When you think of the audit of the future, what words come to mind? Big data, artificial intelligence, virtual workforce, blockchain, professional skepticism? If professional skepticism didn’t make your short list, it should.
Professional skepticism is a foundation of the auditing profession that we need to maintain and evolve to support the audit of the future.
Professional skepticism has always been used to validate information through probing questions, critical assessment of evidence, and attention to red flags and inconsistencies. The auditor’s use of professional skepticism will need to evolve with the use of technological advancements by the profession and by clients. Skepticism will need to be applied to all stages of the audit process, and the auditor will need to be trained to find risks and potential errors that technology-based tools have missed.
For decades, auditors have developed professional skepticism by starting with understanding simple relationships and expectations. For example, agreeing that changes in balance sheet accounts lead to amounts reported in the cash flow statement allowed the auditor to learn how information works together and how financial statement information should flow internally. From this basis, auditors learned to identify anomalies as they progressed through their careers.
When executing an audit, auditors transitioned from predictable analytical procedures, such as those for depreciation expense, to more complex analyses, such as how changes in revenue are tied to external indicators. These experiences fostered the auditor’s professional skepticism by evaluating expectations and identifying risks and results that don’t make sense in the context of all of the relevant information.
But as we continue the evolution into data-driven technology, how does the auditor know that the results are reasonable? In the world of artificial intelligence, auditors will need to understand and be skeptical about how the software is working and learning, as well as understand why outputs from an artificial intelligence tool were possible, and interpret what they mean in the context of individual and unique client situations.
Firms will differ in how they tackle this need to understand and be skeptical about technology. Some firms will build teams of data scientists who analyze data provided by auditors, leaving the auditors to interpret the results. Some firms will train their auditors to run the applications and tools and to understand how to evaluate the data.
In these firms, the auditor will need more technology training and have an interest in the processing of data. Yet other firms will have a combination of both data scientists dedicated to the use of technology and auditors who can use the tools.
Regardless of where the manipulation of data lies, the auditor of the future will need to understand how the data was manipulated and what the results mean, and then be able to apply professional skepticism to identify whether or not the results are reflective of the unique client situation.
This will require auditor training to evolve. For example, in the future, auditors will spend less time learning about how a nonstatistical sample works, and more time learning about the evaluation of the sample inputs and outputs — being skeptical about what is identified and what is not identified.
Accounting and auditing knowledge will continue to be baseline knowledge, but there will be a need for more training on critical thinking, relationships, and processing multiple outcomes. These changes will impact auditors of all experience levels.
Many companies and firms are experimenting with or using optical character recognition to read and evaluate leases for adoption of the new lease accounting standard. This is a perfect example of where professional skepticism is required.
When using OCR tools, the auditor needs to not just accept the results the OCR tool provides, but instead apply professional skepticism in evaluating multiple factors. Auditors need to ask questions, such as: What key terms is the OCR searching for? Do those data queries represent all of the risks of the client’s unique circumstances? What do identified outliers mean, and why were they identified as outliers? How do I know the results are complete and a unique provision with accounting implications that weren’t overlooked?
While we may be able to add confidence that the OCR tool is more accurate at identifying specific terms or items in a contract than a human may be, we must still think about what may have been missed. What might have tripped a skeptical thought in a reader that something was unexpected and needed to be evaluated further?
While we often focus on the challenges of building professional skepticism in a data- and technology-driven world, there are benefits to the changes our technology-driven culture brings that our profession can build on. Because of the vast amounts of data at their fingertips, our children have learned skepticism at much younger ages.
I grew up with bound encyclopedia books that were available in libraries and revered as holders of truthful knowledge about any topic between their covers. I had no skepticism of the accuracy of that information. In contrast, my children have grown up with a variety of websites they can search and phones or virtual assistants they can ask for answers. I’ve had to teach them at a young age to check references and verify that the information shown came from a reputable source. Skepticism is learned as a life skill, and this skill set provides a foundation to build the professional skepticism needed in the auditing profession.
It has never been more critical to invest in people and develop the appropriate processes to drive audit quality through appropriate skeptical behavior. Professional skepticism is foundational to the audit profession and a significant part of what makes us relevant to protect financial statement users and the capital markets today and into the future.
Source: Sara Lord – Accounting Today