Criminal activity in financial services continues to evolve at pace, putting pressure on institutions to anticipate threats rather than simply react. While fraudsters exploit weak links and scale operations with automation and artificial intelligence, regulators and financial institutions are reshaping compliance to close the gap.
The shift from fraud detection to real-time collaboration is now gathering momentum, and the next five years could transform the economics of financial crime, claims Salv.
According to Salv, the key development will be the rise of intelligence sharing as a fourth pillar of compliance, sitting alongside transaction monitoring, sanctions screening and risk-based onboarding. Intelligence sharing is no longer a theoretical concept but a regulatory priority. Article 75 of the EU Anti-Money Laundering Regulation provides explicit support for cross-institutional collaboration, while PSD3 is expected to reinforce the trend. In the UK, policymakers are also pushing in a similar direction.
This approach differs from earlier compliance controls because it is not confined to the internal operations of a single firm. It requires banks, FinTechs and payment providers to collaborate in real time, exchanging data and alerts to stop fraud and money laundering before losses occur. Salv highlights that when intelligence flows across institutions, recovery rates rise, fraudulent accounts are shut down more quickly, and criminal schemes become harder to sustain.
Regulation may be a catalyst, but Salv stresses that culture is the decisive factor. Internal hesitation often slows adoption more than technical or legal barriers. Where leaders actively endorse intelligence sharing, progress accelerates. Estonia provides a case in point: after initial reluctance, the intervention of bank CEOs turned tentative pilots into a national model for collaborative compliance. Over the coming years, more firms are expected to move past a “wait and see” approach, treating intelligence sharing as an industry standard and a source of professional pride.
At the same time, the tactics used by financial criminals are becoming more scalable. Mule account networks, AI tools and automated attack strategies are designed to exploit fragmented systems and slow responses. Sharing structured, suspicion-based insights across organisations changes that equation, making fraud more expensive to commit and less profitable to sustain.
The applications for intelligence sharing extend far beyond fraud. Salv notes that the same framework can support investigations into money laundering, sanctions enforcement, enhanced due diligence and counter-terrorist financing. Integration with broader compliance systems — from national utilities to KYC platforms — is already emerging, with regulators and industry bodies playing a larger role in setting expectations and building confidence.
The strategic shift will move institutions from retrospective compliance to predictive intelligence. Stronger collaboration, standardised data, and the integration of AI and automation will help firms cut false positives, reduce duplication, and act faster. The outcome is a more proactive model of financial crime prevention, one designed to anticipate rather than simply react.
Looking to 2030, institutions will face tougher regulation, more coordinated criminal networks and greater scrutiny from regulators. Yet these pressures also present an opportunity. By embracing intelligence sharing and predictive compliance, firms can not only strengthen their defences but also shape the future of financial crime prevention. The regulatory foundation is already laid — the challenge now is to build on it.
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