The Application of Benford's Law in Forensic Accounting: An Analysis of Credit Bureau Data

Author(s):  
Syed Azfar Hussain
2019 ◽  
Vol 49 (3) ◽  
pp. 548-570 ◽  
Author(s):  
Heng Qu ◽  
Richard Steinberg ◽  
Ronelle Burger

Benford’s Law asserts that the leading digit 1 appears more frequently than 9 in natural data. It has been widely used in forensic accounting and auditing to detect potential fraud, but its application to nonprofit data is limited. As the first academic study that applies Benford’s Law to U.S. nonprofit data (Form 990), we assess its usefulness in prioritizing suspicious filings for further investigation. We find close conformity with Benford’s Law for the whole sample, but at the individual organizational level, 34% of the organizations do not conform. Deviations from Benford’s law are smaller for organizations that are more professional, that report positive fundraising and administration expenses, and that face stronger funder oversight. We suggest improved statistical methods and experiment with a new measure of the extent of deviation from Benford’s Law that has promise as a more discriminating screening metric.


Significance ◽  
2007 ◽  
Vol 4 (2) ◽  
pp. 81-83 ◽  
Author(s):  
Kuldeep Kumar ◽  
Sukanto Bhattacharya

2008 ◽  
Vol 25 (2) ◽  
pp. 152-150 ◽  
Author(s):  
Sukanto Bhattacharya ◽  
Kuldeep Kumar

2021 ◽  
Vol 1 (1) ◽  
pp. 50-60
Author(s):  
Edin Glogić ◽  
Zoran Jasak

Abstract Forensic accounting in scientific sense is the part of accounting that assumes the practice of scientific techniques and methods in conducting investigations and detecting criminal activities in financial statements, business statements and companies. One such tool in detecting anomalies in accounting records is the Benford’s Law, which gives the expected pattern of digit frequencies in numeric data sets according to their position in numbers. Because of this property, Benford’s law has become a significant forensic tool for the detection of anomalies, especially in financial business. One of the most important sources is account turnover data in the observed period, which has a debt and credit side. A classic way of analyzing these liabilities is to merge them and then look for a pattern of leading digits. In such approach, it is not possible to properly determine the source of anomalies, which are a guide to deeper analysis. For such purposes, a variant of the Hosmer-Lemeshow test is designed.


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