Why AI in Audit Works for Reasonable Assurance

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Artificial intelligence is transforming audit by providing auditors with the ability to ingest, analyze and report on vast amounts of ledger data so that firms can offer a higher degree of risk assurance.

In his book, The Fourth Industrial Revolution, founder and executive chairman of the World Economic Forum, Klaus Schwab, states that a shift will occur when 30 percent of corporate audits will be conducted by AI, which 75 percent of people surveyed expected to happen by the year 2025. It’s already underway, with the Big Four firms making significant investments into AI projects and regulatory bodies discussing its impacts.

How will AI change the audit landscape? By creating a new definition of reasonable assurance.

Improving Assurance with AI

Since it’s impossible to assert with certainty that an event, such as a financial misstatement, will or will not occur, auditors have operated under the philosophy of “reasonable assurance” for decades. With too much data, too little time, and limitations inherent in the internal controls of clients, auditors cannot provide absolute and guaranteed assurance on their findings, requiring the use of professional judgement to fill in the gaps.

Both auditors and clients operate on the principle that audit evidence is more persuasive than conclusive, and are guided by the Generally Accepted Auditing Standards (GAAS) to provide reasonable assurance that the assessed financial statements are free of material misstatements. With AI, the growing amount of client data and scarce amounts of an auditor’s time are less of a concern as the work is offloaded to applications that operate at much higher speeds than people.

Additionally, AI improves the testing of ledger data by going beyond traditional sampling techniques to identify misstatements based on risk analysis rather than traditional audit rules.

Big Data, No Problem

Previously, the only feasible method for analyzing large quantities of ledger data was to take small random samples and forego the time and effort necessary to pore through it all. For years, this has been deemed “good enough” and both auditors and clients have accepted the fact that anomalies outside the sample set are potentially missed.

This is known as sampling risk, or the risk of reaching a different conclusion by examining a small set of the data rather than the entire data set. This risk increases when the potential misstatements are rare, including those buried within larger patterns of activity.

Some modern financial analytics tools can relieve a few of these limitations but they often require technical skills and programming knowledge that are not part of an auditor’s core skill set. This makes adopting these tools a cumbersome and potentially time-consuming process. AI offers auditors the ability to focus their judgement and expertise on the entire set of data without requiring these specialized skills or data science expertise.

Identifying New Types of Risk

The very nature of AI means that it is constantly learning about and identifying patterns in data. As these systems learn more about client transactions, they are able to analyze secondary data and cross-correlate hundreds of variables to establish the transactions that seem correct, and those that require further investigation by a person.

One example is a classical AI method called “expert system,” drawing on the knowledge of real-world professionals and practices to identify unusual patterns. By working with audit practitioners to understand suspicious and risky transactions, an expert system can be built that knows hundreds of account interactions and their associated concerns. These methods democratize human knowledge at scale and can be quickly run on large amounts of client data to highlight issues that might be missed by traditional methods.

AI can also identify rare items using empirical methods, leveraging the science of determining what is usual versus unusual. Called “unsupervised learning,” or inferring patterns in data without having labelled or known outcomes, these methods can detect outliers in transactions without bias or history which allows the data speak for itself.

These methods go beyond traditional auditing rules or what a human is able detect. Both methods are especially important when it comes to the PCAOB guidance on material misstatements:

“The auditor also should look to the requirements in paragraphs .66–.67A of AU sec. 316, Consideration of Fraud in a Financial Statement Audit, for related party transactions that are also significant unusual transactions.”

Conclusion

It’s important to understand that AI is not displacing the role of the auditor, rather AI works alongside people to automate large data analysis tasks and provide new insights. By enabling auditors to dive deep into 100 percent of the data, clients can be advised on their financial health and compliance with more comprehensive evidence and greater confidence in risk assurance.

MindBridge Ai is restoring confidence in financial data with Ai Auditor, the world’s only AI-powered auditing solution that leverages machine learning and AI techniques to augment human capacity and redefine reasonable risk assurance.

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About John Colthart

John Colthart

John Colthart is General Manager, Audit and Assurance & VP Product Management at MindBridge Ai. After his departure as a corporate finance and accounting practitioner in 2000, he grew a startup to over 425 employees and exited to IBM with a role of VP Sales Operations in 2010. 

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