A holistic surveillance approach to address money laundering (ML) risks in financial markets

The regulatory trend towards holistic trade surveillance

In recent years, regulators have increased their focus on combating market abuse and providing greater investor protection. The EU Market Abuse Regulation (MAR) was introduced in 2016 and the Market in Financial Instruments Directive (MiFID II) in 2018; and both have an extra-territorial application. The Swiss regulatory framework was brought into line with international standards with the introduction of the Financial Market Infrastructure Act (FMIA) in 2016.

Leading market participants are responding to these regulatory initiatives by making substantial investments in trade surveillance, and we observe a growing trend towards the adoption of a holistic surveillance framework. The integration of a broader combination of signals from various structured and unstructured data points – both trading activities and trader communications – enhances significantly the reactive and preventive functionalities of trade surveillance systems. Fewer but more accurate alerts are produced for the detection of new and evolving risky trading behaviours.

The potential correlation between the market abuse and AML frameworks

Regulators have increased their scrutiny of the emerging exposure of financial markets to money laundering risks and recognised the potential correlation between market abuse and money laundering.

In its 2017 risk based approach guidance, the Financial Action Task Force (FATF) suggested that some forms of market abuse may constitute an offence of money laundering (ML) under applicable national laws, and that certain controls for compliance with market abuse regulations may also be useful in monitoring for suspicious ML activity.

A 2019 thematic review of the Financial Conduct Authority (FCA)  looked at ML risks pertaining specifically to the trading of financial instruments: it found in parts of the financial services industry a potential disconnect between trade surveillance for market abuse and AML transaction monitoring functions. The key findings of the review highlighted the importance of aligning the two functions, and in particular:

• Assessing clients’ intended trading strategies as part of an effective customer risk assessment and due diligence (CDD);
• Sound CDD practices within each intermediary firm involved in the transaction chain;
• Transaction monitoring systems integrating predefined detection rules with scenarios based on customer background and trading patterns, both calibrated around the firm’s business model and risk appetite;
• Increasing focus on network analysis and contextual monitoring, by leveraging technology solutions involving voice and e-communications surveillance, artificial intelligence and behavioural analysis.

Missed red flag trade behaviour

The importance of effective customer risk assessment and due diligence in connection with transactions in capital markets is evidenced by recent enforcement actions.

In a 2019 investigation, prosecutors alleged that four of the world’s largest commodities trading firms, between 2011 and 2014, had funnelled an estimated USD31 million in bribes to corrupt Petrobras employees. The Petrobras trading division allegedly engaged with a brokerage firm which routinely sold fuel and oil derivatives to Petrobras at inflated prices or bought from Petrobras at discounted prices, delivering illicit profits to the other side of the trade, typically a large commodities trading firm. These firms, in exchange, would administer kickbacks relating to the fuel trades to Petrobras officials through an offshore account under the name of a middleman.

Secondly, a leading investment bank reached a USD3.9 billion settlement to end an ongoing investigation into series of bond deals it had facilitated to raise USD6.5 billion capital for a sovereign wealth fund. A large portion of the funds raised were embezzled or laundered, going through fraudulent shell companies to the personal accounts of corrupt officials or employees and disguised to look like legitimate business.

Lessons learned and future developments

We believe that the industry at large is still relatively unaware of its vulnerabilities to ML risks in the financial markets and is partially operating with misaligned AML and trading surveillance functions. However, there are clear signals from the regulatory community and from bigger market players that a change in the general approach lies ahead.

We anticipate greater efforts within the industry to further integrate the two functions, not only in light of the intensified scrutiny but also to exploit the potential for efficiency gains that can be achieved with a more holistic conduct surveillance framework. The integration of AML datasets and related risk indicators as well as the inclusion of a broader combination of data points, such as trader communication signals, would be a foundation for producing fewer, but more accurate, trade alerts and enable more effective identification, assessment and mitigation of misconduct exposures.

How Deloitte can help

Our team of subject matter experts can support your organisation with effectiveness reviews of the implemented market conduct risk framework in order to identify vulnerabilities and optimisation opportunities in the areas of governance, misconduct prevention and detection and related controls and procedures. We are assisting with investigations and lookbacks exercises to assess and mitigate potential exposures to market misconducts and provide cost effective solutions to perform large-scale reviews. Our solutions leverage advanced data analytics techniques to monitor, profile and review data to detect key trends and anomalies in a cost and time effective manner.

What is next

In future articles in this series, we shall provide insights into trade / trader behavioural analysis, and the trends in leveraging data and machine learning for holistic surveillance. We will also explore trends and synergies in investor protection regulations and communication surveillance.