In 2024, global financial regulators issued enforcement actions totalling $4.6 billion, with transaction monitoring violations alone surpassing $3.3 billion. This sharp rise in penalties highlights a major problem of today’s compliance frameworks. Specifically, they are struggling to keep pace with changing regulatory demands, leaving financial organisations exposed to risks.

This issue hits particularly hard when companies in question are operating across multiple jurisdictions with fragmented legal frameworks. Compliance is no longer just about adhering to the rules – it’s about doing so efficiently, without suffocating business growth. 

The question becomes this: how can firms stay compliant under evolving regulations without drowning in all the costs and inefficiencies?

The compliance burden: why firms struggle
Financial companies are caught in an increasingly complex web of rules that often conflict between jurisdictions. All countries and regions have distinct regulatory expectations, making it impossible to maintain a one-size-fits-all approach.

Not only that, but those regulations are constantly changing, forcing institutions to update policies, systems, and processes regularly – either that, or face legal blowback. But these adjustments often come at significant cost: each regulatory update means changing things across multiple levels, from IT to risk management to reporting and so on. It’s never just one thing that can be dealt with quickly.

This is exacerbated by the fact that compliance structures tend to be rigid, relying on outdated processes. Traditional approaches, based on manual oversight and multiple layers of approval, no longer work in today’s fast-paced environment. But adapting them to modern needs is a slow and exhausting process.

Strategies for navigating regulatory complexity
One of the biggest roadblocks to making compliance efficient is the lack of communication between departments. Compliance teams often operate in isolation from other business functions, which can lead to policies that slow a company’s growth instead of enabling it. This is where the first step should be – companies must break down the borders between departments, integrating compliance into everyday business operations. 

This means equipping compliance teams with not just regulatory expertise but also business acumen, so that they can assess how regulations may affect the company’s revenue, client acquisition, and other key metrics.

Another major adjustment that I would recommend is for firms to adopt flexible compliance models that can cater to different jurisdictions while maintaining a unified global framework. This requires centralised oversight combined with localised execution, allowing regional teams to tailor compliance processes to their specific markets. 

However, operational efficiency is impossible without automation. Manual reporting and oversight are no longer a sustainable way to do things. Instead of increasing headcount to handle rising regulatory demands, I believe that firms must invest in scalable compliance technology to reduce costs and improve accuracy.

One way to do so is by investing in the deployment of AI-powered monitoring tools that can detect risks in real time and reduce false positives in AML screenings. The same tools can also be used to introduce smarter workflows with automated regulatory reporting and dynamic compliance dashboards that provide instant visibility into global obligations.

AI as a transformative factor in compliance
AI-based compliance solutions have already begun to demonstrate their value to financial institutions that have embraced them. Take HSBC, for example – the bank previously spoke of how it implemented AI-powered transaction monitoring to enhance fraud detection with impressive results.

Their teams started identifying two to four times more incidents than before, while the number of false positive cases dropped by 60 per cent, saving HSBC a lot of effort needed to make customer inquiries and review transactions manually. Both the compliance processes and the customer experience took a turn for the better.

Another good case to bring up here is JP Morgan Chase, which has deployed its AI system, COiN, as a new method of dealing with contract analysis. Through COiN, the company has automated scanning legal documents, identifying inaccurate information and analysing it for compliance risks and fraud. This reduces the possibility of man-made errors and greatly accelerates the review process, since the potential delays are brought down to a minimum.

That said, despite the many advantages AI technology offers, challenges to its adoption still exist, and many companies still hesitate to integrate AI-powered tools into their compliance. There are several core reasons for this.

Obstacles to AI adoption in compliance
The main point about AI implementation is that it requires considerable upfront investment. Whether you develop solutions in-house or buy them from a third party, it’s going to cost you a pretty penny. And not only that, you will also need to invest into training your employees so that they can work with this technology effectively.

There is also the pushback from compliance teams themselves to consider. From personal experience, I can say that compliance departments often operate in isolation, following rigid processes that make AI adoption quite challenging. Many compliance teams still rely on outdated communication methods such as email, and even the introduction of basic messaging tools faces resistance.

This bureaucratic nature of compliance also extends to decision-making, which means that the implementation of any new technology first requires approval from numerous committees. Furthermore, senior leadership in companies tends to exercise rather poor control over their compliance departments due to its complex and heavily protected nature. As a result, making any changes in the way they operate becomes an uphill battle.

And we should also touch upon a prevalent (and valid) concern about the regulatory uncertainty of AI adoption. Compliance officers are used to operating under strict frameworks that impose hefty fines for non-compliance and, right now, they can’t be certain how regulators will assess the involvement of AI in areas such as risk assessments or fraud detection. They fear potential legal repercussions.

All of these factors make it difficult to evaluate AI’s risks and benefits and, in turn, harder to justify investing in this technology, which significantly hinders its adoption in compliance.

What to look for when choosing the right tools
Given all the obstacles we have covered above, companies must adopt a strategic approach when choosing AI-powered compliance tools and really think things through. To my mind, there are several key points to consider.

First is explainability. The AI solution you choose should provide transparent decision-making processes, allowing compliance teams to understand why a particular transaction was flagged or cleared. This will reduce the need for additional investigations and simplify interactions with regulators when submitting relevant reports.

The second factor is scalability. A good AI-driven compliance tool should be able to work seamlessly with existing systems and grow with the business, adapting to new demands and workloads without requiring constant system replacements.

AI platforms can analyse vast transaction datasets in real time, identifying suspicious activity faster than any traditional system. And no-code AI tools make it possible for compliance teams to deploy fraud detection and risk assessment models without needing extensive technical expertise and in-house development.

The most important point to underscore here is that, for financial institutions, compliance should not be a roadblock. By embracing AI technology to streamline processes, firms can bring their compliance to a new level, making it smarter, faster, and more effective.