Bank ACs will see big changes in the next few years
With advanced data and analytics (D&A) already being used and cognitive artificial intelligence (AI) on the cusp of practical application, banks and their audit committees (ACs) are increasingly asking about technology in the audit. While audit technology is poised to offer significant benefits for the quality of external audits, ACs need to understand not only how the audit will change, but what their organizations need to do to help enable it.
D&A lets auditors accurately leverage 100% of available organizational data (as opposed to sampling) to enhance audit quality with far more granular analysis and testing. It can also uncover data patterns and relationships that can improve audit quality and help clients gain better insight into their businesses. Aside from audit quality and insights, external auditors can leverage the significant investments that banks are making in technology to enhance efficiencies as well.
While D&A represents the pinnacle of logical analysis in the technological revolution, cognitive AI represents the reasoning and rational side of the equation. Appropriately sophisticated algorithm chains give the software the ability to absorb information and actually “think” about it in ways similar to human beings. While D&A lets you do things like measure, compare and predict, cognitive AI can do things like infer, assess, hypothesize, debate and learn.
From a banking perspective, cognitive technology can help address one of the sector’s biggest areas of judgment: determining the risk grade on loans. Using cognitive capabilities, the right technology can quickly process millions of unstructured, loan-related documents and agreements looking for words, phrases and patterns, then parse the data into minute areas of analysis—for example, all the loans in a particular postal code in a particular province. Importantly, any ultimate judgment still resides with a human being, but it will be one technologically empowered by a level of analytical depth, speed and accuracy previously unattainable.
It’s important to remember that much of this is still in the use-case stage. On the external audit side, the process must be collective and methodical. For example, a big issue is whether key data is being gathered and stored in formats the technology can synthesize. Financial institutions are significantly increasing their ability to warehouse data in usable formats to improve its usability for external audits which overcomes the challenge of trying to extract data from multiple legacy systems with varied formats. Questions also remain around the regulator’s views, as they have to judge whether D&A cases accord with external audit standards. All sides, then, need to recognize the requirements and expectations of the others in order to take things forward in a proactive, mutually effective and holistic manner.
ACs always need to keep lines of communication strong and open, but given the organization-wide transformative implications of these developing technologies, it’s even more important that the AC, management, internal audit (IA) and the external auditor work together closely to understand the issues, monitor challenges and act with the goal of improving audit quality. Questions these groups should all be thinking about and asking include:
Issues, of course, remain. Beyond the challenge of data formats lies that of data transfer—that is, building the “pipe” that connects and inputs the bank’s information and stores it into the appropriate audit applications in a secure way. The point is, however, that these barriers are changing every day and solutions are continuously being improved and refined. Pilot testing has moved to production, indicating that some of banks’ biggest data headaches may be on the verge of being eased as D&A and cognitive AI continue to address broader data sets and improve audit quality and efficiency.