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Geyer, CL and Williamson, PP (2004)

Detecting Fraud in Data Sets Using Benford's Law

Communications in Statistics: Simulation and Computation 33(1), pp. 229-246.

ISSN/ISBN: 0361-0918 DOI: 10.1081/SAC-120028442

Abstract: An important need of governments, for tax purposes, and corporations, for internal audits, is the ability to detect fraudulently reported financial data. Benford's Law is a numerical phenomenon in which sets of data that are counting or measuring some event follow a certain distribution. A history of the origins of Benford's Law is given and the types of data sets expected to follow Benford's Law are presented. A statistical detection method developed by Nigrini to test whether or not a particular data set follows Benford's Law is discussed; the purpose of this method is to detect fraud in data sets such as tax data. An obvious alternative to Nigrini's method using a classical approach is given as well as two Bayesian approaches to this problem. A simulation study is performed to compare the different approaches.

@article{, author = {Christina Lynn Geyer and Patricia Pepple Williamson}, title = {Detecting Fraud in Data Sets Using Benford's Law}, journal = {Communications in Statistics - Simulation and Computation}, volume = {33}, number = {1}, pages = {229--246}, year = {2004}, publisher = {Taylor & Francis}, doi = {10.1081/SAC-120028442}, URL = {}, }

Reference Type: Journal Article

Subject Area(s): Statistics