Propension to customer churn in a financial institution: a machine learning approach.

Content Provider	Europe PMC
Author	de Lima Lemos, Renato Alexandre Silva, Thiago Christiano Tabak, Benjamin Miranda
Abstract	This paper examines churn prediction of customers in the banking sector using a unique customer-level dataset from a large Brazilian bank. Our main contribution is in exploring this rich dataset, which contains prior client behavior traits that enable us to document new insights into the main determinants predicting future client churn. We conduct a horserace of many supervised machine learning algorithms under the same cross-validation and evaluation setup, enabling a fair comparison across algorithms. We find that the random forests technique outperforms decision trees, k-nearest neighbors, elastic net, logistic regression, and support vector machines models in several metrics. Our investigation reveals that customers with a stronger relationship with the institution, who have more products and services, who borrow more from the bank, are less likely to close their checking accounts. Using a back-of-the-envelope estimation, we find that our model has the potential to forecast potential losses of up to 10% of the operating result reported by the largest Brazilian banks in 2019, suggesting the model has a significant economic impact. Our results corroborate the importance of investing in cross-selling and upselling strategies focused on their current customers. These strategies can have positive side effects on customer retention.
Related Links	https://europepmc.org/backend/ptpmcrender.fcgi?accid=PMC8898559&blobtype=pdf
ISSN	09410643
Journal	Neural Computing & Applications [Neural Comput Appl]
Volume Number	34
DOI	10.1007/s00521-022-07067-x
PubMed Central reference number	PMC8898559
Issue Number	14
PubMed reference number	35281625
e-ISSN	14333058
Language	English
Publisher	Springer London
Publisher Date	2022-03-06
Publisher Place	London
Access Restriction	Open
Rights License	This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2022
Subject Keyword	Churn Churn prediction Machine learning Random forests Financial services
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence Software