Benford's law for integrity tests of high-volume databases: a case study of internal audit in a state-owned enterprise
Héctor Rubén Morales,
Marcela Porporato and
Nicolas Epelbaum
Journal of Economics, Finance and Administrative Science, 2022, vol. 27, issue 53, 154-174
Abstract:
Purpose - The technical feasibility of using Benford's law to assist internal auditors in reviewing the integrity of high-volume data sets is analysed. This study explores whether Benford's distribution applies to the set of numbers represented by the quantity of records (size) that comprise the different tables that make up a state-owned enterprise's (SOE) enterprise resource planning (ERP) relational database. The use of Benford's law streamlines the search for possible abnormalities within the ERP system's data set, increasing the ability of the internal audit functions (IAFs) to detect anomalies within the database. In the SOEs of emerging economies, where groups compete for power and resources, internal auditors are better off employing analytical tests to discharge their duties without getting involved in power struggles. Design/methodology/approach - Records of eight databases of an SOE in Argentina are used to analyse the number of records of each table in periods of three to 12 years. The case develops step-by-step Benford's law application to test each ERP module records using Chi-squared (χ²) and mean absolute deviation (MAD) goodness-of-fit tests. Findings - Benford's law is an adequate tool for performing integrity tests of high-volume databases. A minimum of 350 tables within each database are required for the MAD test to be effective; this threshold is higher than the 67 reported by earlier researches. Robust results are obtained for the complete ERP system and for large modules; modules with less than 350 tables show low conformity with Benford's law. Research limitations/implications - This study is not about detecting fraud; it aims to help internal auditors red flag databases that will need further attention, making the most out of available limited resources in SOEs. The contribution is a simple, cheap and useful quantitative tool that can be employed by internal auditors in emerging economies to perform the first scan of the data contained in relational databases. Practical implications - This paper provides a tool to test whether large amounts of data behave as expected, and if not, they can be pinpointed for future investigation. It offers tests and explanations on the tool's application so that internal auditors of SOEs in emerging economies can use it, particularly those that face divergent expectations from antagonist powerful interest groups. Originality/value - This study demonstrates that even in the context of limited information technology tools available for internal auditors, there are simple and inexpensive tests to review the integrity of high-volume databases. It also extends the literature on high-volume database integrity tests and our knowledge of the IAF in Civil law countries, particularly emerging economies in Latin America.
Keywords: Internal audit; Benford's law; Relational databases; Argentina; Latin America; ERP; Audit data analytics; Audit trail analysis; Inherent risk; State-owned enterprise (SOE) (search for similar items in EconPapers)
Date: 2022
References: Add references at CitEc
Citations: View citations in EconPapers (2)
Downloads: (external link)
https://www.emerald.com/insight/content/doi/10.110 ... d&utm_campaign=repec (text/html)
https://www.emerald.com/insight/content/doi/10.110 ... d&utm_campaign=repec (application/pdf)
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:eme:jefasp:jefas-07-2021-0113
DOI: 10.1108/JEFAS-07-2021-0113
Access Statistics for this article
Journal of Economics, Finance and Administrative Science is currently edited by Nestor U. Salcedo
More articles in Journal of Economics, Finance and Administrative Science from Emerald Group Publishing Limited
Bibliographic data for series maintained by Emerald Support (feeds@emerald.com).