Transforming the audit with data analytics

Accounting firms are beginning to recognize the power of using data analytics in the audit.


When most people think of accounting (and accountants), they think of tight-laced rule followers that crunch numbers. As an accountant myself, I can say that’s mostly true. That said, the industry has gone through waves of changes through the years with scandals such as Enron and Worldcom pushing the industry to adapt new regulation (Sarbanes Oxley) to address auditor-company relationships that are inherently cozy. There has been increased focus on a company’s controls around their financial reporting and specifically on how companies are addressing more prevalent issues like data security, however the auditing firms use of data has, at least in my experience, been extremely limited.

Value Creation

It looks like this is starting to change, as another old school industry begins to adopt measures to use the vast amount of historical data available to them to spot accounting irregularities and fraud through a more comprehensive approach. For example, many key audit tests rely on selecting a sample of transactions to test for accuracy, with the underlying rationale that a random sample will expose the auditor to any potential inaccuracies. For things like test of revenue transactions, it would include the auditor obtaining any supporting documents and manually checking for the date, shipping method, etc. to ensure revenue was recognized in an appropriate way. Data analytics has the potential to streamline this process and utilize the entire data set.

“The transformed audit will expand beyond sample-based testing to include analysis of entire populations of audit-relevant data (transaction activity and master data from key business processes), using intelligent analytics to deliver a higher quality of audit evidence and more relevant business insights.”

              -EY Assurance Insight Hub, Roshan Ramlukan1

Value Capture

In the audit field specifically, hours are continuously tracked and tied to billings for the client and future contract negotiations. Efficiency is thus paramount to both the auditor (to ensure you are achieving the necessary margin) and the client (because they can be billed extra for accounting issues that cause time delays). Thus, if the company has accounting software that automates much of the financial statement generation, and the auditor has audit software that can quickly comb through vast amounts of data to generate better insights faster, everybody wins. Accounting firms are already starting to compete on incorporating data analytics5. BDO has the “BDO Advantage” which is a suite of data analytics tools that is “transforming our audit approach by functioning as the engine that summarizes and presents complete data set outliers and anomalies”6.

Prospects for the Future

Even as early as 2013, the SEC began to implement data analytics through its Accounting Quality Model or “RoboCop” which aims to identify earnings management through including additional discretionary factors (like filings delays for example) in its regression to produce a “score” and then comparing that against similar companies’ SEC filings.2 Further, in 2015 the AICPA and Rutgers Business School announced an initiative that “will facilitate the further integration of data analytics into the audit process, and demonstrate through research how this can lead to advancements in the public accounting profession.”4 However, these announcements were years ago, and as far as I know it remains to be seen how effective RoboCop and these initiatives have been.

While some may argue that the increased use of automation in accounting software and use of machine learning in auditing may eliminate the need for accountants altogether (this Forbes article)3 , I am more skeptical. A company’s financial accounts are wrought with judgments- think about estimating a reserve for obsolete inventory, an algorithm to automate this would still have inherent assumptions about how old that inventory has to be to be considered obsolete. However, increased use of data analytics could significantly aid in testing that assumption by providing insights about how sales of specific inventory trends over time. Regardless, change is on the horizon and both public accounting firms and companies that are slow to adapt will be left behind.




Alexa, what should I wear today?

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