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Analysis of Resampling Techniques on Predictive Performance of Credit Card Classification

Author

Listed:
  • Maira Anis
  • Mohsin Ali
  • Shahid Aslam Mirza
  • Malik Mamoon Munir

Abstract

Credit card fraud detection has been a very demanding research area due to its huge financial implications and rampant applications in almost every area of life. Credit card fraud datasets are naturally imbalanced by having more legitimate transaction in comparison to the fraudulent transactions. Literature represents numerous studies that are aimed to balance the skewed datasets. There are two major techniques of resampling in balancing these sets i.e. under-sampling and oversampling. However both under-sampling and oversampling techniques suffer from their own set of problems that can seriously affect the performance of classifiers that have been inducted for credit card studies in the past. Thus to accelerate detection of credit card fraud, it is very important to implement the strategy that could possibly provide better predictive performance. This paper attempts to find out what resampling technique can work best under different skewed distributions for the domain of credit card fraud detection.

Suggested Citation

  • Maira Anis & Mohsin Ali & Shahid Aslam Mirza & Malik Mamoon Munir, 2020. "Analysis of Resampling Techniques on Predictive Performance of Credit Card Classification," Modern Applied Science, Canadian Center of Science and Education, vol. 14(7), pages 1-92, July.
    • Handle: RePEc:ibn:masjnl:v:14:y:2020:i:7:p:92
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    References listed on IDEAS

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    1. Juszczak, Piotr & Adams, Niall M. & Hand, David J. & Whitrow, Christopher & Weston, David J., 2008. "Off-the-peg and bespoke classifiers for fraud detection," Computational Statistics & Data Analysis, Elsevier, vol. 52(9), pages 4521-4532, May.
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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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