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Der Einsatz mathematisch-statistischer Methoden in der digitalen Betriebsprüfung

Author

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  • Brähler, Gernot
  • Bensmann, Markus
  • Emke, Anna-Lena

Abstract

In Zeiten von einer Vielzahl spektakulärer Bilanzskandale im In- und Ausland greift die Finanzverwaltung zunehmend im Rahmen der digitalen Betriebsprüfung auf mathematisch-statistische Methoden zurück, welche helfen sollen, dolose Handlungen leichter und effizienter erkennen zu können. Hierzu ist insbesondere das sogenannte Benford's Law zu zählen. Diese Methode macht sich den Umstand zunutze, dass in der Realität Zahlen, die mit der Ziffer "1" beginnen, deutlich häufiger auftreten als Zahlen mit der Anfangsziffer "9". In dem Arbeitsbericht werden zunächst die Grundlagen der digitalen Betriebsprüfung erläutert. Darauf aufbauend setzt sich diese Arbeit grundlegend mit dem Einsatz von Benford's Law sowie auch dem sogenannten Chi-Quadrat-Anpassungstest auseinander. Auch die diesbezügliche Rechtsprechung wird vorgestellt und diskutiert. In einer kritischen Zusammenfassung werden abschießend insbesondere die Grenzen von Benford's Law aufgezeigt.

Suggested Citation

  • Brähler, Gernot & Bensmann, Markus & Emke, Anna-Lena, 2010. "Der Einsatz mathematisch-statistischer Methoden in der digitalen Betriebsprüfung," Ilmenauer Schriften zur Betriebswirtschaftslehre, Technische Universität Ilmenau, Institut für Betriebswirtschaftslehre, volume 4, number 42010.
    • Handle: RePEc:zbw:tuisbw:42010
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    References listed on IDEAS

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    1. Andreas Diekmann, 2005. "Not the First Digit! Using Benford’s Law to Detect Fraudulent Scientific Data," Others 0507001, University Library of Munich, Germany.
    2. Andreas Diekmann, 2007. "Not the First Digit! Using Benford's Law to Detect Fraudulent Scientif ic Data," Journal of Applied Statistics, Taylor & Francis Journals, vol. 34(3), pages 321-329.
    3. George Judge & Laura Schechter, 2009. "Detecting Problems in Survey Data Using Benford’s Law," Journal of Human Resources, University of Wisconsin Press, vol. 44(1).
    4. Quick, Reiner & Wolz, M., 2003. "Benford's Law in deutschen Rechnungslegungsdaten," Publications of Darmstadt Technical University, Institute for Business Studies (BWL) 69992, Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute for Business Studies (BWL).
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