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AML in wealth & asset management: Top questions answered

Anti-money laundering compliance for wealth and asset management is becoming more assertive; here’s what you should know

Mariya Pattara
March 25, 2024

The need for stringent anti-money laundering (AML) strategies for wealth and asset management (WAM) firms, fund distributers and asset servicers is increasing. In this blog, we answer the top questions about AML obligations.

1) What regulatory frameworks govern financial crime compliance in the wealth and asset management sector?

The UK’s Financial Conduct Authority (FCA) recognises the wealth and asset management industry as an ‘inherently high-risk sector for enabling and/or participating in financial crime’. This is underlined in the recent ‘Dear CEO’ letter which lists financial crime and Consumer Duty expectations from firms.

The FCA’s supervision is shifting to become more assertive, proactive and data driven as it identifies the scale of consumers in the sector, with 1.8m portfolios and 14.3m stockbroking accounts. Many of the firms in this sector operate on manual and siloed processes lacking a holistic view of customer risks and financial crime vulnerabilities. In response to these concerns, wealth management firms are urged to enhance financial crime compliance, with a focus on reducing harm, raising standards, and improving the industry's reputation.

In the US, the Financial Crimes Enforcement Network (FinCEN) recently proposed new rules for Anti Money Laundering (AML) /Counter Terrorism Financing (CTF) for Investment Advisers assessing the sector’s vulnerability to illicit finance activity. The proposed rule would require investment advisers to implement an AML/CTF program, file Suspicious Activity Reports and boost information sharing between law enforcement government agencies and financial institutions.

The AUSTRAC (Australian Transaction Reports and Analysis Centre) outlines guidelines on identifying and understanding the source of funds and source of wealth as part of Enhanced Due Dilligence (EDD) here.

2) Some AML red flags include:

Legacy systems for AML compliance, while familiar and seemingly cost-effective in the short term, come with a range of hidden expenses that can significantly impact an institution’s bottom line.  

These hidden costs manifest in various ways, including operational inefficiencies. Getting the right balance of build vs. buy is key to ensure financial crime compliance software is optimal, compliant and scalable to current and future needs.  

Some of the warning signs your current system isn’t up to the mark include:  
  • Poor customer experience due to longer onboarding, and lack of real-time monitoring and KYC checks;
  • Long manual reviews and workarounds by analysts due to too many false positives/ false negatives;
  • Regulatory scrutiny and non-compliance fines due to the system’s inefficiency;
  • Costly and time-consuming maintenance and upgrades, leading to operational inefficiencies;  
  • Integration challenges to newer technologies or platforms, leading to additional costs in bridging these systems;

3) How can emerging technology such as AI help with financial crime compliance in this sector?

The probabilistic, pattern-spotting nature of AI makes it the ideal solution for rightly flagging suspicious activity of behaviour changes and automating the detection of anomalies in screening and monitoring solutions.

Through advanced machine learning algorithms, AI in financial crime can rapidly analyse large datasets, identify unusual patterns, and flag potential financial crime. This enables a more agile response to emerging risks, reducing the reliance on manual reviews and expediting the identification of suspicious transactions.

By streamlining the anomaly detection process, AI not only enhances the firm's ability to combat financial crimes but also significantly improves operational efficiency, allowing resources to be directed more strategically toward addressing high-risk areas and ensuring compliance with regulatory standards.

Read a 12 step guide to the optimal path of AI implementation for financial crime compliance, with tips and data assurance checklists from leading data scientists

4) How can wealth and asset managers mitigate client privacy concerns and enhance customer experience while adhering to compliance?

The asset and wealth management business model relies heavily on building close relationships with customers and third parties, making it challenging to implement robust screening and monitoring processes. Despite regulatory requirements, firms struggle with due diligence implementation, fearing friction in these critical relationships.  

Transparency in KYC (Know Your Customer) data is essential to prevent reputational damage, and asset management firms need to leverage third-party data from sanctions, Politically Exposed Persons (PEPs), beneficiary ownership and adverse media lists for enriched client information, ensuring visibility across distribution layers.

The speed at which wealth and asset management firms can deliver financial products and services is great for the customer experience. But it makes compliance checks for anti-money laundering tricky. Slowing down is not the answer. The more pauses and disruptions that are inserted in customer journeys due to these checks, the more people fall out of those journeys. Leveraging third-party data lists for client information enrichment enhances transparency and ensures that due diligence is conducted thoroughly. This not only prevents blind spots but also facilitates a smoother experience for investors, who no longer need to repeatedly provide the same documentation across different jurisdictions.

Criminals are continuously evolving, and a transformative approach to financial crime compliance processes at WAM firms will not just drive down financial crime but use compliance as a strategic business advantages.

Read our case study on how FundsDLT reduced 70% of the management fee for asset manager products using Napier AI's screening and monitoring solutions.

Photo by Visax on Unsplash

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