Assessing Internal Audit with Text Mining

Abstract

Recently internal controls, corporate governance and risk management have received a great deal of attention. Regarding internal control, several research studies address the issue of internal audit quality. Noteworthy, according to Sarbanes–Oxley (SOX) the internal controls over financial reporting are assessed by the auditors and the management. In the present study, we assess internal controls over financial reporting by employing Text Mining techniques. We analyse the annual reports of 133 publicly traded Greek Companies. The textual parts of the annual reports that refer to internal audit mechanism are extracted. We adopt a Vector Space model and the term-document matrix records the occurrence frequencies of the terms. By applying feature selection, a set of significant keywords, which are used as predictors, is extracted. The Linear Regression model developed explains the variance of the data and highlights significant predictors. The model manages to successfully assess the internal audit function. By performing PCA, major underlying procedures and concepts related to internal audit quality are revealed. Inspite of the undoubted importance of the assessment of internal audit, no previous attempt has been made to assess internal audit and to extract internal audit information from corporate disclosures by using Text Mining techniques. Our results can be useful to internal and external auditors, managers, company decision-makers, regulators and researchers.