Contextual Monitoring for Money Laundering Risk Within Financial Markets

June 2021 marked two years since the FCA published its report: Understanding the Money Laundering Risks in Capital Markets. It explored how institutions using traditional monitoring tools were missing risk while those that had adopted contextual monitoring – and were reviewing client risks more holistically – were reaping significant benefits.

  

“Those participants using network analysis had seen a vast reduction in the number of false positive alerts they received, when compared with using a traditional rules-based transaction-monitoring system” 

-FCA Report: Understanding the Money Laundering Risks in Capital Markets

As Mark Steward (Executive Director of Enforcement and Oversight at the FCA) called out in his April speech The importance of purposeful anti-money laundering controls,  “AML investigations are often complex because they are rarely transactional and require a systemic understanding of how a firm operates, its governance controls, its cultural habits, and the nuts and bolts of sometimes opaque systems.”

As a result, detecting Anti-Money Laundering (AML) in Capital Markets can be a challenge for the following reasons: 

1. Large volumes of transactions and high liquidity 
2. The global cross border nature of transactions 
3. Clients may spread business across a number of business units and/or institutions making it difficult to see a complete picture 
4. Involvement of other financial institutions or intermediation services (e.g. brokers) can make monitoring difficult as this can limit the ability of any one participant to have complete oversight of the transaction 
5. Potential money launderers can trade through a complex web of entities and transactions
   Despite this, many institutions persist in using ineffective tools that use models not designed for financial markets, and that only look at transactions in isolation.  However, in June, the FCA observed that “some participants were introducing initiatives to enhance transaction-monitoring capabilities, with increasing focus on network analysis and contextual monitoring, rather than monitoring driven by transactions alone.”  

The Challenges of Traditional Monitoring

Unlike traditional monitoring platforms that are inward-looking and reactive, a contextual monitoring approach can help organizations improve their risk detection capabilities in an area that is both complex and challenging to monitor.  

Traditional rules-based systems are too simplistic, and only detect the most apparent risks. They are complex to configure, requiring large volumes of parameters to address the many different types of clients using markets products. Institutions that have moved across transaction monitoring tools used for other parts of the group, or have attempted to use market abuse trade surveillance tools, have found that this approach generates high volumes of poor-quality alerts resulting in an overwhelming number of false positive alerts – without actually identifying suspicious activity. As data continues to grow at a staggering rate, this approach is not sustainable. A new approach is needed. 

A New Approach: Contextual Monitoring

Contextual monitoring connects intelligence across the enterprise with external data to create a holistic view of risk and manage a single coordinated response to threats.

Unlike traditional approaches, contextual monitoring empowers organizations to make trusted operational decisions by considering a wider set of data to better understand the business activity being monitored. This approach connects tens of billions of internal and external data points to create a single view, enriched with vital intelligence about the network of relationships between people, organizations and places.

By accessing the context needed to turn data into better decisions at scale, firms can harness the power of relationship networks to uncover hidden risk and opportunities quickly and accurately.    

The Must-Haves to Achieve Contextual Monitoring

Client Data  

“The FCA found that the primary driver of money-laundering risk is the customer, rather than products or delivery channels” 

-FCA Report: Understanding the Money Laundering Risks in Capital Markets

Knowing and understanding who or what an entity is, and how much risk they could present, is critical for ensuring the right monitoring is applied. To get a complete and accurate view of the customer, internal customer data and risk ratings is essential, but alone may not be sufficient in indicating the potential riskiness of a client or the controlling parties. External data and intelligence can be used to deliver context and reveal more up-to-date information that can assist the investigator in reaching an outcome. This data can come from sources such as corporate registry databases, external watchlists and other open sources of data that can provide additional context e.g. IciJ data. 

Some clients within financial markets may be considered inherently less risky since many are often other highly regulated financial institutions. However, certain client types or segments may require greater scrutiny. Examples include: Money Services Businesses (MSBs), Hedge Funds, Sovereign Wealth Funds, Telecoms, Oil & Gas or other extractive industries. 

Historic Cases  

Quantexa’s open architecture approach allows users to leverage the output from other monitoring tools that may have identified potential risk about the institutions’ customers. This context can then be used as part of the Quantexa scoring models. Typically, Quantexa not only uses the alert output, cases and SARs identified by incumbent transaction monitoring tools, but the output from trade surveillance tools given the fact that market abuse is one of the main (but not only, FATF cites 21) predicate offences of money laundering. 

Knowing that a certain customer has been involved in potential market abuse or market manipulation behavior is useful context for an investigator, particularly for those risk types such as wash trading that could be indicative of money laundering. This ensures that a financial crime lens can be applied to this behavior and potential dual track investigations conducted helping to ensure that the right disclosures are filed to regulators. This ecosystem approach can help join the dots about customer behavior and supports data partnerships within the organization. 

Settlements & Payments Data  

A number of cases evidence how financial markets businesses were used to illegally move money across borders, meaning settlements and payments data is critical for tracking the net inflows and outflows between connected parties. By extracting relevant information from the payments data and leveraging Quantexa’s entity resolution capabilities, it is possible to understand the beneficiary of the funds of the settled trade which might not be available in internal records alone.  

Understanding the money movements connected with a trade is absolutely critical to understanding the potential risks that you are exposed to. For example, in addition to identifying red flags such as settlements to third parties or to high risk jurisdictions, it is also possible to understand how patterns of behavior have changed over time, or to identify settlement chains and sequences of events that occur in the context of connected customer, counterparties and beneficiaries. 

As some market participants push to reduce the settlement period from T+2 nearer towards real time, the ability to dynamically evaluate the risk is becoming increasingly important. 

Trading Data  

Certain products that are more susceptible to misuse or high-risk typologies (e.g. mirror trading, wash trading, pump and dump) will require data from trading systems – data that is not available within the settlements data. Example fields that can be used include date, timestamp, buy/sell, volume, price, value, trader, book, salesperson, executed/amended/cancelled etc. 

Not all products within capital markets carry the same level of money laundering risks. Some products are inherently riskier because of the characteristics of the products, the customers that trade them, or the ways they can be traded – which can make them more susceptible to misuse or suspicious behavior.  

Characteristics that could present higher money laundering risk include: 

• Traded Over the Counter (OTC) rather than on exchange which generally attract more scrutiny by regulators and other market participants 
• “Cash like” or easily convertible to cash 
• Fast delivery and settlement (T+2) 
• Facilitate movement of funds cross border 

• Susceptible to layering  
• Products (e.g. bearer share instruments) or services (e.g. omnibus accounts) that can obscure information about the identity of the asset holder.  
• Products that have been susceptible to fraud and market abuse e.g. low priced securities 

Fines and regulatory scrutiny over low priced securities (including microcaps, penny stocks, pink sheets) mean these products regularly feature high on an institution’s product risk assessment alongside other products, such as Spot FX or Precious Metals.  

Other, more complex products may be less attractive to money launderers, especially if there are barriers to entry which prevent their use by certain types of clients. When applying a risk-based approach, it may therefore be unnecessary to bring in the trade data for these products, particularly in the initial stages of implementation.  

 

The PoC showed us that through the scoring process it was possible to generate fewer, better quality alerts that result in a more in depth investigation due to the data available in the network and transaction viewer.  

-Quantexa Customer 

Additional External Data Can Provide More Context & Assist Analysts to Make Better Decisions

Some models looking at certain trading or product risks may also benefit from market data to bring in pricing information, trading volumes etc. to give a more complete view of market activity and potential risk indicators. 

While the primary money laundering risk may well be the client, and there is risk at the product level, there are also other entities where entity resolution can be applied to identify potential risk. This could include areas like Securities Issuers (an area of focus for FINRA in Reg Notice 21-03) but also the service providers to these Issuers ( think Attorneys, Auditors, and Transfer Agents.) Data sources that provide information about  OTC markets can help provide this detail and risk indicators that can be used within the Quantexa scoring models and displayed within the networks. 

Investigative Capabilities & Thematic Reviews  

In addition to alert generation, having all the data in one place supports users to perform proactive intelligence led investigations (e.g. first line or regulatory enquiries) and to dynamically respond to new or emerging risks or market events. It ensures better sharing of data between teams and supports dual track investigations for AML and Market Abuse risks, helping to eliminate duplication and ensure the right disclosures are filed to regulators. This approach supports data partnerships within the organization. Businesses can use the tool to perform risk assessments of new products or books of business to determine what monitoring is required, and the need for new or amended models. 

The solution was tailored to the markets business which was important to us to work with a vendor who has expertise in this area given a weakness of our current system was a lack of markets specific risks and data. 

-Quantexa Customer 

Time to Act 

The FCA found that “participants were generally at the early stages of their thinking in relation to money-laundering risk and need to do more to fully understand their exposure”. Two years later the expectation will be that considerable progress should have been made with improvements to technology or the implementation of new tools to address gaps in coverage and improve risk detection.  

It is not just UK based institutions that must act now. 2020 saw the first joint AML enforcement action by FINRA, SEC and CFTC (their first AML / BSA related enforcement) and the largest AML enforcement involving the Securities industry in North America. 

Don’t be left behind by those that have implemented contextual monitoring, or let data hold you back.