What is Continuous Controls Monitoring (CCM) and how is it different than what Supervizor does?
As a concept, Continuous Controls Monitoring (CCM) has been around for more than two decades and rep...
If you’re a CAE or lead an internal audit team, chances are you found yourself at the Gaylord Resort outside of Orlando, Florida the week before last. You would have been there to attend the Institute of Internal Auditors’, more commonly known as the IIA, GAM (Great Audit Minds) conference. The annual gathering of audit professionals is known to be the event for bringing together internal audit leaders. Over three days they learn about new trends, techniques and technologies, network with other auditors and stay abreast of the latest in the audit industry. It’s for this reason, Supervizor was there too.
Over the course of the conference, we met with hundreds of audit professionals, attended dozens of sessions and heard what other vendors were talking about and spotlighting in their booths.
You could summarize the three days with two words: “analytics” and “AI”. Every single session contained some sort of mention of analytics or AI and in most, both. Discussions of and about analytics and AI were everywhere—in vendors’ booths, keynote sessions and even in casual conversation amongst attendees. Multiple AI Agents were announced by multiple vendors and terms like generative AI, LLM (large language model) and machine learning were cited time and again.
But for all the discussion about analytics and AI, there emerged a growing one about what it takes to be successful with such programs and this comes down to a critical tenet: in order to produce accurate and meaningful analytics or a foundation for which your AI agent to operate accurately, you’ve got to get the data right. And getting the data right is not easy. In fact, in one session’s polling of attendees asked “what is the number one problem your D&A (data and analytics) program faces?” the top concern was “we struggle to get access to the data.” And it’s not just about getting access, it’s also about bringing disparate data together, normalizing it and keeping it up to date (i.e. timeliness). The reality is that AI is only as good as the data it works with. Even the most sophisticated algorithm will fail if it's fed with stale, incomplete, or low-quality information.
It's for this reason more and more attendees stopped by the Supervizor booth as the show progressed. Attendees wanted to know more about our automated controls—both rules-based and AI driven, our continuous monitoring capabilities and our AI technologies specifically designed for financial data contexts, but most of all, they had heard about and wanted to understand the Supervizor data foundation and how such provides for unparalleled data freshness (timeliness and relevance of data) and superior data quality. These combine with our universal schema recognition system that automatically identifies and maps accounting structures, while our semantic layer understands the meaning behind transactions, not just their format. The result is a standardized financial data model that powers AI and analytic applications with unprecedented accuracy that can be up and running in less than a day.
Did you attend GAM? Did you get the chance to talk with Supervizor? If not and you want to learn more about Supervizor’s superior data foundation and how such ensures accurate and meaningful analytics and AI controls, sign up for a demo.
