### 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.

**Bibtex:**

```
@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 = {https://www.tandfonline.com/doi/abs/10.1081/SAC-120028442?cookieSet=1},
}
```

**Reference Type:** Journal Article

**Subject Area(s):** Statistics