Peyton Hahn is Sr. Manager - Audit Innovation at Fidelity Investments
“Chaos isn’t a pit, chaos is a ladder.” – Littlefinger (from Game of Thrones).
Opportunity in a challenging time
While the happenings of the audit industry may not be as dramatic or – hopefully – as cut-throat as the competition for the Iron Throne, it is not a stretch to say that many are facing mounting (and dare I say…unprecedented) challenges in attracting and retaining talent.
On one hand, we are several months into the “Great Resignation,” with turnover wreaking havoc on audit departments and increasing risk for their clients. On the other, it’s no secret that the economy has been struggling and some companies (especially tech companies), are instituting hiring freezes and even cutting headcount.
Still, for most companies, the competition for talent has never been fiercer. But what does this mean for audit analytics practitioners? How can audit leaders see the opportunity within this challenging time and climb the ladder?
Defining audit analytics
Let’s first be clear about what I mean by “audit analytics," because it’s been a buzzword in the industry for years. The AICPA defines audit analytics as “The science and art of discovering and analyzing patterns, identifying anomalies and extracting other useful information in data underlying or related to the subject matter of an audit through analysis, modeling and visualization for the purpose of planning or performing the audit.”1 To cast the broadest net possible, I will simply define audit analytics as any means of using technology to perform or enhance audit activities.
Analytics have been of interest in the audit industry for decades – early examples include the creation of the audit command language (“ACL”) being invented in the 1970s before being launched as an enterprise software in the 1980s2. Since then, interest in the area has been increasing as technology improves, gets cheaper, and becomes more accessible to potential practitioners. The Institute of Internal Auditor (“IIA”) standards even state that “In exercising due professional care, internal auditors must consider the use of technology-based audit and other data analytics techniques.” Despite the increased interest, many audit analytics struggle to create and sustain a successful analytics program. According to the 2022 IIA Pulse survey, technology is the second-most desired area to spend extra funding (after additional staff). Of the different technology areas, the number one area of focus is data analytics software3. The current moment offers an opportunity to make good on the promise that analytics have long offered. There are three major driving forces that can move the needle:
Cross Pollination
Turnover risk
Increasing salaries (and decreasing tech costs)
Cross Pollination
The other side of resigning from a job is beginning a new one, which means auditors are cross-pollinating new departments, bringing with them their experience and expertise. As a result, audit departments are more likely to be adding auditors to their ranks that have had some exposure to audit analytics. This “new blood” can help bring ideas and energy to any analytics practice, so audit leaders should try to find ways to tap into it.
Even auditors who may not have been practitioners can still be a major influence by spreading their positive experiences of analytics implementations that they witnessed in previous roles. As Malcolm Gladwell expressed it: “Ideas and products and messages and behaviors spread just like viruses do.”
Turnover Risk
The less fun side of experiencing the Great Resignation is being part of the team trying to carry on after a key resource has quit a job. Turnover risk is likely top of mind for any business leader and Internal Audit is no exception. Analytics can play a part in addressing turnover risk in several ways.
First, establishing an analytics function can offer a career path for auditors who may have a long-term career interest in programming and technology. Creating opportunities for associates to work in this area could be a strong incentive and change-of-pace for those who might otherwise leave to pursue jobs in tech.
Second, using analytics to perform repetitive, menial tasks (which are often the low-hanging fruit) allows for your auditors to focus on more interesting, high value work. If you are a leader, this might be an area to discuss openly with your team – there may be hours spent mindlessly scrolling through PDFs, formatting work papers, or searching for evidence that could easily be gathered programmatically.
Finally, when turnover inevitably does happen, automation can lighten the blow. To automate a process (correctly), you must understand it well. As a result, automation can be a byproduct of knowledge transfer between auditors and the analytics team. Once the understanding of the process has been documented and automated, the activity can continue to be performed even when the auditor closest to the process has moved on. In this way, analytics, and automation – when done well – can play a role in mitigating the impact of turnover risk.
Increasing Salaries (and Decreasing Tech Costs)
Across industries, we are once again experiencing automation as a response to the increasing cost of labor. It is not the first time – history is full of examples that teach us that whenever wages increase, automation is likely to follow.
According to a report from Glassdoor.com, people who are seeking new jobs expect an average increase of $9,000 over their current salary4. The same report states that this has been steadily increasing since the beginning of the pandemic, and now exceeds pre-pandemic measurements. Naturally, these increased demands put hiring managers in a difficult position to balance pay equity within their own teams against the need to fill open positions. While salaries are increasing, the cost of technology has been decreasing for decades.
Storage is not the only aspect of technology that is becoming more affordable, either. Many analytics tools have free or inexpensive licensing that cover most use cases. If you are looking to code, the top two data science programming languages – Python and R – have integrated development environments that you can download for free and will have you programming within minutes. If you are looking for point-and-click analytics tools, there are software packages like KNIME or JMP that can lower the learning curve for analytics practitioners who may not feel as comfortable developing from scratch. The barrier of entry for trying out new analytics tools is as low as it has ever been.
Analytics tools have been made even more accessible by the increased availability of quality online education materials. Right now, I can take a Python course with 60 hours (about 2 and a half days) of lecture for $126 (or, my personal favorite course that is a more manageable 10 lecture hours for the same price). If I am interested in learning about how to use analytics within the context of audit, ACI Learning and other websites have courses designed specifically for this industry. Becoming tech-savvy no longer requires a computer science degree – it is a matter of time, dedication, and a decent internet connection.
Why does this matter? When budgeting decisions need to be made, the calculus on the tradeoff between using manual labor and investing in automation has changed significantly, and it will continue to trend in this direction unless there is a shakeup in the labor market.
The Time is Now
No matter where you are in your analytics journey, the time is now to take the next step – or the first one. If your department is struggling to fill open positions, consider casting a wider net by creating additional dedicated analytics positions. As turnover ravages operations for many companies, have an open discussion about scaling back the audit plan temporarily and investing that time into starting (or expanding) the audit analytics function. It is important to keep in mind that this too shall pass, and we must find a way to make the most of the current situation. Ask yourself and your leadership this question: with business processes becoming more digital and data-driven, what will audit look like 10 years from now?The leaders who make the investments in analytics today will “climb the ladder” and be the best-positioned auditors of tomorrow. The “Great Resignation” has created the conditions that encourage leaders to make the investment.
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