling bank customer behaviour using feature engineering and classification techniques 4ed8c020eae0c9bec4f5d9495d86d415 Abedin Abedin Abedin Abedin true false 2023-08-31 BAF 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. Journal Article Research in International Business and Finance 65 101913 Elsevier BV 0275-5319 1878-3384 Customer behaviour, Data mining, Feature transformation, Feature selection, Classification techniques 30 4 2023 2023-04-30 10.1016/j.ribaf.2023.101913 http://dx.doi.org/10.1016/j.ribaf.2023.101913 COLLEGE NANME Accounting and Finance COLLEGE CODE BAF Swansea University The authors received no financial support for the research, authorship, and/or publication of this article. 2023-09-20T10:57:07.7206796 2023-08-31T17:45:06.5703503 Faculty of Humanities and Social Sciences School of Management - Accounting and Finance Abedin Abedin 1 Petr Hajek 2 Taimur Sharif 3 Md. Shahriare Satu 4 Md. Imran Khan 5 64243__28585__4dc4fe8b68524010b5b893ec04e3dbf7.pdf 64243.VOR.pdf 2023-09-19T14:35:27.6020081 Output 1693689 application/pdf Version of Record true © 2023 The Author(s). Published by Elsevier B.V. Distributed under the terms of a Creative Commons Attribution 4.0 License (CC BY 4.0). true eng https://creativecommons.org/licenses/by/4.0/
title Modelling bank customer behaviour using feature engineering and classification techniques
spellingShingle Modelling bank customer behaviour using feature engineering and classification techniques
Abedin Abedin
title_short Modelling bank customer behaviour using feature engineering and classification techniques
title_full Modelling bank customer behaviour using feature engineering and classification techniques
title_fullStr Modelling bank customer behaviour using feature engineering and classification techniques
title_full_unstemmed Modelling bank customer behaviour using feature engineering and classification techniques
title_sort Modelling bank customer behaviour using feature engineering and classification techniques
author_id_str_mv 4ed8c020eae0c9bec4f5d9495d86d415
author_id_fullname_str_mv 4ed8c020eae0c9bec4f5d9495d86d415_***_Abedin Abedin
author Abedin Abedin
author2 Abedin Abedin
Petr Hajek
Taimur Sharif
Md. Shahriare Satu
Md. Imran Khan
format Journal article
container_title Research in International Business and Finance
container_volume 65
container_start_page 101913
publishDate 2023
institution Swansea University
issn 0275-5319
1878-3384
doi_str_mv 10.1016/j.ribaf.2023.101913
publisher Elsevier BV
college_str Faculty of Humanities and Social Sciences
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hierarchy_top_id facultyofhumanitiesandsocialsciences
hierarchy_top_title Faculty of Humanities and Social Sciences
hierarchy_parent_id facultyofhumanitiesandsocialsciences
hierarchy_parent_title Faculty of Humanities and Social Sciences
department_str School of Management - Accounting and Finance{{{_:::_}}}Faculty of Humanities and Social Sciences{{{_:::_}}}School of Management - Accounting and Finance
url http://dx.doi.org/10.1016/j.ribaf.2023.101913
document_store_str 1
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description 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.
published_date 2023-04-30T10:57:04Z
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score 11.037319

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