AI and technology

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.

For more information on how top accounting firms are driving growth with AI,
register now for our upcoming webinar with Cherry Bekaert.

This webinar will cover how Cherry Bekaert’s success with leveraging advanced data analytics for risk discovery has continued to offer more significant insights and more efficient audits.

Webinar: Improving audit efficiency by reducing sampling: The value of data-driven assurance

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.