How accounting firms are driving growth with AI

Commoditization has been a hot topic in the audit industry for some time now. We’ve heard from many prominent voices that commoditization is real – the invisible and somewhat mean-hand of the market driving prices down for audit as the only differentiator is price. But at the same time, we have been seeing average audit fees rise faster than inflation. For example, for a basket of US-listed entities, average audit fees increased from $6m in 2002 to over $15m by 2020, significantly outpacing inflation over the same period. 

We’ve also seen significant evidence that price is not the only factor when choosing an audit firm. Of course, the expertise of the engagement team comes out number one, but increasingly the technological capabilities that firms can bring to bear are playing a key role.

Generally, it seems that firms that are responding faster to society’s rising expectations for efficient audits are reaping the benefits. Being able to win larger, more important, and more profitable audit clients is a key strategic advantage for these firms. However, at the larger end of the market, firms that are now unable to talk to their next-generation or data-driven audit are left with price as a remaining primary lever they must use to differentiate.

Of course, this is not always the case. A significant portion of buyers in the audit is still looking purely for the lowest price. The additional trust and credibility that auditors bring to financial statements today does not seem to carry much weight for this lowest price-focused buyer. For many such stakeholders, it is not easy to differentiate between a high and low-quality audit product. It is up to the audit firm to demonstrate that differentiation, and if undercutting is still a key strategy for an audit firm, then there is still a way to use technology to display how the level of assurance is not compromised despite the low price.

Thinking about the value proposition for a data-driven audit throughout the customer lifecycle is key to demonstrating this value.

Clear outcomes

Before speaking to value, having a clear set of outcomes for a data-driven audit approach is essential. Understanding how the service you are providing will change helps you effectively sell the value of this approach internally and externally. Defining these outcomes and understanding the differentiated value proposition that your firm offers is key.

1. Market facing

Even before you start talking to a prospective client, the firm must be communicating its outcomes in implementing a data-driven approach. Whether this is a lessened focus and effort on low risk-areas, more informed conversations with clients, or direct value-add, it’s critical to emphasize these factors to your market. Creating case studies, dedicating a section of your website for innovation, providing examples in newsletters, and aligning to accounting technology standards such as ISA315, SAS142, and SAS145 are great ways to raise awareness.

2. Proposals and tendering

Allowing your innovation and data team to have input into the proposal process is a step above, both in the form of a dedicated section in the proposal template as well as a process that allows the engagement team to demo the analytics capabilities during meetings. This demonstration could be done with demo-designed data or real data, depending on the importance of the proposal. It also offers technical and data-savvy staff an opportunity to get involved in this discussion with the client.

3. Planning and fieldwork

This audit stage centers around evidence gathering and learning from the auditor. Using data to deepen the engagement team’s understanding of the client can help with a far more productive conversation earlier in the audit process, but it is key that you’re demonstrating how you came to the conclusions you came to. Using visualizations during these conversations is a fantastic way to achieve this, and even better if you can navigate an analytics tool on the fly to adapt to where the conversation is going during the planning meeting.

4. Completion

Ultimately, it’s at this stage that the client sees most of the outputs for the audit process. As a result, we’ve found that including descriptive analytics is a fantastic way to add context to audit findings and cement a perception of value with your client. 

Where to go from here? The pace of change in audit is accelerating, and there is a growing number of technologies that auditors can leverage in various ways. These are opening up strategic opportunities for firms to differentiate – but to do so means that they must be willing to take a different approach from their peers. So whether it is changing where the team is focusing on the audit, how they communicate with their client, or adding net new insights to the post-audit reporting, implementing technology is becoming mandatory as a differentiator and a means to deliver a more efficient audit.



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How AI is changing expectations for auditors

CFO using the MindBridge API for auditing automation

There are some ways that AI is becoming obvious in our daily lives, be it in the driverless technology found in cars or in the tailored content selected for you by streaming services. Many of us have received a reassuring text message from our banks, verifying that the recent payment was you and not some fraudster. You can thank the watchful eye of anomaly detection algorithms that have been keeping our money and accounts safe.  

 Businesses are similarly coming to rely on machine learning to inform critical decision-making. Increasingly, machine learning is finding its place throughout organizations, from customer retention to marketing and finance. Assurance and audit are no different. As the value of these technologies becomes clear and society expects more, pressure builds on auditors to improve. 

 

Reasonable to ask for more assurance 

 The standards have required auditors to deliver a ‘reasonable’ level assurance, a level that is not absolute but rather a high level determined, really, by a shared sense of best practices. Over the last few years, we have seen auditors adapt to the way they are working, and the way they demonstrate their quality. This is largely in response to the market; buyers are becoming more sophisticated. “Audit committees require audit firms to provide extensive evidence to demonstrate their quality. It has become normal to test a firm’s technology, including its data analysis capabilities,” noted PwC in 2018. 

 This is a trend that we are seeing in multiple markets, with a top US firm commenting that “our client’s technology and data availability plays a role in drivers of change. The more clients are using technology, their expectation is elevated on our use of technology.” What constitutes a reasonable level of assurance is changing. 

 Regulators are aware of the positive impact that new technologies can deliver, with the PCAOB foreseeing that “the future of audit will be able to provide a greater level of “reasonable assurance” as auditors may be able to examine 100 per cent of a client’s transactions.” 

 This view is also backed up in a large review of the UK Audit market performed by Sir Donald Brydon. ” As such technologies become widespread in use, stretching beyond journal testing, they will clearly have an impact on the cost of audit (less human checking) and on the depth of testing that will be possible” noted Brydon. 

 Cost savings and the search for efficiencies have often been key drivers of technology adoption in audit for audit partners, but the importance of demonstrating higher levels of audit quality has become clear. The fact that BDO calls out technology as a key aspect in their recent win of SAP as an audit client demonstrates this fact. 

 

AI: An enabler for risk-based auditing 

 Whilst the PCAOB speaks of transaction scoring as a technology of the future, firms are leveraging MindBridge’s 100% risk scoring across the US today. By scanning transactions using a variety of techniques, auditors are both better able to assess risk, and better able to find those risky and unusual transactions. This translates to an audit with less ticking-and-tying, and a greater focus on what matters. It allows fewer audit staff to get through more information and provides greater assurance at the end of it all. 

 An example of an audit algorithm in action is MindBridge’s “outlier detection.” This category of algorithm identifies unusual financial patterns, helping fulfil the requirement of ISA 240, which sets an expectation for auditors to look for unusual activities. An additional benefit of outlier detection is that its methodology consists of unsupervised machine learning, meaning algorithms are not trained or taught on specific data. 

 This overcomes bias in data analysis, with reviewed transactions (i.e., the general ledger of companies), identifying what is normal for the audited entity and separating out what is empirically unusual activity.  

 The unsupervised methods of outlier detection allow for data to be analyzed and anomalies drawn out without requiring training on similar entities. It can also be applied to all types of organizations, irrespective of their size or industry.  

 While outlier detection is effective for detecting new activity and outliers in data, it does not have a prior or pre-existing understanding of accounting processes. It is our belief that there is still a role for the expert system in the context of risk scoring for audit. MindBridge’s “Expert Score” is an example, it’s an indicator that flags transactions based on a database of pre-existing rules determined to be unusual. Write-offs directly between cash and expense will consistently get flagged by Expert Score. 

 Expert Score has recently been enhanced by looking at the prevalence of financial flows in the data selected to take part in our curated learning process. Unusual transaction flows are studied and documented before being added to the Expert Score rule base. 

 

Demonstrating quality: key to growth 

 By leveraging these techniques and changing the profile of work, the firms that are most successfully implementing MindBridge are driving success in the market and growth. By speaking to the value throughout the customer lifecycle, these firms are ensuring that the customer sees the value of working with them. 

Expand your expertise, watch this short webinar from MindBridge here and learn how firms are adopting AI to drive growth. 

Audit standards don’t need to change. Our approach to innovation does.

Audit standards - Innovation in the approach

If there is one thing that COVID-19 taught us, the audit industry can implement wide-reaching change at scale. In a matter of weeks we witnessed audit partners driving the adoption of video calling software, new headsets appearing, and people’s home desks being upgraded. It wasn’t long before palm-tree laden beaches were appearing as backgrounds on Zoom calls, something I couldn’t have imagined a few years ago.

Following the onset of COVID-19, many of these tools became a lifeline for accounting firms, who had to quickly change the way they operate, shifting to remote digital practices.

Despite the fast pace of change for these operational parts of audit, little has changed for the core ways that auditors generate assurance. Whilst the impact and take-up of data analytics and artificial intelligence is accelerating, there are still some oft-quoted roadblocks to wide-spread reliance on these techniques. “As a profession and including our regulators, we must up the ante on making analytics a mandatory part of the audit process” recently noted Becky Shields, Head of Digital Transformation at Moore Kingston Smith. 

Often the finger is pointed at the audit standards or regulators as major blockers of innovation. “They do not allow or require new technologies” is a perspective I’ve heard a few times, and comes in a few guises. Either in pointing specifically to the audit standards, or in a general comment that such innovation isn’t required by the PPC, a checklist commonly used by audit firms in the US.

Is it true that the standards and regulations themselves are standing the way of innovation? Or do we as an industry struggle to see value from innovation and change the way we think about assurance?

 

The audit standards – fit for purpose

Let’s take a quick look at the audit standards. All the checklists and expectations for a good quality audit start with the standards, after all.

The Public Company Accounting Oversight Board (PCAOB), which regulates auditors of publicly traded companies in the United States, recently explored whether there is a need for changes to standards, guidance, or other regulatory actions in light of technological advancements like AI. 

In their report, the PCAOB stated that its “auditing standards are not precluding or detracting from firms’ ability to use technology-based tools in ways that could enhance audit quality (for example, to perform more thorough and better-informed risk assessments).” The PCAOB did, however, acknowledge that the current standards “do not explicitly encourage” the use of technology-based audit tools, which isn’t a surprising revelation. 

Encouragingly, the PCAOB praised the capabilities of technology-based audit tools, noting that they can “enhance the auditor’s ability to efficiently and effectively analyze larger volumes of data, and in more depth, than when using manual audit technologies alone.” The Board also stated that technology-based tools could assist auditors with addressing the requirements in PCAOB risk assessment standards, a view that we at MindBridge share.

The PCAOB is not alone in its assessments. Across the pond, the UK’s Financial Reporting Council (FRC) recently consulted stakeholders regarding the use of technology to enhance audit quality. In a December 2020 report outlining its findings, the FRC noted: “Respondents also agreed that whilst additional application material and guidance would be beneficial, the current assurance model and audit standards do not represent a significant impediment to the development and deployment of technology in audit.” The report also noted that nearly all the respondents agreed that technology use could “significantly improve audit quality.”

These are views that are largely shared by the international standards setters at the IAASB. In a June 2020 update from the Technology Working Group they stated “The ISAs are flexible in terms of how audit procedures may be performed – manually, involving the use of , or a combination of both.” Whilst there has been fewer statements of this kind from the AICPA, the convergence between the AICPA and the ISA’s will likely mean that those following the American Clarified Statements on Auditing Standards will be in a similar position.

Whilst not a look at the standards, Chartered Professional Accountants of Canada (CPA Canada) has issued publications which encourage their members to adopt new technologies into their practices to stay relevant: “Firms left behind during the ADA implementation are more likely to see their situation deteriorate further with each new wave of emerging technologies. Therefore, there is a pressing need for accounting firms to develop and implement a long-term ADA adoption and use strategy that will allow them to continue with the next generation of analytics using Big Data and prepare for the use of analytics related to the .”

It’s the opinion of the standards-setters, whether in the US or internationally, that audit standards are fit for purpose. Reading the standards themselves, this isn’t hugely surprising.The standards are principles based and lay out a framework under which firms are free to rely on data-driven techniques for evidence. Regulators, methodology providers, and firms must interpret these standards to create their own workflows.

If the standards are fine, perhaps it is the regulators that are standing in the way of innovation?

 

Speak softly and carry a big stick – the role of the regulator

Whilst often the standards setters and regulators are the same body, it’s worth making the distinction between these two roles. The standards are high level and principles based. The regulators, whether it is the PCAOB, the FRC, or any of the others, are entrusted with the interpretation and enforcement of the standards. The goal is to maintain a level of audit quality amongst audit firms, and to make sure the audit is delivering on its mission to society as a whole.

Largely, regulators focus on after-the-fact punitive measures to ensure audit quality. Whether through the reputational damage caused by releasing their public inspection results, fines or action taken against individual audit partners, regulators and the regulatory process focus their attention on completed audit files.

When looking at these audit files, auditors and regulators are applying a shared sense of what good looks like. There is a cultural norm for audit files. These expectations change from market to market, and from sector to sector. The audit file for a large bank will look nothing like the audit file for the small non-profit down the road. With new technologies changing the way that assurance is generated, the expectations of those that enforce quality have to keep pace with the rest of the market. How do regulators know what good looks like when assessing a revolutionary new technique?

As firms innovate, they change their audit file and assurances models. They may do in a way that does not fit with the regulator’s or peer’s expectations for what a good quality audit looks like. This dynamic makes firms rightly nervous about putting together an audit file that looks nothing like other files in the same sector. The fear of the regulator is a real deterrent to trying new things – why stick your head above the parapet when the checklist is accepted.

We often see auditors request for greater levels of guidance from the regulators. It’s a theme that was echoed in much of the recent research from the regulatory bodies, and would be surely welcome. But the regulators are in a difficult position, as innovation often requires an agile, experimental and iterative approach. How can the regulator’s possibly create guidance for techniques that are under drastic evolution from one year to the next?

 

Safe spaces and dialogue

Regulators across the US, Canada, and the UK have a major role to play in encouraging and fostering innovation. Dialogue with auditors is a good place to start, and in our experience regulators are open to speaking with firms innovating on their audit approach. Even better would be the establishment of safe spaces for firms and the regulators to collaborate on innovative techniques. It’s an approach that the UK financial regulator, the FCA, has implemented well with its Digital Sandbox and TechSprints

This kind of collaboration between firms and regulators would allow both to explore new ways of creating assurance, and to receive feedback in a low pressure environment. It’s a classic approach for exploring new ways of thinking. It also has the potential to enable firms to innovate on real data; another key requirement to truly exploring new ways to generate assurance. Given the opportunity for artificial intelligence to change the way that the industry works, now seems like as good a time as any.

There’s also an opportunity for firms to create their own safe-spaces for innovation. Asking a separate team to try a new technique or technology alongside the traditional checklist is one such approach. This allows a side by side comparison, and an opportunity to assess what level of assurance both are generating. It provides a place where auditors can ask ‘what do I actually learn about my client from doing it this way’, without the regulators breathing down their neck. The firm can always take these experimental working papers to their regulator if they want feedback.

 

Where’s the carrot? Firms need incentives to innovate

In the end, firms need to gain a competitive advantage in the market from innovation. If they didn’t, there is no point in them innovating. Often innovation drives improved audit quality, so the firm must translate this into bigger and better client wins, or eliminate work they were previously conducting. 

Publicly released inspection reports from the regulators are one key public facing measure of audit quality, but it is hard for everyone involved to tell whether their particular audit is one that is deficient in some way. The large delay between the audit work happening and the release of these public results also undermines the value that these inspections play. 

Whilst the regulators could consider ways to make the audit process and its outputs more transparent, firms also have the opportunity to talk about the results of their audit much more openly with their clients and prospective clients. Greater transparency into the audit process and its outputs make it more likely that innovative firms will reap the rewards from pushing the boundary. 

The capabilities that technology-based tools can bring to the audit process mean that clients expect more from their auditors than ever before. Rather than a hindsight perspective, clients want a “forward-looking view”, with deeper insights that add value. Clients no longer want a financial checklist, according to Audit 2025: The Future is Now, a report from Forbes Insights and KPMG. Firms that maintain the “if it’s not broken, don’t fix it” attitude about their methodology are increasingly losing ground in the market.

“Clients want their auditors to take things to the next level to weigh and prioritize risks and opportunities based on their in-depth knowledge of the organization, its controls and processes, so more-informed decisions can be made to guide the organizations forward,” the report stated. Openly presenting the findings, particularly aided by visualisations, cements the auditor’s position as a fountain of knowledge about a company.

That forward-looking expectation also means that more clients expect their auditor to stay current with technology and adapt as new tools become available. As a result, clients “rightly believe that technology has improved the quality of audit,” noted the KPMG/Forbes Insights report. 

So it’s no surprise that 78% of KPMG/Forbes Insights respondents believe auditors should use more sophisticated technologies for gathering data, and nearly 80% think auditors should analyze bigger samples.

 

A wider range of skills needed for tomorrow’s audit

The pace of change is accelerating in the audit market, and it seems clear that today’s innovations will define how we generate assurance in tomorrow’s audit.

The skill set of auditors has to expand in order to facilitate the audit process of tomorrow. That shift necessitates an adjustment in attitudes and mindsets to benefit the industry at large. In order for these changes to be successful, change needs to happen at every level of the industry, from the junior associates joining our firms, through the partners and regulators. 

Among the sought-after skills that clients want their auditor to have, the KPMG/Forbes Insights report found that 67% of clients are looking for increased technology skills, while 66% want better communication skills, 65% for critical thinking skills, and 59% for investigative financial skills. They are not looking for the old-school, number-crunching accountant any more. Considering they are obsessed with learning patterns and identifying anomalies in data, It is time for us to learn from what the data scientists are doing.

Given the massive skills and talent gap that the audit industry is facing, It’s also worth pointing out that teaching these skills is an opportunity for firms to keep their best staff around for longer.  Many like-minded organizations, including MindBridge, have also taken an interest in preparing tomorrow’s accountants and auditors with the tools and knowledge they need to succeed before they reach the workforce.

 

The data revolution is an opportunity for auditors

The audit sector is known for being stuck in its ways. We’re the number-crunching, adhering to regulations, checklist-oriented type. Sometimes, we follow those traits to a fault. As technology proliferates and data volumes grow, modern businesses are putting data front and center. This introduces both risk and complexity, but also an opportunity for data savvy auditors to provide leadership in the data driven world.

By encouraging more curiosity, creativity, and experimenting across the industry, we can hopefully adjust attitudes and mindsets regarding technology-based tools and audit standards. 

The future of audit is being reshaped by technology, there is no dismissing that. However, the industry must ensure it is keeping pace with the broader economy. Instead of looking for excuses to resist change, collectively, we need to find ways around any obstacles so that the accounting industry becomes a leader in innovation, resilience, and agility. We can’t wait to be told by regulators what tools to use ​​ it’s crucial to change attitudes and embrace the changes that are happening now. 

A study by the Harvard Business Review summed it up perfectly: “If more and more companies methodically dismantle blockers to innovation and encourage employees to experiment, perhaps we will finally see the gap close between leaders’ innovation goals and reality.”

From person to machine: The role of audit data analysis

a path to success illustration

An auditor can view themselves as many different personas, but up until recently ‘audit data analyst’ was not one of those personas. The truth is, I’ve always thought that this was a bit of an unfair position for auditors.

For as long as I have been involved in the accounting and finance industries, auditors have been drawing conclusions about large populations of data by using random sampling or a particular strategic lens. What has always impressed me is how a seasoned partner can spot an error deep in the numbers just by looking at the primary statements.

While strong audit data analysts are still applying their incredible talents, many auditors are beginning to leverage new audit technologies to streamline their analysis methods.

Embracing new data analysis techniques during audits

What’s most interesting today is how professional data analytics techniques from other fields are being combined with traditional audit approaches. This has enabled new ways for auditors to interrogate, understand, and gain assurance during data journal entry analysis or general ledger analysis. This ranges from basic aggregation techniques such as calculating proof in totals and creating moderately complex data visualizations to machine learning techniques designed to spot unusual patterns.

Using AI-powered technology such as Ai Auditor, audit data analysis appears to be entering a new phase of progression. AI audit solutions leverage machine learning to analyze general ledgers and deliver automated risk scores across all transactions and financial data.

How the role of the data analyst is evolving with AI technology

Learning how to properly implement these technologies to evolve auditing processes and general ledger analysis requires consideration. However, I have seen many instances where these cutting-edge audit analysis technologies were able to flag truly interesting items such as the purchase of a Porsche for a company director. When one experiences these types of results with AI audit software, it’s easy to believe that the future is here for journal entry analysis. And, long gone is the day of manual data segmentation in Excel.

Many of these AI audit solutions work by building some expectation of normal within a specific pool of data. The many breakthroughs that are still occurring in data science and artificial intelligence will likely improve the machine’s sense of nuance. As more accurate models involve higher levels of complex analysis, we must, as an industry, weigh this fact against our need for explainable results.

This is not the end for analyzing audit data. Some auditors will always carry the persona of data analysts because they are inherently great at decoding data. However, perhaps that role is evolving alongside new AI audit technology. And perhaps, that’s a good thing.

Want to learn more about how auditors are using AI?