WIT Press


Data Mining Education For External Auditors

Price

Free (open access)

Paper DOI

10.2495/DATA050541

Volume

35

Pages

5

Published

2005

Size

292 kb

Author(s)

A. M. Young

Abstract

The auditing profession is suffering a credibility crisis in the eyes of the public due to the high profile collapses of many large companies and indeed one of the largest accounting firms in the world, amid fraud and professional breaches. Regulators are strengthening requirements and the auditing profession is once again working towards \“educating” the public about what they should be expecting from the auditors. A revolution is seen to be necessary whereby external auditors, via the support of regulators, apply data mining techniques to improved financial reporting requirements in the context of electronic data collection. The public would be provided with more of their expectations including better predictions of fraud, errors and company failures. Keywords: expectation gap, exploration, deployment. 1 Introduction The auditing profession is embroiled in another credibility crisis or what has been described by Kahn [5] after Enron’s bankruptcy as a \“crisis in confidence”. Tomasic [8] notes that \“on the heels of corporate collapses, we are in many ways witnessing a fundamental crisis of trust”. It is proposed in this article that current external auditing practices are outdated, insufficiently rigorous and are contributing to the expectation gap between what the public expects from the auditors and what they are delivering. Data mining techniques are suggested to close this expectation gap and revolutionise how auditors undertake their work. It is acknowledged that legislative support would be required to ensure suitable data is captured and stored to make the changes work. Whilst the Australian auditing standards pay some lip service to introductory data mining techniques in its standard on analytical procedures (AUS 512) [4], little has been done to support the application of the techniques and employ more rigorous and suitable techniques.

Keywords

expectation gap, exploration, deployment.