Beyond the Basics — A Multi-layered Approach to Transaction Monitoring

In the rapidly evolving world of fintech and banking, the stakes have never been higher when it comes to ensuring the integrity of financial transactions. As financial crime grows more sophisticated, the traditional methods of transaction monitoring have proven inadequate. It’s time to think beyond the basics and explore a multi-layered approach that not only meets today’s challenges but also anticipates the threats of tomorrow.

Current Landscape: Transaction Monitoring’s Old Playbook

Transaction monitoring has long been a cornerstone of compliance strategies within the finance ecosystem. Typically, this process was driven by a rule-based system, where specific criteria are set to flag suspicious activities. While this method was foundational, it is increasingly clear that relying solely on a single-layer approach is no longer sufficient.

Traditional systems are largely reactive, dependent on periodic updates and manual reviews that can’t keep pace with the dynamic nature of financial crime. As criminals become more adept at evading detection, these systems often fail to identify subtle or emerging threats. The result? Financial crime is able to flow through the financial system while a flood of false positives obscure genuine risks from compliance teams.

In today’s world, sticking to the old playbook isn’t just inefficient – it’s risky. Financial institutions need a strategy that goes beyond the basics to safeguard their operations and ensure that their customers are transacting with reputable institutions that effectively keep financial crime from infiltrating their systems.

Cracks in The Armor: Challenges of the Traditional Approach

If transaction monitoring was a fortress, the traditional approach would be its crumbling walls. The challenges inherent in this method are significant and increasingly unsustainable in the face of modern threats.

1. A Deluge of False Positives: The most glaring issue with rule-based systems is their tendency to generate an overwhelming number of false positives. Not only do false positives waste valuable resources, but they also create operational bottlenecks that slow down the entire process, impacting customer experience.

2. Static Rules, Dynamic Threats: Financial crime is anything but static, yet traditional transaction monitoring relies on predefined rules that struggle to adapt to new and evolving threats. Criminals are constantly devising new ways to bypass detection, and static rules simply cannot keep up with these innovations. This mismatch leaves institutions vulnerable to complex schemes that go unnoticed.

3. The Hidden Dangers: Perhaps the most concerning limitation of the traditional approach is an inability to detect hidden or complex patterns of suspicious behavior. These systems are often good at catching the obvious red flags but fail when it comes to recognizing more sophisticated forms of financial crime such as money mules, trade-based money laundering, layering, cryptocurrency mixing services, complex corporate structures, and others. 

4. Resource Drain: Manual review processes are resource-intensive, requiring significant headcount to sift through alerts, many of which are false positives. This not only drains resources but also diverts attention from more strategic activities that could better protect the institution from financial crime risks.

Breaking the Mold–– Why Multi-layered Monitoring is the Future

Given the shortcomings of traditional transaction monitoring, it’s clear that a new approach is needed —one that is as dynamic and multifaceted as the threats it aims to combat. Enter the multi-layered approach, a paradigm shift in the world of compliance and risk management.

1. A Comprehensive Defense: The primary advantage of a multi-layered approach is its ability to address the limitations of traditional systems. By integrating multiple layers of analysis — such as AI-driven analytics, machine learning, and behavioral analysis — this approach provides a comprehensive defense against a wide range of threats. Each layer is designed to catch what the others might miss, creating a robust and resilient monitoring system.

2. Adaptable and Future-Proof: One of the most significant benefits of a multi-layered system is its adaptability. Unlike static rule-based systems, multi-layered approaches can evolve with emerging threats. Machine learning algorithms, for example, can learn from the data and improve accuracy of detection, allowing the system to detect nuances of unwanted behavior. This adaptability not only improves current defenses but also ensures that the system remains effective in the future.

3. Efficiency at its Best: With a multi-layered approach, institutions can significantly free up resources for more strategic tasks. By leveraging AI, these systems can quickly and accurately identify actionable threats, allowing compliance teams to focus their efforts where they are most needed. The result is a more efficient, effective, and streamlined transaction monitoring process.

Layer by Layer –– Building a Multi-layered Monitoring System

So, what exactly does a multi-layered approach look like? It’s not just about stacking different solutions on top of one another; it’s about creating a cohesive system where each layer complements and enhances the other. Here is a breakdown of the key components.

1. The Foundation: Rule-Based Monitoring. At the core of any transaction monitoring system is a rules-based approach. This layer handles the basics, catching the obvious red flags through predefined criteria. While this may be the simplest layer, it’s also essential — it provides a solid foundation upon which the other layers can build.

2. The Risk Lens: Risk-Based Analysis. Next comes risk-based analysis, which evaluates transactions based on their level of risk. This layer goes beyond static rules, focusing on specific risk features or categories such as customer behavior, transaction history, and geographic location to assess the likelihood of suspicious behavior. This enables organizations to filter by higher risk activities or entities to help prioritize investigations and allocate resources more effectively.

3. The AI-Advantage: Machine Learning. This layer uses machine learning to analyze large volumes of data, identifying patterns and anomalies that would be impossible for a human to detect. Over time, the system learns from past experiences, continually refining its ability to spot suspicious activity. This layer is particularly effective at detecting unknown threats – those that haven’t been codified into rules.

4. The Human Element: Behavioral Analysis. The final layer is behavioral analysis, which focuses on the actions of individuals rather than just the transactions themselves. By analyzing patterns of behavior — such as unusual spending habits or sudden changes in transaction volume — this layer can flag suspicious activities that would otherwise go unnoticed. Behavioral analysis adds a crucial human element to the system, providing context and nuance that purely data-driven approaches might miss.

A New Standard–– The Benefits of Going Multi-layered

The advantages of adopting a multi-layered approach for transaction monitoring are manifold. For institutions that embrace this shift, the benefits are both immediate and long-term.

1. Improved Accuracy: By increasing the detection of true positive alerts, multilayered systems allow compliance teams to focus on investigating genuine threats. This not only improves operational efficiency but also enhances the overall effectiveness of the monitoring process.

2. Enhanced insights on risk exposure: By leveraging AI and machine learning, multilayered systems offer profound insights into customer risk exposure through comprehensive analysis. This capability allows organizations to gain a deeper understanding of their customer database and implement appropriate due diligence measures based on accurate risk assessment.

3. Greater configurability: A multi-layered approach, supported by cloud integration and APIs, offers the flexibility to consolidate data points and conduct efficient analysis. This setup not only allows for updates to deterministic rules in response to regulatory changes but also leverages machine learning for continuous learning and detection, ensuring the monitoring system remains effective against new threats.

4. Scalability: As institutions grow, so do their transaction volumes and the complexity of the threats they face. A multi-layered system is scalable, capable of handling increased workloads without sacrificing performance.

5. Improved Quality of SARs: The multi-layered approach improves the quality of Suspicious Activity Reports (SARs) by offering analysts more precise analysis and understanding of potential risk to inform decision making.

Measuring Success — KPIs for Multi-layered Monitoring

To truly understand the impact of a multi-layered approach, institutions need to measure its success through key performance indicators (KPIs). These metrics provide valuable insights into the efficiency and effectiveness of the system, guiding continuous improvement efforts.

1. Detection of True Positives: One of the most critical KPIs is true positives for high quality SARs. By tracking how many alerts are flagged as genuine threats versus false alarms, institutions can gauge the accuracy of their monitoring system and make adjustments as needed to avoid regulatory repercussions.

2. Decision-Making Accuracy: The percentage of transactions reviewed and analyzed through the multi-layered monitoring system reflects the system’s effectiveness in evaluating behavior and linked networks. This metric indicates how often these analyses lead to accurate and informed decisions on whether to submit a Suspicious Activity Report.

3. Identification of Complex Threats: Another important KPI is the system’s ability to identify complex threats  — those that go beyond the obvious red flags. By tracing the number and nature of these threats, institutions can assess the effectiveness of their multilayered approach and identify areas for improvement.

4. Explainability of Alerts: This KPI focuses on the system’s ability to provide clear, understandable explanations for flagged alerts using transparent AI. This approach ensures that institutions can effectively report and liaise with regulators, enhancing the system’s credibility and facilitating smoother regulatory interactions.

5. Compliance Rates: Finally, compliance rate is a crucial KPI for measuring how well the system adheres to regulatory requirements. By monitoring this metric, institutions can ensure that their transaction monitoring processes meet the necessary standard, reducing the risk of penalties and enhancing their reputation with regulators.

Embrace the Multi-layered Future

In the battle against financial crime, the traditional approach to transaction monitoring is no longer enough. It’s time to elevate your defense by embracing a multi-layered approach that provides a comprehensive, adaptable and efficient solution. By integrating multiple layers of detection and analysis, institutions can protect themselves against threats  —both known and unknown.