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Deep Ensemble Classifiers and Peer Effects Analysis for Churn Forecasting in Retail Banking

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Advances in Knowledge Discovery and Data Mining (PAKDD 2018)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10937))

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Abstract

Modern customer analytics offers retailers a variety of unprecedented opportunities to enhance customer intelligence solutions by tracking individual clients and their peers and studying clientele behavioral patterns. While telecommunication providers have been actively utilizing peer network data to improve their customer analytics for a number of years, there yet exists a very limited knowledge on the peer effects in retail banking. We introduce modern deep learning concepts to quantify the impact of social network variables on bank customer attrition. Furthermore, we propose a novel deep ensemble classifier that systematically integrates predictive capabilities of individual classifiers in a meta-level model, by efficiently stacking multiple predictions using convolutional neural networks. We evaluate our methodology in application to customer retention in a retail financial institution in Canada.

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Acknowledgements

This research was partially supported by NSF IIS 1633331 & 1633355, NSF DMS 1736368, and Simons Foundation. The work of V. Lyubchich was supported by Mitacs Accelerate Internship Awards with contributions from Temenos Canada.

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Authors and Affiliations

  1. Southern Methodist University, Dallas, TX, USA

    Yuzhou Chen

  2. University of Texas at Dallas, Richardson, TX, USA

    Yulia R. Gel

  3. University of Maryland Center for Environmental Science, Cambridge, MD, USA

    Vyacheslav Lyubchich

  4. Temenos, Surrey, BC, Canada

    Todd Winship

Authors
  1. Yuzhou Chen
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  2. Yulia R. Gel
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  3. Vyacheslav Lyubchich
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  4. Todd Winship
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Corresponding author

Correspondence to Yulia R. Gel .

Editor information

Editors and Affiliations

  1. Deakin University, Geelong, Victoria, Australia

    Dinh Phung

  2. National Chiao Tung University, Hsinchu City, Taiwan

    Vincent S. Tseng

  3. Monash University, Clayton, Victoria, Australia

    Geoffrey I. Webb

  4. Japan Advanced Institute of Science and Technology, Nomi, Ishikawa, Japan

    Bao Ho

  5. University of Melbourne, Melbourne, Victoria, Australia

    Mohadeseh Ganji

  6. University of Melbourne, Melbourne, Victoria, Australia

    Lida Rashidi

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Chen, Y., Gel, Y.R., Lyubchich, V., Winship, T. (2018). Deep Ensemble Classifiers and Peer Effects Analysis for Churn Forecasting in Retail Banking. In: Phung, D., Tseng, V., Webb, G., Ho, B., Ganji, M., Rashidi, L. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2018. Lecture Notes in Computer Science(), vol 10937. Springer, Cham. https://doi.org/10.1007/978-3-319-93034-3_30

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  • DOI: https://doi.org/10.1007/978-3-319-93034-3_30

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-93033-6

  • Online ISBN: 978-3-319-93034-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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