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Latent Variable Models for Integrated Analysis of Credit and Point Usage History Data on Rewards Credit Card System

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  • Ryotaro Shimizu
  • Haruka Yamashita
  • Masao Ueda
  • Ranna Tanaka
  • Tetsuya Tachibana
  • Masayuki Goto

Abstract

Recently, credit cards with point rewards functions (rewards credit cards) are widely used. Credit card companies can collect the users’ usage log data of various stores in multiple industries. The purposes of possessing a credit card varies depending on each user such as to use only the credit function, to use both the credit and point rewards functions, etc. Moreover, credit cards can be used in various situations in users’ lives, and the purchase history of each user is diverse. Focusing on the diversity of both card possessing purposes and purchasing behavior for each user, we propose two latent class models representing these diversities in this research.

Suggested Citation

  • Ryotaro Shimizu & Haruka Yamashita & Masao Ueda & Ranna Tanaka & Tetsuya Tachibana & Masayuki Goto, 2020. "Latent Variable Models for Integrated Analysis of Credit and Point Usage History Data on Rewards Credit Card System," International Business Research, Canadian Center of Science and Education, vol. 13(3), pages 106-106, March.
    • Handle: RePEc:ibn:ibrjnl:v:13:y:2020:i:3:p:106
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    References listed on IDEAS

    as
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    2. Khandani, Amir E. & Kim, Adlar J. & Lo, Andrew W., 2010. "Consumer credit-risk models via machine-learning algorithms," Journal of Banking & Finance, Elsevier, vol. 34(11), pages 2767-2787, November.
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    More about this item

    JEL classification:

    • R00 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General - - - General
    • Z0 - Other Special Topics - - General

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