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Geyer, D (2010)

Detecting fraud in financial data sets

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.

@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 = {}, }

Reference Type: Journal Article

Subject Area(s): Economics