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Winter, C, Schneider, M and Yannikos, Y (2012)

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

Availability, Reliability and Security (ARES 2012), Seventh International Conference, August 20–24, 2012, Prague, Czech Republic. IEEE CS volume E4775, pages 255–261. IEEE Computer Society.

ISSN/ISBN: 978-1-4673-2244-7 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.

@inproceedings{, author = {Winter, Christian AND Schneider, Markus AND Yannikos, York}, title = {Model-Based Digit Analysis for Fraud Detection overcomes Limitations of {Benford} Analysis}, booktitle = {{ARES} 2012}, booksubtitle = {2012 Seventh International Conference on Availability, Reliability and Security, August 20--24, 2012, Prague, Czech Republic}, publisher = {IEEE Computer Society}, pages = {255--261}, month_ = {aug}, year = {2012}, }

Reference Type: Conference Paper

Subject Area(s): Accounting, Statistics