Journal of Business and Economics Research 8 (7), pp.75-83.
ISSN/ISBN: Not available at this time. DOI: Not available at this time.
Abstract: An important need of 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 is discussed. This paper examines how a sample of students falsify financial numbers. The paper shows that they fail to imitate Benford’s law and that there are cheating behaviour patterns coherent with previous empirical studies.
Bibtex:
@article {,
AUTHOR = {Dominique Geyer},
TITLE = {Detecting fraud in financial data sets},
JOURNAL = {Journal of Business and Economics Research},
YEAR = {2010},
VOLUME = {8},
NUMBER = {7},
PAGES = {75--83},
DOI = {},
URL = {https://pdfs.semanticscholar.org/a205/bc874d0fb282af2b1b2a43bf956ceed713d4.pdf},
}
Reference Type: Journal Article
Subject Area(s): Economics