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A data mining approach for bank telemarketing using the rminer package and r tool

Author

Listed:
  • Sérgio Moro
  • Paulo Cortez
  • Raul M. S. Laureano

Abstract

Due to the global financial crisis, credit on international markets became more restricted for banks, turning attention to internal clients and their deposits to gather funds. This driver led to a demand for knowledge about client’s behavior towards deposits and especially their response to telemarketing campaigns. This work describes a data mining approach to extract valuable knowledge from recent Portuguese bank telemarketing campaign data. Such approach was guided by the CRISP-DM methodology and the data analysis was conducted using the rminer package and R tool. Three classification models were tested (i.e., Decision Trees, Naïve Bayes and Support Vector Machines) and compared using two relevant criteria: ROC and Lift curve analysis. Overall, the Support Vector Machine obtained the best results and a sensitive analysis was applied to extract useful knowledge from this model, such as the best months for contacts and the influence of the last campaign result and having or not a mortgage credit on a successful deposit subscription.

Suggested Citation

  • Sérgio Moro & Paulo Cortez & Raul M. S. Laureano, 2013. "A data mining approach for bank telemarketing using the rminer package and r tool," Working Papers Series 2 13-06, ISCTE-IUL, Business Research Unit (BRU-IUL).
    • Handle: RePEc:isc:iscwp2:bruwp1306
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    References listed on IDEAS

    as
    1. Geoffrey M. Hodgson, 2019. "The great crash of 2008 and the reform of economics," Chapters, in: Jonathan Michie (ed.), The Handbook of Globalisation, Third Edition, chapter 28, pages 439-456, Edward Elgar Publishing.
    2. Andrea Gerali & Stefano Neri & Luca Sessa & Federico M. Signoretti, 2010. "Credit and Banking in a DSGE Model of the Euro Area," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 42(s1), pages 107-141, September.
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    JEL classification:

    • C88 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Other Computer Software
    • C38 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Classification Methdos; Cluster Analysis; Principal Components; Factor Analysis
    • M31 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Marketing and Advertising - - - Marketing

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