Q&A: The future of audit is fueled by technology

One major change is just the amount of data that external auditors need to conduct audits. It can be hard to both acquire the needed data and manage it – and it can be even harder to pull out actionable insights. Additionally, the area of audit is undergoing a transformation, with the potential to leverage data and artificial intelligence (AI) to drive innovation. The use of advanced technologies such as business intelligence and data analysis can provide deeper insights and a more efficient audit process and can support audit quality. With these capabilities, we can audit entire data sets rather than just a small subset, producing new insights and supporting the overall quality of the audit.

With advanced technology and tools, we also can let the data drive us to where the risk is. For example, we’d typically price test by selecting a sample and reviewing invoices. Using data analysis, we can automate price testing for the entire inventory population and highlight where purchases are dramatically different from the norm. I think it can be likened to using a metal detector to help us find the needle in the haystack.

At Crowe it’s about more than the technology itself. It’s about how we have deconstructed our audits so we can get work assigned to the right people with the right skills, centralizing or automating tasks with the aid of technology so that our people are able to focus on areas requiring their auditor judgment. It’s about the work we are doing to upskill our people to use these new techniques and methodologies, connecting knowledge to our core solutions so that our people have access to that knowledge as they do their work.

The Crowe approach has always been a combination of technology and people. Data might be “the new oil,” but at Crowe, it’s about how data and technology empower our people to provide high-quality audits.

What do organizations being audited and the auditors doing the auditing expect from technology these days?

Organizations expect us to use technology to help transform the audit experience to support audit quality.

Performing a data-driven audit might lead to conversations that can provide value to clients about their business based on risks identified by the auditor – which can be audit-related but also operational.

Technology can find patterns that the client might not have seen before. Let’s take our previous example of price testing. In the past when we sampled, we’d need the client to pull all the related invoices – and no one knew how many invoices that would be. But when we’re able to review and extract the raw data and run an analysis, we can isolate anomalies and ask for specific invoices – saving both us and the client time.

Our engagement management tools also offer efficiencies. Instead of exchanging mailed letters, we have a complete portal (Crowe Secure Information Exchange) where both we and the clients can manage the project, delegate tasks, and track the status. This allows for improved engagement management and alleviates a variety of pain points.

We believe our clients expect us to continue to use technology to deliver differentiated value.

Like the rest of the world these days, I am obsessed with AI content generation technologies. The use of AI and machine learning technology in the audit industry is expected to have a significant impact in the near future. One example of this is the use of language generation models that can quickly summarize and analyze legal documents, such as leases. Additionally, training these models on specific document types and industry terminology can further enhance their capabilities. I see this being a very powerful co-pilot for auditors, expediting the review of any legal document, refining or creating first drafts of work papers, and doing simple math and comparisons.

Another promising area is the development of technology that can assist with typical audit procedures such as risk assessment and substantive analytical procedures. These tools use data and advanced algorithms to identify patterns and potential issues, allowing auditors to focus on areas of higher risk and increasing the efficiency of the audit process.

Of course, we need to crawl before we can walk, so it is important to note that the implementation of such technology is still in early stages, and before fully utilizing these tools, we need to focus on gathering and organizing data to power more advanced AI and machine learning algorithms. We already are seeing significant progress in this area, and we expect that over time, the use of AI in auditing will support audit quality.