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Robust Monitoring of Time Series with Application to Fraud Detection

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  • Rousseeuw, Peter
  • Perrotta, Domenico
  • Riani, Marco
  • Hubert, Mia

Abstract

Time series often contain outliers and level shifts or structural changes. These unexpected events are of the utmost importance in fraud detection, as they may pinpoint suspicious transactions. The presence of such unusual events can easily mislead conventional time series analysis and yield erroneous conclusions. A unified framework is provided for detecting outliers and level shifts in short time series that may have a seasonal pattern. The approach combines ideas from the FastLTS algorithm for robust regression with alternating least squares. The double wedge plot is proposed, a graphical display which indicates outliers and potential level shifts. The methodology was developed to detect potential fraud cases in time series of imports into the European Union, and is illustrated on two such series.

Suggested Citation

  • Rousseeuw, Peter & Perrotta, Domenico & Riani, Marco & Hubert, Mia, 2019. "Robust Monitoring of Time Series with Application to Fraud Detection," Econometrics and Statistics, Elsevier, vol. 9(C), pages 108-121.
    • Handle: RePEc:eee:ecosta:v:9:y:2019:i:c:p:108-121
    DOI: 10.1016/j.ecosta.2018.05.001
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    Cited by:

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    6. Gian Marco Paldino & Bertrand Lebichot & Yann-Aël Le Borgne & Wissam Siblini & Frédéric Oblé & Giacomo Boracchi & Gianluca Bontempi, 2024. "The role of diversity and ensemble learning in credit card fraud detection," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 18(1), pages 193-217, March.
    7. Maria E. Frey & Hans C. Petersen & Oke Gerke, 2020. "Nonparametric Limits of Agreement for Small to Moderate Sample Sizes: A Simulation Study," Stats, MDPI, vol. 3(3), pages 1-13, August.
    8. Lucio Barabesi & Andrea Cerioli & Domenico Perrotta, 2021. "Forum on Benford’s law and statistical methods for the detection of frauds," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 30(3), pages 767-778, September.

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