Management & Accounting Review 16(2), pp. 73-100.
ISSN/ISBN: Not available at this time. DOI: Not available at this time.
Abstract: Fraud is an illegal activity that does not discriminate. It affects the global economy as well as all types of organizations. Surveys and reports by ACFE, Deloitte, KPMG and NFA confirmed that the public sector is more vulnerable to fraud compared to the private sector. Comments in the Auditor General’s (AG) Report 2012 concluded the same findings. Thus, with respect to fraud, detection, investigation, and preventive measures are extremely important. While anomalies or red flags act as indicators for the auditor, management and other responsible parties to investigate whether there is real fraud, auditing and statistics remain the two primary strategies for detecting fraud. Taking this perspective, Benford’s Law is an advanced digital analysis useful in uncovering anomalies. This paper evaluates 500 accounting data from public sector agencies in Malaysia using theFirst-Digit, Second-Digit, First-Two Digit, First-Three Digit and Last-Two Digit tests. Results show that Benford’s analysis is a credible analytical tool in identifying and detecting suspicious accounts for further scrutiny of fraud incidences in the public sector. This study represents an initial effort to derive a tool to monitor and detect potential fraud incidences or trends, thereby enabling organizations to curb tendencies toward fraud and thus pilot an initiative towards an effective management of fraud risk exposure.
Bibtex:
@article{,
author = {Nooraslinda Abdul Aris and Rohana Othman and Muhamad Anas Mohd Bukhori and Siti Maznah Mohd Arif and Mohamad Affendi Abdul Malek},
title = {Detecting Accounting Anomalies Using Benford’s Law: Evidence from the Malaysian Public Sector},
journal = {Management & Accounting Review (MAR)},
volume = {16},
number = {2},
year = {2017},
pages = {73--100},
url = {http://arionline.uitm.edu.my/ojs/index.php/MAR/article/view/666}
}
Reference Type: Journal Article
Subject Area(s): Accounting, Statistics