Businesses are coming to rely on artificial intelligence (AI) to inform critical decision-making. Increasingly, AI is finding its place throughout organizations, from customer retention to marketing and finance. Assurance and audit are no exception to the benefits of artificial intelligence. As the value of next gen technologies become more transparent, firms will look to help bolster their staff with modern tools for more efficient and timely engagement.
Reasonable to ask for more assurance
Evolving standards have called for auditors to deliver 'reasonable' assurance levels – levels that are not perfect but rather are a high degree of assurance regarding material misstatements. Over the last few years, auditors have adapted how they audit and how they exhibit their quality to clients. This adaptation is mainly in response to the market; buyers are becoming more sophisticated.
This is a pattern that we find in numerous markets, with a top US firm remarking that “our client’s technology and data availability plays a role in drivers of change. The more clients are using technology, their expectation is elevated on our use of technology.”
Regulators knew about the positive effect that new advancements could convey, with the PCAOB foreseeing that “the future of audit will be able to provide a greater level of “reasonable assurance” as auditors may be able to examine 100 percent of a client’s transactions.”
These predictions proved to be accurate as MindBridge's Ensemble AI is capable of analyzing your data to identify the level of risk for 100% of transactions. As such results become the norm due to increasing AI adoption, the significance of exhibiting more elevated audit quality levels will continue to grow evident.
AI: An enabler for risk-based auditing
By scanning transactions using various techniques, auditors can better assess risk and find those risky and unusual transactions. This translates to an audit with less ticking-and-tying and a greater focus on what matters. It permits less audit staff to overcome more data and gives more prominent assurance in the end.
An example of an audit algorithm in action is MindBridge’s “outlier detection.” This algorithm category identifies unusual financial patterns, helping fulfill ISA 240, which expects auditors to look for unusual activities. An additional benefit of outlier detection is that its methodology consists of unsupervised machine learning, meaning algorithms are not trained or taught on specific data. This allows firms to analyze data and draw out anomalies without requiring training on similar entities. It can also be applied to all types of organizations, irrespective of their size or industry.
While outlier detection effectively detects new activity and outliers in data, it does not have a prior or pre-existing understanding of accounting processes. We believe that there is still a role for the expert system in risk scoring for audit. MindBridge’s “Expert Score” is an example; it’s an indicator that flags transactions based on a database of pre-existing rules determined to be unusual. For instance, write-offs directly between cash and expense will consistently get flagged by Expert Score.
Demonstrating quality: A key to growth
By leveraging these techniques and changing the profile of work, the firms that are most successfully implementing MindBridge are driving success in the market and growth. In addition, by speaking to the value throughout the customer lifecycle, these firms ensure that the customer sees the value of working with them.
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*The views expressed are the author’s and not ICAEW’s.
