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

Noureddine Boustani, Ali Emrouznejad (), Roya Gholami, Ozren Despic and Athina Ioannou
Additional contact information
Noureddine Boustani: Aston University
Ali Emrouznejad: University of Surrey
Roya Gholami: University of Illinois Springfield
Ozren Despic: Aston University
Athina Ioannou: University of Surrey

Annals of Operations Research, 2024, vol. 339, issue 1, No 23, 613-630

Abstract: 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.

Keywords: Cross-selling; Deep learning; Artificial intelligence; Banking; Targeted marketing (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s10479-023-05209-5 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:annopr:v:339:y:2024:i:1:d:10.1007_s10479-023-05209-5

Ordering information: This journal article can be ordered from
http://www.springer.com/journal/10479

DOI: 10.1007/s10479-023-05209-5

Access Statistics for this article

Annals of Operations Research is currently edited by Endre Boros

More articles in Annals of Operations Research from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().