Applying visual analytics to fraud detection using Benford's law

Published date01 October 2020
AuthorClarence Goh
Date01 October 2020
Applying visual analytics to fraud detection using
Benford's law
Clarence Goh
School of Accountancy, Singapore
Management University, Singapore,
Clarence Goh, Singapore Management
University, 60 Stamford Road, Singapore
178900, Singapore.
Benford's law has been examined as a useful tool for detecting potential
accounting fraud. In this article, I provide an introduction to Benford's law
and examine how the first digit, second digit, and first-two digits tests in
Benford's law can be employed to detect potential accounting fraud. In addi-
tion, I also highlight, through a worked example, how Tableau, a visual analyt-
ics tool, can be used to perform Benford's law's first-digit test to detect
potential fraud.
Benford's law, fraud, tableau, visual analytics
In 1938, in examining over 20,000 observations from a
diverse range of datasets, including datasets on the areas
of rivers and the atomic weights of element, Frank
Benford, an American Physicist, observed a consistent
pattern where small digits occurred more frequently in
the first position of numbers than larger digits (Benford,
1938). This observation laid the foundation for the math-
ematical tenet that has become known as Benford's law,
which defines the expected frequency that digits appear
in data.
Benford's law has been examined extensively in a
wide range of areas including in mathematics (Hill, 1995;
Newcomb, 1881), the physical sciences (Sambridge,
c, & Jackson, 2010), and business (Giles, 2007;
Judge & Schechter, 2009). Research has also examined
Benford's law in the accounting setting. For example, in
the area of tax accounting, Nigrini (1996) examined how
Benford's law can be used to investigate tax compliance
among tax payers. In the area of audit, Nigrini and
Mittermaier (1997) examined how Benford's law could be
used as an effective aid in analytical procedures in the
planning stage of an audit while Nigrini and Miller
(2009) examined how second-order tests of Benford's law
can be used to detect unusual issues related to data
integrity that might not have been easily detectable using
traditional audit analytical procedures.
Benford's law has also been examined as a useful tool
to detect potential accounting fraud. In a survey con-
ducted on 86 accountants to gain insights into the percep-
tions of fraud detection and prevention methods,
Bierstaker, Brody, and Pacini (2006) found that the
accountants rated digital analysis,which is based on
Benford's law, as the tenth (out of 34) most effective fraud
detection procedure. Consistent with this, various
accounting studies (e.g., Durtschi, Hillison, & Pacini,
2004; Kumar & Bhattacharya, 2007) highlight the appli-
cations of Benford's law as an easy to implement data
mining technique that can effectively determine the
authenticity or otherwise of a set of accounting data.
Beyond the research setting, Benford's law has also
been demonstrated to have been an effective tool for
fraud detection in practice. In June 2009, Bernie Madoff
was sentenced to 150 years in prison for operating the
largest Ponzi scheme in United States history (Nasaw,
2011). While the scale of the US$65 billion scam was eye-
catching, what was perhaps more surprising was that
Madoff managed to run the Ponzi scheme for decades
without getting caught. In the aftermath of the high-
profile case, forensic investigators have been left asking
themselves whether there were any clues that could have
Received: 21 November 2019 Accepted: 3 February 2020
DOI: 10.1002/jcaf.22440
202 © 2020 Wiley Periodicals, Inc. J Corp Acct Fin. 2020;

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