Winter, Christian ; Schneider, Markus ; Yannikos, York (2012)
Model-Based Digit Analysis for Fraud Detection overcomes Limitations of Benford Analysis.
Prague, Czech Republic
doi: 10.1109/ARES.2012.37
Konferenzveröffentlichung, Bibliographie
Kurzbeschreibung (Abstract)
Benford Analysis is a statistical method used for detecting financial fraud. It compares the distribution of digits in data with the Benford Distribution. But there are often disadvantages ranging from uncomfortable rates of false positives up to total inapplicability of the method. We identified the inaccurate fit of typical data to the Benford Distribution as reason for these deficits. So we propose to use adaptive distributions of digits instead. For that we introduce a procedure which derives the distribution of digits from a “model” for the distribution of data. The term “model” means an abstract distribution which reflects basic properties of the data. This paper identifies different models and analyzes their relevance and performance. We show that model-based Digit Analysis provides a more reliable and more generally applicable tool for fraud detection to auditors.
| Typ des Eintrags: | Konferenzveröffentlichung |
|---|---|
| Erschienen: | 2012 |
| Autor(en): | Winter, Christian ; Schneider, Markus ; Yannikos, York |
| Art des Eintrags: | Bibliographie |
| Titel: | Model-Based Digit Analysis for Fraud Detection overcomes Limitations of Benford Analysis |
| Sprache: | Deutsch |
| Publikationsjahr: | August 2012 |
| Verlag: | IEEE Computer Society |
| Buchtitel: | Availability, Reliability and Security (ARES 2012), Seventh International Conference, August 20–24, 2012, Prague, Czech Republic |
| Band einer Reihe: | E4775 |
| Veranstaltungsort: | Prague, Czech Republic |
| DOI: | 10.1109/ARES.2012.37 |
| Kurzbeschreibung (Abstract): | Benford Analysis is a statistical method used for detecting financial fraud. It compares the distribution of digits in data with the Benford Distribution. But there are often disadvantages ranging from uncomfortable rates of false positives up to total inapplicability of the method. We identified the inaccurate fit of typical data to the Benford Distribution as reason for these deficits. So we propose to use adaptive distributions of digits instead. For that we introduce a procedure which derives the distribution of digits from a “model” for the distribution of data. The term “model” means an abstract distribution which reflects basic properties of the data. This paper identifies different models and analyzes their relevance and performance. We show that model-based Digit Analysis provides a more reliable and more generally applicable tool for fraud detection to auditors. |
| Freie Schlagworte: | Secure Data;Digit Analysis, Benford’s Law, fraud detection |
| ID-Nummer: | TUD-CS-2012-0152 |
| Fachbereich(e)/-gebiet(e): | LOEWE LOEWE > LOEWE-Zentren LOEWE > LOEWE-Zentren > CASED – Center for Advanced Security Research Darmstadt |
| Hinterlegungsdatum: | 30 Dez 2016 20:23 |
| Letzte Änderung: | 12 Jan 2019 21:21 |
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