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Time Series Decomposition for Anomalous E-commerce Transactions

Author

Listed:
  • Anton Gerunov

    (Faculty of Economics and Business Administration, Sofia University St. Kliment Ohridski)

  • Ilia Atanasov

    (Faculty of Economics and Business Administration, Sofia University St. Kliment Ohridski)

  • George Mengov

    (Faculty of Economics and Business Administration, Sofia University St. Kliment Ohridski)

Abstract

Online trading is one of the pillars of the digital economy. The rapid increase of e-commerce transactions has increased the risk exposure of providers and made it virtually impossible to track consumer behaviour by relying on human experts alone. Here we show how time series decomposition can be used to automatically detect suspicious transactions and flag the mout for subsequent actions. The identified outliers have clear business meaning and can be interpreted as peaks in demand produced by idiosyncratic consumer behaviour or by malicious activity. Either way, they deserve sufficient attention and active management.

Suggested Citation

  • Anton Gerunov & Ilia Atanasov & George Mengov, 2020. "Time Series Decomposition for Anomalous E-commerce Transactions," Bulgarian Economic Papers bep-2020-04, Faculty of Economics and Business Administration, Sofia University St Kliment Ohridski - Bulgaria // Center for Economic Theories and Policies at Sofia University St Kliment Ohridski, revised Dec 2020.
    • Handle: RePEc:sko:wpaper:bep-2020-04
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    File URL: https://www.uni-sofia.bg/index.php/eng/content/download/243200/1607914/file/BEP-2020-04.pdf
    File Function: First version, 2020
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    More about this item

    Keywords

    Anomaly detection; time series decomposition; e-commerce; online trade.;
    All these keywords.

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