The use of data analytics in auditing is not a new concept but recent advances have revolutionised traditional practices.
An essential part of a robust audit is an auditor’s expectation of how a company has performed. This expectation is then compared to what has actually been achieved by the company when assessing risk and designing the detailed audit approach. What data analytics has changed, and continues to change at a phenomenal rate, is where that expectation is derived from and the accuracy of it.
Ordinarily the expectation would come from an auditor’s knowledge of the business and the sector the client operates in, in addition to conversations with the client. This expectation would then be compared to actual performance with explanations obtained from the client for any deviations. These explanations would then be corroborated through the audit work itself.
Although this sounds simple, in practice these analytical procedures (as required under ISA 520) are commonly criticised by regulators when reviewing audit files. It is easy to understand why this is the case when the client is so involved in the process and corroborating explanations sometimes prove to be futile.
How does AI help?
New technology and access to huge volumes of data are now providing the means for auditors to overcome these problems. The process begins when we seek permission to access the cloud system of the client to monitor and track trends and patterns on a real time, live basis. Running alongside this we make use of data mining tools that are continually scrutinising external data sources. These data sources can range from competitor websites through to population or demographic information and even weather forecasts.
All of this information is then collated into our modelling software and using Artificial Intelligence we are possibly armed with more information and knowledge about the client’s business than the client themselves. Moreover, as these predictive analytics have been derived by the auditor with much less involvement from the client they can provide the auditor and the client with early warning indicators throughout the year as opposed to waiting for the year end.
In short, this predicative technology is turning traditional audit work on its head. Traditional audit methods will be complimented rather than replaced with predictive analytical tools for some time to come but data gathering and mining is becoming much more powerful together with the AI systems that analyse it. As such, audit teams need to evolve to embrace these technologies and hone their skills in these areas.
The speed of technological advancement in these areas does pose the question whether the concept of the statutory audit and the ISAs that prescribe it will remain fit for purpose. The question is when, not if, they will be due a complete overhaul.
What should auditors focus on?
In addition auditors’ responsibilities are increasing and are expected to continue to do so. As examples, it may not be long before the auditor has a duty to report to the shareholders if they believe that covenants will be breached at some point in the future or if weather predictions for a disastrous summer will have severe implications for a particular seasonal business.
The concept of real time reporting and auditing is connected with predictive analytics and is something that many commentators in the profession believe will become a reality in the future. HMRC are already a long way down the track for Making Tax Digital where results will be reported on a quarterly basis and with real time reporting the annual financial statements could become obsolete. Of course if this becomes a reality then not only will auditing standards need a complete overhaul, but so will accounting standards and the Companies Act.
Advances in predictive analytics are something that auditors must embrace given the continuing backlash against the profession whenever there is a corporate failure. If new analytical procedures in auditing are performed correctly they will significantly improve audit quality, making the entire process much more robust and sceptical. Difficult as it may be to keep up with the pace of change, in the end the application of big data and AI to the audit process will only enhance the service we can provide to clients and the wider public.