AML for financial markets: The paradigm shift to contextual monitoring
Written by Quantexa
Published: 23rd Jun 2020
Today, many financial institutions find that their traditional anti-money laundering (AML) transaction monitoring systems are insufficient when relied on to detect risk in financial markets. In short, they fail to enrich, connect, or operationalize the various forms of data associated with such markets, often causing investigators to miss suspicious patterns of behavior that may exist within this data. Similarly, these legacy approaches overburden investigators with exceptionally high volumes of false positive alerts, making the process of detecting risks related to financial crime to be both inefficient and ineffective.
Now, industry expert Scott Nathan, Head of Innovation for AML and Risk at State Street, has been working with Quantexa on a new approach to AML in markets, one that harnesses contextual decision intelligence for more efficient and effective risk detection in financial markets.
Something Had to Be Done
Recent money laundering cases have resulted in fines approaching a billion dollars for numerous tier one banks. In these cases, the critical factor was the relationships between both sides of the trades. Using a rule-based approach, financial institutions may capture one or more of the trades associated with a scheme yet fail to act on them because the institution lacks context around those trades.
Conversely, using contextual monitoring that leverages entity resolution, network analytics, and advanced analytics offers a new approach to generating insights that empower decision making. Using this approach, financial institutions can resolve related entities into one and make connections between distinct entities through interactions and relationships to derive context, and this context gives investigators a far more unified and accurate picture of where true risk lies. Furthermore, advanced analytics at the transaction, entity, and network-level can support the generation of risk-scored alerts, allowing analysts to prioritize and focus their efforts.
Unlocking New Opportunities and Insights
When Scott Nathan joined State Street, he was excited to learn that the company was interested in trying something new, as the legacy approach to AML was falling short. “We process and persist data in ways that are unlike any traditional commercial bank, and that presents unique challenges but also really interesting opportunities to use new technology,” said Scott.
Scott acknowledges that up until recently, many financial institutions relied on linear models, which targeted many scenarios and specific behaviors, yet lacked the context to identify risk that may look different from that pre-defined set of typical typologies. This resulted in a lack of visibility into activity happening throughout the markets and across the financial services ecosystem.
Today, Scott reports that State Street uses AI and machine learning technology to detect, analyze, and visualize markets-related risk. The technology enables the bank to enrich data to create a single entity view and then build networks to understand customer activity, inherent risk, and how its products are potentially being abused. This approach leads to a more streamlined and optimized detection process.
A context-led approach empowers State Street to identify risk with fewer false positives, enabling investigators to focus on genuine risk and make more accurate decisions in their investigations. Most importantly, it helps Scott and his colleagues protect both the company and the financial system as a whole from the dynamic threats associated with financial crime.
To learn more about contextual monitoring and how State Street has implemented this approach to improve its markets AML processes, watch the full webinar here.
You may be interested in…
Creating Value For The Enterprise Using Data
In this episode, Vishal Marria, CEO at Quantexa, speaks with the Chief Data Scientist at Dun & Bradstreet, on overcoming common data challenges, digital resilience, and creating enterprise value using in AI and data & analytics.
How Danske Bank Is Adopting Data and Analytics Technology
To maximize the value of data, enterprises need the right IT infrastructure in place. In this episode, Bo Svejstrup, CIO at Danske Bank discusses resolving legacy data challenges, improving collaboration between business and IT, and the future of cloud adoption.
How Allianz Is Transforming Using Tech
Quantexa speaks with Allianz CEO to discuss the challenges of adopting technology across the enterprise, the role of data in customer-centricity, and leading transformation in the insurance industry.
New Risk Factor Guidelines to Strengthen Financial Crime Detection
The updated European money laundering and terrorist financing risk factor guidelines highlight taking into account “wider, contextual factors.” Find out how contextual decision intelligence can ensure enhanced risk detection and due diligence measures.
QuanCon 2021: Meaningful Data for Trusted Decisions
QuanCon 2021 Virtual explored compelling thought leadership from the Altimeter Group and Accenture, knockout presentations from State Street and ABN AMRO, and an in-depth show and tell on Quantexa’s new capabilities.
Tech For Good: How Standard Chartered Bank Is Revolutionizing Investigations
Learn how Standard Chartered Bank has made huge strides in harnessing the power of data to revolutionize financial crime investigations.
Reduce the tax gap, identify fraud and non-compliance, and operate as efficiently as possible with limited resources.
Reveal hidden risks and detect criminal activity faster. Reduce false positives to manage the cost of compliance. And improve investigations to make faster and more consistent decisions at scale.
Customs Agencies & Border Control
Contextual Decision Intelligence enables faster decisions, increased revenue collection and enhanced compliance. The Quantexa platform enables Customs and Border agency teams to analyze data successfully, automate and accelerate decision-making, and achieve improved results.
Identify potentially fraudulent activity by looking at people or transactions in isolation. Understand the context surrounding the organizations you do business with to make fast, accurate decisions.
Fraud, Waste & Abuse
Empower your team with the best tools available for today’s challenges to identify and prevent fraud, waste and abuse with contextual decision intelligence software.
Understand your customers, their business structures and supply chains. Make better lending decisions, faster. And support digital risk transformation.
Know Your Customer
Reduce significant manual effort across onboarding, refreshes and remediation. Automate checks, implement continuous monitoring, and focus on contextual decision making.
Generate a complete view of the context around your customers and prospects to build better relationships, reduce attrition and find hidden opportunities.
Master Data Management
Connect all data—internal and third party—to create a joined-up, contextual view of all the relationships between your customers and every other domain.
See how we help to reduce costs and improve coverage for financial crime compliance.
See how our platform uses contextual analysis to turn data into a high value asset.
See how our platform uses financial crime technology to enhance your existing IT ecosystem.