View Complete Reference

Geyer, CL and Williamson, PP (2004)

Detecting Fraud in Data Sets Using Benford's Law

Communications in Statistics: Simulation and Computation 33(1), 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.


Bibtex:
Not available at this time.


Reference Type: Journal Article

Subject Area(s): Statistics