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

Noureddine Boustani; Ali Emrouznejad; Roya Gholami; Ozren Despic; Athina Ioannou

doi:10.1007/s10479-023-05209-5

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

Noureddine Boustani, Ali Emrouznejad^*, Roya Gholami, Ozren Despic, Athina Ioannou

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

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.

Original language	English
Journal	Annals of Operations Research
Early online date	4 Feb 2023
DOIs	https://doi.org/10.1007/s10479-023-05209-5
Publication status	E-pub ahead of print - 4 Feb 2023

Bibliographical note

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/.

Keywords

Artificial intelligence
Banking
Cross-selling
Deep learning
Targeted marketing

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10.1007/s10479-023-05209-5

N Boustani et al_Improving predictive accuracy cross selling consumer loans deep learning networks
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/.
Final published version, 602 KBLicence: CC BY 4.0

Cite this

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title = "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{\textquoteright} 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.",

keywords = "Artificial intelligence, Banking, Cross-selling, Deep learning, Targeted marketing",

author = "Noureddine Boustani and Ali Emrouznejad and Roya Gholami and Ozren Despic and Athina Ioannou",

note = "Copyright {\textcopyright} 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/.",

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AU - Emrouznejad, Ali

AU - Gholami, Roya

AU - Despic, Ozren

AU - Ioannou, Athina

N1 - 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/.

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Y1 - 2023/2/4

N2 - 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.

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

Abstract

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Keywords

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