Each year, a staggering $800 billion to $2 trillion (equivalent to 2–5% of global GDP) is laundered through the world’s financial systems. From organized crime networks to sophisticated cyber-fraud rings, illicit funds are moved through legitimate channels at a scale that strains regulators, financial institutions and law enforcement alike.
To counter this, anti-money laundering (AML) programs rely increasingly on advanced technologies such as AI and machine learning. These systems sift through vast numbers of transactions to detect patterns, spot anomalies and generate alerts. But machine learning can only be as effective as the data it learns from — and that’s where a crucial, yet often overlooked, process comes into play: labeling.
Why Transaction Labeling Matters in AML Machine Learning
Just as a facial recognition system needs to be told which images contain a face, an AML model must be shown which transactions were genuinely suspicious and which were legitimate. Labeling turns raw historical transaction data into the “ground truth” that machine learning systems need to learn and improve.
In supervised learning, labels are the benchmark. Mark a flagged transaction as “suspicious” and the model refines its sense of risk; label one as “legitimate” and it learns to avoid false alarms. Feed these outcomes back into the system, and you’ve created an AML feedback loop: a cycle that sharpens accuracy with every new data point.
Without clear, consistent labeling, even advanced AML tools can become blunt instruments, swamping compliance teams with false positives while missing more sophisticated laundering techniques. But with it, labeling transactions in machine learning becomes a powerful way to adapt to evolving threats, focusing human attention where it matters most.
Types of Labels for AML Models
In AML machine learning, not all labels have the same purpose. To train models effectively, it’s important to use a structured transaction classification framework that captures different dimensions of risk. At the broadest level, risk outcome labels record the result of an investigation: whether a transaction was flagged, confirmed as suspicious or ultimately cleared. These labels form the foundation of any feedback dataset, giving the model a clear sense of which alerts were accurate and which were false positives.
Beyond the outcome, behavioral labels provide deeper insight into the patterns behind illicit activity. For example, a transaction might be linked to mule activity, where individuals move funds on behalf of criminals, structuring, which involves breaking large amounts into smaller transactions to evade detection, or layering, the process of moving money through complex chains to obscure its origin. These labels teach the model to recognize behaviors that often precede or accompany money laundering, even before a case is fully confirmed.
Finally, entity-based labels help capture risk at the organizational or geographic level. Transactions connected to a shell company (a legally registered business entity that exists mostly on paper) or originating from a high-risk jurisdiction can be marked accordingly, allowing the model to weigh the reputational and jurisdictional risks alongside transactional patterns.
Using a variety of AML labels in this way enriches the dataset, enabling models to learn from multiple perspectives. The multidimensional approach to risk tagging in compliance makes it possible to train systems that are both more accurate and more adaptable to the shifting tactics of financial crime.
How to Label Transactions for AML Machine Learning
Building a high-quality labeled dataset is the backbone of any effective AML machine learning system. Done well, it ensures the model learns from accurate examples and can adapt to new threats.
1. Define a Clear Label Taxonomy: Start by establishing a consistent set of labels that covers risk outcomes, behavioral patterns and entity risks. This taxonomy should be standardized across your organization so that every investigator applies the same terms to similar cases. Consistency is key to building a usable dataset.
2. Gather and Prepare Historical Data: Collect past transactions along with their investigation results. Where possible, include both confirmed suspicious cases and cleared transactions. A balanced dataset helps the model learn to distinguish between genuine threats and false positives, a crucial part of labeling transactions for AML machine learning.
3. Apply Labels to Transactions: Assign the appropriate labels to each transaction. Analysts can do this manually, programmatically via APIs or a combination of both. Accuracy at this stage is vital; mislabeled data will undermine model performance.
4. Implement Feedback Loops: Feed the labeled outcomes back into the model to enable continuous learning — an AML feedback loop implementation. Each time an investigation concludes, its label updates the model’s understanding of risk patterns, helping it adapt to new laundering techniques.
5. Validate and Maintain Label Quality: Regularly audit your labeled dataset to catch inconsistencies or outdated classifications. As regulations, typologies and laundering tactics evolve, update the taxonomy and relabel historical cases where necessary to keep the dataset relevant.
By following these steps, you can build a labeled dataset for AML that fuels a learning system capable of detecting nuanced, emerging threats. The result is a model that not only flags suspicious transactions more accurately but also reduces noise, freeing compliance teams to focus on the alerts that matter most.
Best Practices for Building Labeled AML Datasets
A strong AML dataset is more than just well-tagged transactions: it’s a living resource that evolves with threats. Prioritize consistency by training analysts on the same labeling standards, and balance by including both suspicious and legitimate cases. Build in quality controls such as periodic audits to catch errors early, and maintain adaptability by updating labels when new typologies emerge. Finally, ensure traceability: each label should be backed by clear investigation notes, so the reasoning remains transparent for model retraining and regulatory review.
Labeling vs. Tagging: What’s the Difference?
In the realm of AML compliance, the terms labeling and tagging might appear interchangeable, but in practice, they serve different purposes.
Tagging is the flexible, preliminary categorization of transactions. It adds risk indicators like “high-risk jurisdiction” or “suspicious pattern” to help triage cases. Tags guide investigations but aren’t always validated or standardized.
Labeling is more formal, used to create the “ground truth” for machine learning. Labels reflect confirmed outcomes and follow a consistent taxonomy. High-quality labeling powers AML feedback loops, feeding verified results back into models to sharpen detection.
In short, tags help sort potential risks quickly, while labels provide the confirmed, structured data models need to learn effectively. Together, they form a complete workflow from first suspicion to lasting model improvement.
How SEON Helps Automate Transaction Labeling
SEON’s Label API enables teams to apply structured outcomes, such as “suspicious” or “legitimate”, directly to transactions. The verified results can then power SEON’s hybrid machine learning models, combining the transparency of rules-based insights with the adaptability of advanced algorithms. Labeled outcomes feed into an AML feedback loop, enabling continuous improvement and sharper detection accuracy.
This process is enhanced by digital footprint analysis and device intelligence, enriching each transaction with additional context, such as IP risk, browser and device attributes or online behavior patterns. The extra data points give compliance teams a clearer view of risk, making reviews faster and decisions more accurate.
SEON’s AML transaction monitoring combines configurable rules, behavioral analytics, and enriched intelligence to deliver relevant, timely alerts in real time. All monitoring, labeling, and investigation tools are unified within a single AML command center, supporting complete workflows from screening through regulatory reporting—without ever breaking the data chain.
By closing the loop between detection, enrichment and feedback, SEON helps AML systems learn faster, adapt to emerging threats and deliver a compliance process that’s both more efficient and more precise.
Discover how automated transaction labeling, enriched with digital footprint and device intelligence, can transform your AML processes.
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Frequently Asked Questions
It’s the process of assigning clear, standardized outcomes, such as “suspicious,” “cleared” or “structuring,” to past transactions. These labels form the verified dataset that machine learning models use to recognize risk patterns and improve detection accuracy.
Yes. For supervised AML machine learning, labels are essential because they provide the “ground truth” that guides how a model learns, adapts and evaluates its own predictions. Without labeling, the system can’t reliably distinguish between legitimate and illicit activity.
Definitely. Consistent and accurate labeling enables models to refine their decision-making, helping them better identify real threats while reducing the number of alerts generated for legitimate transactions. This means fewer wasted investigations and faster responses to genuine risks.
Rule-based flags are triggered by predefined conditions, such as transactions over a certain value, without confirming whether the alert is accurate. Labels, on the other hand, record the verified outcome of an investigation and are used to train adaptive machine learning models that evolve over time.
Sources
- WORLDMETRICS.ORG REPORT 2025 – Money Laundering Statistics
