More than ever before, financial institutions and fintech companies are recognizing the need to adopt cutting-edge AI technologies to combat financial crimes, and transaction monitoring is a critical aspect of these anti-financial crime (AFC) efforts. However, traditional rules-based transaction monitoring systems which rely on largely binary triggers to detect pre-defined criminal behaviors generate a huge amount of false positives and repeat alerts. This well known limitation results in investigators sifting through vast volumes of noise to confirm true suspicious activities. In addition to being time-consuming, this approach means some crimes never get reported in time — or at all — with predictable consequences.
This is where AI and machine learning come into play — these capabilities enable organizations to more effectively process large volumes of data, detect undefined patterns, and significantly improve the efficiency of their transaction monitoring systems. By effectively applying machine learning, organizations can detect known risks with greater speed and accuracy while also increasing their detection of novel, previously unknown criminal activities.
However, implementing machine learning can be easier said than done, as it’s sometimes difficult to know where to get started.
Ensemble modeling is a game-changing approach that’s largely still in its early days of adoption within the anti-financial crime landscape. In transaction monitoring, there are a wide range of factors that need to be accounted for when assessing behavior — multiple data points must be considered in order to ensure effective detection. It’s extremely difficult to take account of all of these factors in a single model.
To resolve this issue, ensemble modeling allows the use of multiple, smaller models that are highly focused on a specific sub-problem. It’s the outcome of these specialized models that determine whether the behavior is suspicious. This approach is especially effective for transaction monitoring because it offers a comprehensive view of the complex data that’s typical in financial services.
While it’s conventionally accepted to seek out experts to get the best information, it’s often proven that a crowd of laymen can supply better aggregated knowledge. As author and New Yorker business columnist James Surowiecki puts it, “When our imperfect judgments are aggregated in the right way, our collective intelligence is often excellent.”
This logic is at the heart of ensemble modeling: While conventional reasoning may suggest building the biggest model that can encapsulate the world, it’s the combined performance of smaller, less perfect models that gets the best result.
Our team has found that ensemble modeling offers the most practical approach to detection: Each of the models that it comprises offers complementary information about the behaviors, and when these are combined, they improve the performance of detecting a wider range of unusual behaviors while also promoting higher accuracy and a reduced number of false positives.
Ensemble models allow organizations to analyze different components of transactions and focus on specific aspects of the task at hand, whether it’s examining the size of a transaction, its location, or the nature of the counterparties.
Financial institutions and fintech companies can reap the following benefits delivered by the ensemble model:
As mentioned above, explainability in AI is not merely a regulatory requirement — it’s a critical component of analyst and investigator efficiency. Simply put, financial crime investigators need transparency over the factors that determine AI-driven outcomes, as having an understandable explanation for why a transaction was flagged as suspicious is essential for effective investigations. Without having proper explanations, investigators may struggle to initiate their investigations, which can lead to inefficiencies and delays.
“When we say something is important we can tell you why,” Resistant AI CEO Martin Rehak said in an interview with insideBIGDATA. “We can tell you which indicators point to this. We can essentially write a report for each finding that says this should be a high risk because of these factors.”
On a related note, the concept of interpretability is often used in the context of explainability. It implies that stakeholders can comprehend the main drivers of a model-driven decision. Ensemble modeling inherently supports interpretability, as it involves combining simpler, more explainable models. As a result, these models provide interpretable outputs, making it clear why a particular transaction was flagged as high-risk.
In an ensemble, individual models assess just one factor in a transaction and provide explanations in human-readable language. This approach enhances both interpretability and transparency, and importantly, it aids investigators in understanding the reasons for alert generation (especially when transactions are blocked or held for further investigation). This level of detail is key in building trust and ensuring effective decision-making.
Using important takeaways from our team’s engagements with customers, we’ve established a reliable, five-step process for organizations that are interested in implementing machine learning:
AI is no longer simply a buzzword — it’s an umbrella term for many actionable programs that financial institutions and fintech companies can implement today.
As organizations across the globe continue to face evolving threats, the adoption of AI and machine learning approaches like ensemble modeling is essential for staying ahead in the battle against financial crime. By properly understanding and implementing this approach, organizations can protect their operations and comply with regulations — all while maintaining the trust of their customers and stakeholders.