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High-Cardinality Categorical Attributes and Credit Card Fraud Detection

Author

Listed:
  • Emanuel Mineda Carneiro

    (Sao Paulo State Technological College (Faculdade de Tecnologia—Fatec), Sao Jose dos Campos 12247-014, Brazil
    Brazilian Aeronautics Institute of Technology (Instituto Tecnologico de Aeronautica—ITA), Sao Jose dos Campos 12228-900, Brazil)

  • Carlos Henrique Quartucci Forster

    (Brazilian Aeronautics Institute of Technology (Instituto Tecnologico de Aeronautica—ITA), Sao Jose dos Campos 12228-900, Brazil)

  • Lineu Fernando Stege Mialaret

    (Federal Institute of Education, Science and Technology of Sao Paulo (Instituto Federal de Sao Paulo—IFSP), Jacarei 12322-030, Brazil)

  • Luiz Alberto Vieira Dias

    (Brazilian Aeronautics Institute of Technology (Instituto Tecnologico de Aeronautica—ITA), Sao Jose dos Campos 12228-900, Brazil)

  • Adilson Marques da Cunha

    (Brazilian Aeronautics Institute of Technology (Instituto Tecnologico de Aeronautica—ITA), Sao Jose dos Campos 12228-900, Brazil)

Abstract

Credit card transactions may contain some categorical attributes with large domains, involving up to hundreds of possible values, also known as high-cardinality attributes. The inclusion of such attributes makes analysis harder, due to results with poorer generalization and higher resource usage. A common practice is, therefore, to ignore such attributes, removing them, albeit wasting the information they provided. Contrariwise, this paper reports our findings on the positive impacts of using high-cardinality attributes on credit card fraud detection. Thus, we present a new algorithm for domain reduction that preserves the fraud-detection capabilities. Experiments applying a deep feedforward neural network on real datasets from a major Brazilian financial institution have shown that, when measured by the F-1 metric, the inclusion of such attributes does improve fraud-detection quality. As a main contribution, this proposed algorithm was able to reduce attribute cardinality, improving the training times of a model while preserving its predictive capabilities.

Suggested Citation

  • Emanuel Mineda Carneiro & Carlos Henrique Quartucci Forster & Lineu Fernando Stege Mialaret & Luiz Alberto Vieira Dias & Adilson Marques da Cunha, 2022. "High-Cardinality Categorical Attributes and Credit Card Fraud Detection," Mathematics, MDPI, vol. 10(20), pages 1-23, October.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:20:p:3808-:d:943211
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    References listed on IDEAS

    as
    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.
    2. Aderemi O. Adewumi & Andronicus A. Akinyelu, 2017. "A survey of machine-learning and nature-inspired based credit card fraud detection techniques," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 8(2), pages 937-953, November.
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    Cited by:

    1. Alexey Ruchay & Elena Feldman & Dmitriy Cherbadzhi & Alexander Sokolov, 2023. "The Imbalanced Classification of Fraudulent Bank Transactions Using Machine Learning," Mathematics, MDPI, vol. 11(13), pages 1-15, June.

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