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Six Best Practices That Will Transform Transaction Monitoring Systems in Financial Markets
With regulatory pressures mounting and traditional systems falling short, financial institutions face growing challenges in monitoring financial markets.
With increasing regulatory pressures and the limited effectiveness of traditional transaction monitoring (TM) systems, financial institutions are being driven to upgrade their monitoring solutions for financial markets. However, implementing a new TM system can be a complex challenge, especially in the dynamic world of financial markets, where diverse data formats, multiple asset classes, numerous trading venues, and less established risk typologies add to the difficulty. In this article, we uncover six essential best practices for ensuring the success of TM projects in the ever-evolving environment of financial markets.
1. Start with risk assessment
A solid risk assessment is the foundation of effective transaction monitoring. It helps firms take a risk-based approach while ensuring regulatory compliance. A well-executed risk assessment across AML typologies can uncover risk areas that need targeted controls. In addition to evaluating risks based on customer profiles and geographical footprint, a critical step is accurately mapping AML typologies to the right products. Traditionally, financial institutions have applied all AML risk scenarios to every product, leading to a huge number of false positives and operational inefficiencies. A precise typology-to-product mapping, with input from product experts, is crucial to tailoring monitoring to effectively target the risk.
2. Adopt a holistic approach to risk finding
Historically, TM systems have struggled to deliver results in financial markets, as reflected in the low SAR (Suspicious Activity Report) rates. One major reason for this is that traditional monitoring approaches have often focused on isolated risk factors rather than looking at the bigger picture. A holistic approach, however, combines multiple data points to create a fuller view of potential suspicious activity. Not only does this approach help identify risk more effectively, but it also reduces false positives. For instance, a shift in customer behaviour may simply be a response to market volatility. By considering peer activity and market trends, a holistic approach helps filter out this noise and enhance the accuracy of monitoring.
3. Prioritize early access to data and data SMEs
Data is the backbone of any TM project, and in financial markets, it can be especially complex to source and interpret. To build a comprehensive 360-degree view of customers and other entities, it’s crucial to tap into both internal and external data sources. Since trade and settlement data structures can vary widely by product type and systems, gaining a solid understanding of these nuances early in the implementation phase is vital for success.
Securing early buy-in from system owners and gaining access to data—whether it’s full or even sample data—along with input from data SMEs, can greatly enhance design decisions. This, in turn, boosts the accuracy, effectiveness, and efficiency of the monitoring system, setting a strong foundation for better, more informed monitoring efforts.
4. Expand monitoring to include trade data
Traditionally, TM vendors and financial institutions have relied primarily on settlement data to detect AML risks in financial markets, as this is where the flow of money is captured. However, focusing solely on settlement data is no longer enough. To effectively spot suspicious behaviour, financial institutions must broaden their approach by incorporating trade data. Trade data provides the essential context needed to uncover risky patterns, such as mirror trading, penny stock manipulation, and other illicit activities. When linked with settlement data, trade data creates a more comprehensive view, enabling institutions to gain a fuller understanding of the transactions and patterns of activity in question.
5. Allow sufficient time for tuning
Proper tuning is essential for striking the right balance between system sensitivity and the operational efficiency of the system. Monitoring systems that are too sensitive can overwhelm teams with false alerts, leading to inefficiencies, increased operational costs, and fatigue. On the other hand, systems with overly lenient thresholds may fail to catch critical suspicious activities, exposing institutions to potential money laundering risks, regulatory penalties, and reputational damage. Unfortunately, the tuning phase is often rushed to meet project deadlines or to make up for delays elsewhere, resulting in operational headaches down the road. To avoid these pitfalls, it’s crucial to allocate sufficient time within the project timeline for thorough tuning. By doing so, organizations can ensure that their monitoring system is both effective and efficient, minimizing false positives while safeguarding against emerging threats.
6. Involve end users early in the process
Engaging investigators and other end users early in the process can provide invaluable insights and help shape a more effective system. Their real-world experience with day-to-day operations means they have a practical perspective on what works, what doesn’t, and where improvements can be made. Leveraging their input in key design decisions and system configurations is essential for developing a solution that is practical, intuitive, and aligned with the operational team's needs. End users can also provide insights into typical mitigating factors which can then be built into the detection system to reduce noise. This early involvement not only ensures that the system is built with user requirements in mind but also leads to smoother user adoption when the system goes live. Moreover, it fosters better feedback loops, allowing for continuous improvement and quicker adjustments once the system is fully operational.
The Quantexa advantage
Quantexa’s Contextual Monitoring approach is a paradigm shift from traditional transaction monitoring and is currently supporting various organizations with their AML modernization journey. Please reach out to discuss how Quantexa’s Decision Intelligence platform can help institutions uncover new risk in capital markets while simultaneously gaining operational efficiencies and improving decision-making.
