.svg)
For too long, transaction monitoring (TM) has operated only in the shadows of retail banking standards. But wealth and asset management institutions face unique challenges that demand a more refined and risk-based approach to measuring effectiveness of TM systems.
As the Wolfsberg Group and the Association of Certified Anti-Money Laundering Specialists (ACAMS) make clear, effectiveness in TM must be redefined around real outcomes. Useful information for law enforcement, proactive detection of crystallised risk, and strategic deployment of resources where they matter most. For firms operating in low-volume, high-value environments, like wealth and asset management, this shift is not just necessary; it’s urgent.
The challenges of monitoring in the wealth and asset management industry
Unlike retail banks, wealth and asset managers face a unique set of anti-money laundering / counter-terrorist financing AML/CTF compliance challenges, shaped by the nature of their clients and operational models.
- Complex customer relationships: High-net-worth clients often operate through a web of entities: trusts, special-purpose vehicles (SPVs), and offshore holdings making it difficult to establish a clear picture of beneficial ownership or control.
- Fragmented data across intermediaries: Transfer agents, custodians, and fund administrators each hold partial data, leading to siloed insights and gaps in transaction visibility.
- Evolving risk typologies: Exposure to digital assets and cross-border private placements creates new financial crime vectors that static rules struggle to detect.
- Operational pressure: Fee compression, onboarding complexity, and the need for seamless digital experiences mean compliance can’t come at the cost of customer experience.
Traditional TM systems built for high-volume retail environments often fail to account for the nuanced risk in these scenarios. As a result, alerts are too broad or too blunt, leading to wasted investigative time, poor suspicious activity report (SAR) quality, and, most concerningly, missed risks.
To solve this, firms must focus on effectiveness not just the number of alerts, but whether those alerts reflect real risk and drive meaningful outcomes.
What does effective transaction monitoring look like in wealth and asset management?
The Financial Action Task Force (FATF) and global regulators are clear: firms must tailor their controls to their specific risk profiles. According to the Wolfsberg Group Statement on effective monitoring for suspicious activity, ‘There is no ‘one size fits all’ financial crime risk management programme’. That starts with rethinking what effective monitoring looks like in a wealth and asset management context.
1. Segment-specific product risk assessments
Effective TM starts with understanding the risk landscape of the products and services on offer. TM systems must reflect the inherent risks of wealth specific products such as trusts, private funds, managed portfolios or private market placements. An annual product-level risk assessment should feed directly into the tuning of monitoring rules and AI models.
2. Coverage across structures and accounts
High-value clients rarely transact through a single account or entity. Effective TM must connect the dots across SPVs, nominee accounts, offshore trusts, and personal investment vehicles bringing them into a single risk view.
This requires integrating transaction data with up-to-date know your customer (KYC) profiles, adverse media, onboarding documentation, and behavioural patterns. By applying network analytics and client segmentation, firms can identify patterns that would otherwise be missed such as layered transfers across accounts or sudden shifts in fund movements that suggest smurfing or front-running activity.
3. Customisation over factory vendor settings
While most modern transaction monitoring vendors provide a pre-built library of financial crime typologies, wealth and asset Management firms often need customised scenarios tuned to client behaviour, product use, and regional risk profiles. TM platforms must allow teams to tailor risk thresholds, adjust typology logic, and integrate alternative data sources. However, testing and deploying these changes directly into a live system introduces operational and regulatory risk. That’s where sandbox environments become essential.
A sandbox provides a secure, isolated environment to trial new rules, model recalibrations, or AI-led detection logic without disrupting operations or triggering false alerts in production.
4. Continuous feedback and outcome-based tuning
Truly effective monitoring doesn’t stop at generating alerts. It involves reviewing the quality of outcomes: how useful SARs are to law enforcement, whether alerts led to meaningful internal investigations, and where missed risks were only surfaced after regulatory inquiries.
Firms should establish regular feedback loops to feed these insights back into scenario tuning, model retraining, and risk scoring.
5.Compliance-first AI to augment rules-based systems
Access to sufficiently realistic data is a key barrier for developing and demonstrating the effectiveness of alternative and innovative approaches to detecting money laundering activity. Recently, Napier AI, the Alan Turing Institute, Plenitude Consulting and the Financial Conduct Authority (FCA) announced a new project aimed at improving money laundering detection through the creation of a new fully synthetic dataset augmented with a wide range of money laundering typologies. This is reflected in our library of typologies in the Napier AI Transaction Monitoring solution, powered by compliance-first AI.
A compliance-first approach to AI blends the transparency and familiarity of traditional rules with the adaptability and pattern-detection capabilities of AI, providing firms with a practical and explainable route to innovation.
Find out more:
Regulatory compliance first AI: How to move from in theory, to in production
Photo by MARIOLA GROBELSKA on Unsplash
