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The rise of e-commerce, driven by digital business models, has made online shopping easier and more convenient. While this benefits both businesses and shoppers, the increase in online transactions also attracts fraudsters. In this work, we address the identification of fraudulent transactions using a machine learning approach. For this purpose we analyze the DataCo Supply Chain Dataset and apply a two-step preprocessing workflow to identify non-informative features. The predictive power of the resulting dataset is tested and compared with classification models induced after applying a feature selection approach. Results suggest that a small number of factors are helpful to build highly predictive models capable of identifying fraudulent transactions.

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Fraudulent Transactions Identification Using a Machine Learning Approach

  • 会议论文

  • 发表日期：

    2025年

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  • Silvia Vázquez-Noguera,
  • Miguel García-Torres,
  • Sebastián Grillo,
  • Francisco Gómez-Vela,
  • Katherin Arrua,
  • Ricardo R. Palma,
  • Lorena Andrea Bearzotti

摘要

The rise of e-commerce, driven by digital business models, has made online shopping easier and more convenient. While this benefits both businesses and shoppers, the increase in online transactions also attracts fraudsters. In this work, we address the identification of fraudulent transactions using a machine learning approach. For this purpose we analyze the DataCo Supply Chain Dataset and apply a two-step preprocessing workflow to identify non-informative features. The predictive power of the resulting dataset is tested and compared with classification models induced after applying a feature selection approach. Results suggest that a small number of factors are helpful to build highly predictive models capable of identifying fraudulent transactions.