Cleaning up a dirty business: Money laundering in financial markets
In recent weeks, regulators have reprimanded leading investment banks for insufficient money laundering controls within their markets and broker-dealer business.
This comes as no surprise; few have invested adequately in protective controls, which creates unnecessary exposure. In its 2018 Report on Examination Findings, the Financial Industry Regulatory Authority (FINRA) noted increased trading within firms by foreign entities in similar low-float and low-priced securities. And in some instances, firms failed to question the commonality of ownership status of these foreign legal entity accounts—which raised concerns.
This lack of oversight represents one reason low-priced securities, foreign exchange and precious metals make easy targets for money launders, tax evaders and terrorists. Increased scrutiny on traditional banking systems also moves money laundering activities to less regulated securities markets.
Yet some financial institutions are marching forward, which paves the way for the next generation of anti-monitoring laundering (AML) systems that embrace and harness techniques worthy of 2019. Here’s a look at how this works.
Acknowledging complexity, accepting risk
This journey began when institutions recognized their exposure to these markets. To date, few had identified suspicious activity. Yet the lack of “known” bad behavior only proves it’s not being caught, not that it doesn’t exist.
And this makes sense. Foreign exchange and precious metals serve as vehicles to transfer money or goods between people, companies and across borders. Is that so different to a cash deposit or wire?
Low-priced securities, by their very design, are associated with small, easy-to-set-up companies in some of the highest risk industries. Despite the initial honorable intention of this market’s existence, it ranks as notoriously dicey.
And the realization of the complexity of financial markets (compared to traditional banking) only compounded risk acceptance.
Take a low-priced security; traditional methods might look at the deposit, purchase or sale of it in isolation. Yet experts recognize that any fraud, money laundering or market abuse event involves a huge web of interconnected parties: from customers, broker-dealers and counterparties; to issuers, auditors, legal counsel and other service providers. Understanding this network and its wider context marks the first step in adopting more efficient, effective ways to identify and manage risk within these markets.
Counting on context
Understanding the many dimensional facets of a market’s products, pioneering institutions sought a more intelligent approach to understand and prevent this risk. They’ve recognized that as part of their monitoring process, putting some of their best experts to work during their investigation could accurately identify risk.
Put simply, their computers receive information that allows them to think more like humans.
In real terms, that means providing the computer with context about all elements of the interconnected web—transactions and trades; issuers and products; customers; broker-dealers; and counterparties. This context can then form the foundation of automated risk assessment that supports the paradigm shift from transactional to contextual monitoring.
Contextual monitoring provides gains in AML effectiveness and efficiencies. In effect, it replicates the laborious parts of investigative process in an automated yet fully transparent and understandable manner. Furthermore, a contextual approach naturally capitalizes on the big data and open analytics strategies banks now heavily invest in. This results in far less data replication, re-use of existing security models, and an open, extensible architecture.
Proof that it works
Contextual monitoring has proven more effective and efficient when identifying risks within foreign exchange, equities and commodities. This is achieved by:
- Wider use of available data as part of a risk assessment. This includes using improved company and Know Your Customer (KYC) data, as well as third-party resources to fully understand company structures.
- Extensive use of analytical methods. Behavioral analytics, peer group analysis and anomaly-based detection provide a greater assessment of normal-versus-abnormal transactions.
- Use of dynamic entity and network-based techniques. This means activity can be risk assessed holistically across connected people, companies and related entities.
Through this approach, institutions can proactively stop criminals who abuse this market, with some of the very first regulatory Suspicious Activity Reports now being issued.
It’s like a game of chess: Each side makes its move and the other responds. We must make sure the criminals are the ones in checkmate, unable to do a risk assessment of their own.
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For more articles like this, check out our recent BAI Executive Report: Fraud and cybersecurity: Staying steps ahead.