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Model-Based Digit Analysis for Fraud Detection overcomes Limitations of Benford Analysis

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
Conference or Workshop Item, Bibliographie

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.

Item Type: Conference or Workshop Item
Erschienen: 2012
Creators: Winter, Christian ; Schneider, Markus ; Yannikos, York
Type of entry: Bibliographie
Title: Model-Based Digit Analysis for Fraud Detection overcomes Limitations of Benford Analysis
Language: German
Date: August 2012
Publisher: IEEE Computer Society
Book Title: Availability, Reliability and Security (ARES 2012), Seventh International Conference, August 20–24, 2012, Prague, Czech Republic
Series Volume: E4775
Event Location: Prague, Czech Republic
DOI: 10.1109/ARES.2012.37
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.

Uncontrolled Keywords: Secure Data;Digit Analysis, Benford’s Law, fraud detection
Identification Number: TUD-CS-2012-0152
Divisions: LOEWE
LOEWE > LOEWE-Zentren
LOEWE > LOEWE-Zentren > CASED – Center for Advanced Security Research Darmstadt
Date Deposited: 30 Dec 2016 20:23
Last Modified: 12 Jan 2019 21:21
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