Corporate scandals and failures due to fraud have resulted in significant financial
losses to shareholders. Recently, there has been an increase in the occurrence of
such events both globally and within South Africa. More importantly, these events have occurred in companies where satisfactory audit opinions have been issued. As a result, concerns regarding the integrity and reliability of Independent Audit opinions underpins the need for this study. Therefore, the purpose of this study was to identify a suitable tool for detecting fraud or error in financial statements. Benford’s Law, the tool used, claims that digits in numeric data are distributed according to expected frequencies (Nigrini & Mittermaier, 1997). A quantitative analysis of a sample of known and suspected incidences of fraudulent financial reporting was analysed. First, second and first-two
digit Benford’s tests were performed and the Mean Absolute Deviation (MAD),
Kolmogorov–Smirnov statistic (KS) and Z-Statistic were used for assessing
conformance. Inconsistencies and limitations were identified in the results of the KS and Z-Statistics as well as the usefulness of first-two digit test. Overall, the MAD statistic confirmed that suspected and fraudulent financial data does not conform to Benford’s Law for all companies when applying the first and second digit tests.
Mini-dissertation (MBA)--University of Pretoria, 2019.