With UC environments continuing to evolve, compliance and supervision teams are facing pressures unlike anything seen before.
The challenge is no longer simply about scale. It is about managing an ever‑growing variety of communication types – across platforms, devices, and formats – without losing sight of what actually matters.
According to Don McElligott, VP of Compliance Supervision at Global Relay, the past few years have fundamentally altered the compliance landscape.
“The volume and variety of communications has exploded,” he says.
“The old days of email and maybe a couple of message types are gone. Firms now have dozens of channels they’re expected to supervise – mobile platforms, voice, conferencing, chat – all of it.”
Rising Regulatory Expectations
Alongside the expansion of UC environments, regulators have become far more proactive in how they assess communications supervision.
Where organisations once relied on employee attestations or broad “best effort” approaches, those assurances are no longer sufficient.
“The days of just telling regulators you’re doing your best effort don’t fly anymore,” McElligott explains.
“They will dig into communications and look for evidence that firms are missing data.”
This shift has placed enormous pressure on compliance leaders.
Coverage gaps are no longer theoretical risks – they are liabilities that regulators actively seek to uncover. As firms adopt more communication tools, the challenge becomes proving that nothing has been missed.
For many organisations, that pressure is compounded by uncertainty. It is not always obvious which channels are being used, how frequently, or where conversations move once they begin.
In an environment where regulators expect demonstrable oversight, uncertainty itself becomes a risk.
The False Positives Problem
As firms expand supervision across more channels, many discover that their existing tools struggle to cope.
The result is often an overwhelming number of alerts, most of which do not represent genuine risk. McElligott says,
“You ask compliance folks what their top three problems are, and all three will be false positives”
“It’s the pain of doing the job and knowing that even when you process all of that noise, there’s a good chance you’re still missing the real risk.”
This creates a damaging cycle. Review queues grow larger, reviewers become fatigued, and the likelihood of overlooking meaningful issues increases.
The effort required to clear the queue becomes an end in itself, rather than a means of protecting the organisation.
“If I’ve got 500 messages in my review queue, I can pretty much guarantee 490 of them are useless,” he adds. “That’s how real risk gets missed.”
Why Noise Reduction Misses the Point
For years, the compliance technology conversation has centred on reducing false positives.
While that objective is understandable, McElligott argues it has distracted the industry from the true purpose of supervision.
“The industry got so focused on reducing noise and false positives, it really lost sight of the fact that the true purpose of this is to identify risk,” he says.
Traditional keyword‑based and lexicon‑driven approaches are a major contributor to the problem.
These systems rely on predefined terms and phrases, forcing teams to anticipate how misconduct might be expressed in advance.
“It becomes a cat and mouse game,” McElligott explains. “If someone is really trying to subvert regulations, they’re not going to say it in clear English.”
As language evolves and communication becomes more informal, predictive keyword models struggle to keep up.
The result is a growing volume of alerts triggered by benign language, while genuinely problematic behaviour remains hidden in plain sight.
Context And Intent as a Turning Point
This is where AI‑driven supervision is beginning to change the equation.
Rather than relying solely on static keywords, newer approaches analyse conversations in context, looking at patterns, intent, and behaviour over time.
“An AI model can actually read and understand the context – read between the lines, if you will – and surface what a person is really trying to do,” McElligott says.
The goal is not simply to generate fewer alerts, but to generate better ones. Review queues may still be busy, but the content within them is far more relevant.
Instead of spending hours clearing noise, reviewers are presented with items that require genuine investigation.
That shift fundamentally changes how supervision teams operate and how they perceive their role.
Rethinking The Role of the Reviewer
As the quality of alerts improves, so too does the experience of those responsible for reviewing them.
Rather than acting as human filters for irrelevant data, reviewers can focus on understanding behaviour, investigating patterns, and escalating real concerns.
“There’s been a mindset of checking the box and keeping regulators happy,” McElligott observes.
“But when you’re actually identifying real risk, compliance becomes a value add to the organisation, not just a cost centre.”
This shift also has implications for how compliance functions are viewed internally.
When teams are able to surface meaningful insight rather than simply report activity volumes, they can play a more strategic role in the business.
A New Mindset for UC Compliance
For UC and compliance leaders, the message is clear.
AI‑driven supervision, when applied thoughtfully, offers a way out of the false positives trap.
By focusing on context, intent, and behavioural patterns, organisations can begin to detect real risk with greater confidence.
For McElligott, that shift is ultimately about purpose, not optimisation. “Stop trying to just battle the trouble with false positives and get to the real purpose of this – find the risk. Stop reducing the noise and find me the real risk.”
