For years, transaction monitoring (TM) in wealth and asset management has lagged behind the standards set in retail banking. Yet, given the complexity of clients and risks involved, firms in this sector urgently need to redefine what effective monitoring looks like.
According to the Wolfsberg Group and ACAMS, effectiveness must now be measured by outcomes: the ability to detect crystallised risks, generate intelligence that supports law enforcement, and use resources strategically, claims Napier AI.
Firms in the wealth and asset management space face a distinctive set of challenges when it comes to AML/CTF compliance. High-net-worth clients often operate through layers of entities—such as trusts, SPVs and offshore structures—obscuring beneficial ownership. On top of that, transaction data is often fragmented across intermediaries like custodians and fund administrators. The rise of digital assets and complex private placements is also making static, rule-based systems increasingly ineffective. At the same time, firms are under pressure to deliver seamless digital experiences, putting additional strain on compliance teams. Traditional TM tools, built for high-volume, low-value retail environments, frequently miss the mark—producing either low-value alerts or overlooking high-risk activity altogether.
To improve this, institutions need to rethink what TM effectiveness really means. The FATF and regulators stress the importance of risk-based controls, tailored to each firm’s unique risk profile. According to the Wolfsberg Group, there is no “one size fits all” solution. Instead, wealth and asset managers must adopt a more refined strategy that begins with evaluating the specific risk profile of each product. Annual product-level risk assessments should be directly linked to how TM rules and AI models are calibrated.
Another essential practice is creating visibility across client structures and accounts. Wealthy clients rarely use a single account; their financial activity is often spread across various nominee accounts, offshore trusts and SPVs. Connecting this activity into a unified risk view requires integrating KYC profiles, onboarding documentation, adverse media and transaction history. With tools like network analytics and client segmentation, firms can identify otherwise hidden threats, such as layering or suspicious shifts in fund flows.
Customisation is also key. While many TM vendors provide out-of-the-box typologies, wealth-focused firms often need bespoke rules. These systems must support scenario tuning, regional risk calibration and integration of alternative data. Sandbox environments are increasingly important here, as they allow firms to safely test new detection logic without impacting live systems.
An effective TM programme also requires regular feedback loops. It’s not enough to generate alerts—firms must assess how often those alerts result in meaningful investigations, high-quality SARs, or missed risks identified too late. This data should inform ongoing tuning and model updates.
Finally, AI can play a crucial role—but only if deployed responsibly. A recent collaboration between Napier AI, the FCA, Plenitude Consulting and the Alan Turing Institute demonstrates this approach. By creating synthetic data enriched with realistic money laundering behaviours, they are advancing the industry’s understanding of what “compliance-first AI” looks like. Napier AI’s own platform incorporates this philosophy, combining the explainability of rules with the adaptability of AI, allowing firms to innovate without compromising oversight.
As financial crime continues to evolve, wealth and asset managers must move away from legacy TM thinking. With outcome-focused strategies, customised controls and AI augmentation, firms can create more resilient and responsive compliance frameworks.
Copyright © 2025 RegTech Analyst
Copyright © 2018 RegTech Analyst
