Improving the predictive accuracy of the cross-selling of consumer loans using deep learning networks

Abstract

Traditionally most cross-selling models in retail banking use demographics information and interactions with marketing as input to statistical models or machine learning algorithms to predict whether a customer is willing to purchase a given financial product or not. We overcome with such limitation by building several models that also use several years of account transaction data. The objective of this study is to analysis credit card transactions of customers, in order to come up with a good prediction in cross-selling products. We use deep-learning algorithm to analyze almost 800,000 credit cards transactions. The results show that such unique data contains valuable information on the customers’ consumption behavior and it can significantly increase the predictive accuracy of a cross-selling model. In summary, we develop an auto-encoder to extract features from the transaction data and use them as input to a classifier. We demonstrate that such features also have predictive power that enhances the performance of the cross-selling model even further.

Publication DOI: https://doi.org/10.1007/s10479-023-05209-5
Divisions: College of Business and Social Sciences > Aston Business School > Operations & Information Management
College of Business and Social Sciences > Aston Business School
Additional Information: Copyright © The Author(s), 2023. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit https://creativecommons.org/licenses/by/4.0/.
Uncontrolled Keywords: Artificial intelligence,Banking,Cross-selling,Deep learning,Targeted marketing,Decision Sciences(all),Management Science and Operations Research
Publication ISSN: 1572-9338
Last Modified: 28 Feb 2024 08:29
Date Deposited: 23 Feb 2023 18:16
Full Text Link:
Related URLs: https://link.sp ... 479-023-05209-5 (Publisher URL)
http://www.scop ... tnerID=8YFLogxK (Scopus URL)
PURE Output Type: Article
Published Date: 2023-02-04
Published Online Date: 2023-02-04
Accepted Date: 2023-01-19
Authors: Boustani, Noureddine
Emrouznejad, Ali (ORCID Profile 0000-0001-8094-4244)
Gholami, Roya
Despic, Ozren (ORCID Profile 0000-0003-1232-1066)
Ioannou, Athina

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