Statement on the Proposed Auditing Standard – Designing and Performing Substantive Analytical Procedures and Amendments to Other PCAOB Standards – Enhancing Investor Trust in Audit Procedures

Remarks as prepared for delivery

Thank you, Chair Williams. Substantive analytical procedure is a specific type of audit procedure used to obtain evidence with respect to financial statement assertions. This type of substantive testing has been in use for decades. In certain cases, substantive analytical procedures can be more effective and efficient than test of details which is another type of substantive testing. With the proliferation and advancement of data analytics methods, opportunities exist to further enhance substantive analytical procedures or expand their use. In particular, I appreciate that this amendment clarifies the use of substantive analytical procedures in this modern data era and helps mitigate the regulatory uncertainty by doing so.

In general, auditors will perform substantive analytical procedures by identifying sufficiently plausible and predictable relationships, developing expectations and thresholds, comparing expectations to recorded company amounts, and evaluating differences. The proposal clarifies the distinction between substantive analytical procedures and other analytical procedures “…in that substantive analytical procedures are designed and performed at a level of precision that is sufficient to respond to assessed risks of material misstatement, alone or in combination with other procedures.” Put another way, substantive analytical procedures include a more precise auditor expectation and are generally designed and performed on a more disaggregated basis than other types of analytical procedures. Hence, clarifying the differences between substantive analytical procedures and other analytical procedures provides auditors with a clear understanding of their appropriate use to obtain the right level of assurance.

Based on the staff research in developing this proposal, some auditors indicated that technology-assisted analysis helps to identify additional plausible and predictable relationships, leading to more substantive analytical procedures and increasing the likelihood to identify potential misstatements. As such, it is possible substantive analytical procedures could be a good candidate to explore the use of artificial intelligence (AI) to improve audit quality. Machine learning techniques could be used to identify plausible and predictable relationships on large amounts of data. This may help auditors expand the use of substantive analytical procedures and design more precise expectations when using substantive analytical procedures during the audit engagement. Even further into the future, I believe that AI could be utilized to efficiently perform tests of details on the entire population of a particular account, possibly in lieu of performing substantive analytical procedures for some accounts. These examples just scratch the surface of the potential uses of AI on audit engagements, and I expect that the profession will identify many valuable use cases in the near future.

In closing, I support this proposal and encourage all stakeholders to provide comment letters on the potential benefits, challenges, and costs associated with this proposal. I would like to express my appreciation to the Office of the Chair, the Office of the Chief Auditor, especially Dima Andriyenko, Dominika Taraszkiewicz, and Donna Silknitter for their leadership on this proposal and their proactive engagement with me and my staff, and the Office of Economic and Risk Analysis, for their contributions to the proposal.

Thank you. Back to you, Chair Williams.