Compliance in banking has always been a complicated balancing act and it is only growing more so in recent years. Between rising costs and tightening regulations, companies have less and less room for errors. This is particularly evident than in the UK, where financial firms are under intense pressure.

Recent reports show that almost 40% of financial service companies in this region were penalised for compliance failures in the past year alone. And 62% of surveyed firms say that keeping up with compliance requirements has become significantly more challenging over the last five years.

UK firms are now spending over £21,000 per hour fighting financial crime. In terms of annual costs, it adds up to about £38.5bn, roughly three-quarters of the UK’s defence budget. Meanwhile, smaller institutions are being hit the hardest, spending five times more of their revenue on compliance compared to larger players.

With numbers like that and regulations constantly changing, not just in the UK but worldwide, how are banks and fintechs realistically supposed to keep up without burning out their teams or budgets?

This is where artificial intelligence enters the stage to lend a helping hand.

Tackling the biggest barriers: cost, complexity and talent

To determine how, exactly, AI can make things better, let’s first take a clear look at the core issues banks face with compliance. Namely, that it is expensive, complex and heavily reliant on human expertise.

Finding qualified compliance professionals isn’t just hard, it’s becoming nearly impossible for smaller firms, since most are being lured away by bigger fish that can offer better salaries. And even when the right people are in place, the regulatory landscape often changes fast enough that compliance teams are constantly on the back foot, struggling to adapt.

AI offers a way out of this cycle. One of the biggest advantages it offers is reducing the cost of compliance operations through automation of processes. It can handle tasks that used to require entire teams, automatically reviewing transactions, flagging suspicious patterns and keeping records in order. As such, the necessity of maintaining large departments of analysts and compliance officers falls away.

And it’s not just about doing the same work faster and at lower costs. AI can actually make the process itself more efficient. Compliance is a field that requires a constant attention to detail and humans can only maintain that level of concentration for so long before something starts slipping through the cracks. AI, on the other hand, needs no rest and its attention span is not limited by physiological factors.

Not only that, but some AI companies are working on improving AI systems in ways that will allow compliance teams to write rules in plain language, without touching a line of code. These measures will turn what used to be an IT-heavy process into something intuitive and far more accessible to non-technical staff, as easy as asking a question in ChatGPT has already become today. This kind of natural language interface will empower a new approach to compliance, reducing bottlenecks and making companies more agile.

For SMBs struggling with limited budgets and talent shortages, this is going to be a major game-changer.

Turning compliance proactive

The thing about compliance is that, traditionally, it has always been reactive. Something happens, an unusual transaction, a missed check, a regulatory change and then the team scrambles to respond. But AI is turning this approach around.

With modern AI tools, you can scan vast amounts of data in real time, identifying potential risks and points of suspicion well before they become actual threats. AI-powered risk assessment platforms can take into account historical data and records of transactions and leverage predictive analytics to identify compliance issues ahead of time. Instead of just reacting to problems, financial firms can now move to prevent them.

Let’s say an AI system spots a pattern in customer transactions that resembles a known fraud typology. It can alert a compliance officer immediately, or even stop the transaction automatically, before any damage is done. Similarly, these AI models can be set to monitor regulatory updates from around the world, flagging new rules that may affect your company’s operations and helping compliance teams stay ahead of the curve.

This proactive approach not only helps with avoiding penalties, but also builds trust with regulators, investors and customers. In today’s environment, when people’s money is on the line, being able to say “we saw this coming and acted early” can be incredibly powerful for your reputation.

Of course, it’s not going to be all smooth sailing from here on out. Compliance is still a high-stakes field and the risks of getting it wrong are significant. A poorly trained AI model or lack of human oversight can easily lead to false positives or missed threats.

This is why I believe collaboration between AI and human experts will be essential. AI can handle the heavy lifting, but experienced compliance officers are still going to be needed to interpret results and provide final judgment calls. In fact, I’d say that we are about to enter a new era of “augmented compliance,” where technology and human insight go hand-in-hand.

What’s in store for the future?

As we look to the future, AI is set to reshape compliance strategies to be more streamlined, agile and scalable. And while some will undoubtedly worry about this transformation replacing human workers, I would argue that that’s not the point at all. It’s about giving compliance specialists the tools they need to do their jobs better, faster and with greater confidence.

Regulatory requirements are only going to get tougher in the future, so that kind of support is not just helpful, it is necessary. Banks and fintechs that learn to embrace this shift will be better equipped to navigate the increasing complexities. And those that don’t may find themselves left behind, buried under the weight of rising costs and missed opportunities.

Roman Eloshvili, Founder of XData Group