Compliance has ascended to become one of the most challenging strategic concerns facing institutional finance in 2025, because regulatory demands, along with financial crime risks, are changing faster than the systems designed to manage them.
Global sanctions regimes are shifting with little notice, while financial crime networks work across borders, platforms, and asset classes with growing sophistication. Meanwhile, transaction volumes have soared as finance has gone increasingly digital and international. Regulators also are broadening their emphasis beyond financial activity itself to the technologies institutions use to manage risk, including AI.
Taking all these pressures together, they are pushing compliance from being a back-office function to a main determinant of resilience, competitiveness, and regulatory standing.
Legacy systems under strain
A lot of compliance frameworks in operation today have been designed for a different age. They are heavily reliant on static, rules-based systems that are intended to flag known patterns of risk. While such systems may have worked well in the past, they generate vast numbers of false positives that force banks and asset managers into maintaining large teams of compliance staff reviewing alerts that rarely indicate genuine wrongdoing.
Data fragmentation compounds the challenge. Information is usually split across know-your-customer checks, transaction monitoring, sanctions screening, and customer operations, making it difficult to create a holistic view of behavior across products and jurisdictions.
Manual processes meet adaptive crime
Regulatory change has accelerated and become less predictable, yet translating new rules into operational controls still relies very much on manual processes.
Meanwhile, financial crime has become increasingly adaptable: for every channel closed off, new ones open up in its place. Traditionally, institutions have used experienced human analysts to provide judgment and adaptability, but that model is expensive and hard to scale. The result, many executives now concede, is a system that is slow to evolve and operating at the outer edge of its structural capacity.
AI offers a new operating model
Artificial intelligence is starting to provide an alternative. Using machine learning, models can evaluate behavior across thousands of current transactions in real time, looking for anomalies based on evolving patterns rather than fixed thresholds. This greatly reduces the incidence of false positives, while early detection of new risks goes up.
Crucially, these systems learn from outcomes, creating feedback loops that static rules cannot replicate.
Generative AI reshapes workflows
Generative AI is also changing compliance workflows. Guidance from regulators can be interpreted and translated into operational logic faster, and processes like responding to customer requests for source-of-funds information can be automated at scale.
Organizations are also leveraging AI to monitor regulatory developments continuously, enabling them to be proactive rather than reactive in their response to new requirements.
Human oversight remains central
Rules and human oversight are not going away. Escalation, judgement and governance are still important, with AI conducting detection and routine processing — but not accountability.
Data and infrastructure challenges
The shift towards AI-driven compliance also relies on data and infrastructure. In most firms, the data related to compliance is held in silos, and earlier attempts at consolidation on a large scale have produced mixed results.
AI alters that economics by allowing models to directly consume both structured and unstructured data, extracting insights without exhaustive upfront standardisation. As more data sources connect, performance improves.
Compliance as a competitive lever
The financial implications are significant. Better detection can reduce the need for excessive capital buffers against compliance failures. Automation lowers operating costs, while faster, more accurate processes reduce customer friction.
For some institutions, compliance is starting to move from a pure cost centre to a source of competitive advantage.
Regulating the regulators’ tools
Meanwhile, regulators are also focusing attention on AI itself. In the EU, the European Union AI Act establishes explicit requirements related to risk classification, governance, and transparency for high-risk systems. In the UK, existing accountability and conduct frameworks are being applied to the use of AI.
Firms deploying non-transparent or poorly governed models run the risk of creating a new class of regulatory risk.
A widening gap
Industry advisers say that institutions should now take a structured approach: take stock of impending regulatory change, review existing compliance capability, and identify where AI could deliver the greatest impact — usually in alert quality, customer information handling, and adaptive monitoring.
“Compliance is no longer just about meeting minimum standards,” says Alex Batlin, a London-based fintech and digital assets adviser. “It has become core institutional infrastructure.”
As regulatory scrutiny intensifies, so too does the gap between institutions that modernise early and those that remain reliant on legacy systems.