Artificial intelligence advocates speak of a time to come when these systems will be capable of auditing 100% of a company’s financial transactions. These visionaries foresee the day when AI will enable auditing that is a continuous and real-time process, not a prolonged exercise requiring large teams of accountants working overtime after the close of a fiscal year.
But is AI in auditing a good idea? Or do we even have a choice — is it just part of the data-focused technology wave that all companies must embrace?
We’ve approached our development of AI in auditing from the ground up to ensure that human values remain at the core of our audit work and that auditors have the tools they need to continue to improve audit quality. Here’s what we’ve learned:
—The details matter. Data acquisition is at the heart of auditing. Auditors need to obtain raw business data before they can “audit” it – check the accuracy and alignment of data sets like purchase orders, billing, receivables, payments, expenses, and compensation. Further, auditors regularly consider external data sources to understand risks, plan the audit, and confirm company assertions. To incorporate AI into their audit methodology, auditors need to understand systematically how those data sets are structured; how they differ from one industry, client, or source system; and how to transform the data reliably for use in our solutions.