For years, auditors have used interviews and workshops to conduct successful company assessments. As more businesses become digitized, auditors need to blend those traditional techniques with a more data-driven approaches. In the face of higher expectations for accuracy, internal audit departments need to take note: big data is changing the way organizations operate and internal audit can’t afford to be left behind. Yet recent studies show there is still much work to be done.
As business processes become more complex and greater volumes of information become available, auditors are under pressure to use analytics to turn assets into insights. This means understanding the inner workings of those processes and gaining access to much more data, instead of basing audits on a small data sample. But how can internal audit teams capitalize on the wealth of data available—from their own business activities as well as external sources—to increase efficiency?
Machine learning and artificial intelligence algorithms are playing an increasingly important role in this, by guiding the internal auditors through the analysis and pointing out compliance issues and inefficiencies along the way. Simply put, the combination of analytics and artificial intelligence acts like MRI machine for the business, providing unbiased visibility and diagnostics of an organization’s processes.