If you think back far enough into the past, digital data was stored on giant reels of tape. Floppy disks were heralded as a giant technology leap in the 1970s. Decade after decade, the form factor of archiving solutions got smaller and smaller. Now, they’re something that you don’t even “see” miraculously captured in a cloud. Of course, there’s a lot of technology behind the cloud that you don’t see, but that’s part of what makes the advances in archiving so incredible.
Highly indexed. Searchable. Even unstructured data types like videos and images can now be tagged, archived, and queried on demand.
But there is an art to it.
Being “pro-active” when it comes to surveillance is where it all starts. Crafting a policy that is specific, clear, and actionable is Step 1. Calling out what behaviors are going to be monitored, where and how they’re going to be monitored, and what the consequences are for transgressions from those acceptable behaviors is an essential activity on the path towards compliance.
Can artificial intelligence (AI) and machine learning (ML) technologies make a difference? Sure. Sophisticated approaches that leverage AI/ML are designed to identify patterns. People – for the most part – are predictable. When they start communicating with a new person, or at a heightened frequency, there’s usually a reason for that change. Proactively monitoring what happens next is something that all financial firms need to do. Conversely, if the communication on that channel stops, but financial transactions are being posted at an increased rate or with new stocks or commodities, that incongruity may be worth investigating. Here, AI/ML can help snuff out the false positives which reduce the cost of compliance upfront – and downstream if an investigation into false positives sparks a backlash.