Ai Auditor uses a combination of statistical, domain expertise, machine learning and cross correlation tests to examine financial data sets to assess the risk a transaction being materially misstated.

This powerful tool enables auditors to more efficiently plan their audit by assessing risk within areas of the business, greatly improve the effectiveness to identify transactions of audit interest and delivering more value to customers through financial reporting, comparisons and deeper insights.

To better understand how Ai Auditor works, we need to first define what a Control Point Indicator (CPI) is. Second, we need to introduce some of the basic statistical checks that are best practice within the audit process today. Third, we need to understand how Machine Learning synthesizes complex relationships in data to assess other more powerful control points.

Control points, simply put, are events that occur within data sets and it’s easy to explain control points at the same time as some of the basic statistical methods that exist within auditing today.

If transactions ending in $0.00 have a higher likelihood of fraud, audit tools can look for transactions meeting this control point, along with other simple statistical methods to identify riskier transactions. The more control points are triggered , the greater the risk associated with the transaction. Higher risk transactions do not mean a transaction is fraudulent, but rather that a transaction is of audit interest

What makes Ai Auditor special however, is its ability to score risk based on context. This is achieved by teaching the tool what to look for in massive sets of data. “What to look for” is taught by domain experts who can rely on their professional experience in addition to cases of real fraud within data sets. Over time, as more and more data is synthesized, experts begin testing the tool and algorithms are honed in on exactly what to look for. Ai Auditor must pass these tests before ever being provided with a real-world data set.

Over time, as more and more data sets are fed into Ai Auditor and users interact with the data by flagging and interacting with transactions, Ai Auditor continues to build on its level of sophistication and accuracy. The more people that use Ai Auditor, the smarter it becomes which is essential to its accuracy because different G/L accounts will have different behavior. Businesses in different industries or even different sizes will also have different “normal” behavior within their financial history.

All of this information is gathered and analyzed to improve the sophistication of a machine that’s primary function is to learn.

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