Time Series Decomposition for Anomalous E-commerce Transactions

Anton Gerunov, Ilia Atanasov () and George Mengov ()
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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

Bulgarian Economic Papers from Faculty of Economics and Business Administration, Sofia University St Kliment Ohridski - Bulgaria // Center for Economic Theories and Policies at 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.

Keywords: Anomaly detection; time series decomposition; e-commerce; online trade. (search for similar items in EconPapers)
Pages: 8 pages
Date: 2020-12, Revised 2020-12
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Persistent link: https://EconPapers.repec.org/RePEc:sko:wpaper:bep-2020-04

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