Beyond the Alert History: How Synthetic Data Elevates Sanctions Screening Validation

Sanctions screening systems are the most common tool to identify the potential of dealing with sanctioned entities and navigating the complex environment of economic sanctions. Traditionally, these systems are calibrated and validated using historical alert data, and manually crafted test datasets. While this approach offers valuable insights, it inherently limits the scope of validation to past scenarios. To proactively address emerging threats and reduce false positives, integrating synthetic data that looks to exercise edge cases into the validation process is essential.

The Limitations of Historical Alerts

Historical alerts provide a retrospective view of a system's performance, highlighting instances where the system successfully identified potential risks. However, this backward-looking approach has inherent constraints:

• Incomplete Coverage: Historical data may not encompass the full spectrum of potential risk scenarios, especially novel or rare events.

• Bias Reinforcement: Relying solely on past data can perpetuate existing biases within the system, hindering its adaptability to new threats.

• Lack of Ground Truth: In many cases, the true nature of flagged entities remains uncertain, complicating the assessment of the system's accuracy.

These limitations underscore the need for a more dynamic and comprehensive validation approach.

Introducing Synthetic Data into Validation

Synthetic data refers to artificially generated information that mimics the statistical properties of real-world data. In the context of sanctions screening, synthetic data can be crafted to simulate a wide array of scenarios, including those not present in historical records.

Benefits of Synthetic Data

1. Enhanced Scenario Coverage: Synthetic data allows for the creation of diverse test cases, including edge cases and rare events, ensuring a more robust validation process.

2. Controlled Testing Environment: With synthetic data, the ground truth is known, enabling precise measurement of the system's performance metrics.

3. Privacy Preservation: Utilizing synthetic data mitigates concerns related to data privacy and confidentiality, as it doesn't involve real customer information.

Applications in Sanctions Screening

Reducing False Positives

False positives are a significant challenge in sanctions screening, leading to unnecessary investigations and resource allocation. Synthetic data can be employed to fine-tune the system's parameters, helping to distinguish between legitimate and suspicious entities more effectively.

For instance, by generating synthetic profiles that closely resemble legitimate customers, validators can assess and adjust the system's sensitivity, reducing the likelihood of false alerts. This form of testing also allows teams to assess the impact of configuration changes before deployment—lowering operational risk while improving efficiency.

Anticipating Novel Threats

Financial crime tactics are continually evolving, with adversaries devising new methods to bypass existing controls. Synthetic data enables the simulation of these emerging threats, allowing systems to be tested and fortified against potential vulnerabilities.

This is not a hypothetical concern. Research in adversarial machine learning has shown how slight perturbations in input data can cause significant changes in outcomes—an effect that could be exploited by actors seeking to evade detection. Simulating these adversarial examples proactively has become an increasingly recognized best practice in financial services risk testing.

A study published by Altman, et al, introduces a synthetic dataset designed to benchmark anti-money laundering methods. This dataset provides a valuable resource for testing and training detection algorithms against sophisticated laundering techniques.

Aligning with Regulatory Expectations

Regulatory bodies emphasize the importance of robust model validation practices. The Office of the Comptroller of the Currency (OCC) outlines in its supervisory guidance that validation should encompass:

• Evaluation of Conceptual Soundness: Assessing the quality of the model's design and construction.

• Ongoing Monitoring: Ensuring the model continues to perform as intended over time.

• Outcomes Analysis: Comparing model outputs to actual outcomes to verify accuracy.

Synthetic data plays a role in all three areas. It allows validators to probe the assumptions baked into match logic (conceptual soundness), test consistency over time (ongoing monitoring), and produce unambiguous results for metrics like balanced accuracy, precision, recall, and F-beta score (outcomes analysis).

Going Deeper: Measuring What Matters

One of the most overlooked aspects of sanctions screening validation is the quality of the performance metrics. Institutions often rely on pass/fail thresholds or anecdotal alert reviews. Synthetic data enables rigorous statistical evaluation, including:

• Balanced Accuracy: To ensure performance across both true positives and true negatives.

• False Match Rate (FMR): To quantify the likelihood of matching a non-listed entity.

• False Non-Match Rate (FNMR): To identify how often listed entities go undetected.

• Threshold Sensitivity Analysis: To evaluate the effect of scoring logic on match rates across multiple fuzziness configurations.

This type of structured, quantitative testing not only strengthens internal confidence in system performance but also prepares institutions to respond effectively to regulatory scrutiny.

Implementing Synthetic Data in Practice

To effectively integrate synthetic data into sanctions screening validation:

1. Collaborate with Experts: Engage data scientists, compliance analysts, and linguists to design realistic synthetic scenarios.

2. Leverage Specialized Platforms: Use systems like Yanez that support configurable synthetic data pipelines, jurisdiction-specific testing, and explainable benchmarking tools.

3. Document the Methodology: Ensure the synthetic data strategy is documented, repeatable, and aligned with internal model governance frameworks.

4. Test Across Use Cases: Include both high-risk (adversarial) and low-risk (tuning) scenarios to calibrate your system holistically—not just for precision, but for resilience.

   
   

How Yanez Enables Synthetic Data Validation

Yanez implements a dual-mode synthetic testing framework to address both tuning and risk detection use cases. Our platform generates synthetic traffic that mirrors real-world alert behavior for the purpose of reducing false positives, while also producing adversarial edge cases that challenge the boundaries of your system's match logic.

Our clients can configure parameters such as jurisdiction, name variation, transliteration fuzziness, and list volatility to create highly targeted validation datasets. Each scenario is fully labeled with known outcomes, enabling precise performance metrics and stress testing. The result is a validation environment that not only improves current configurations but anticipates failure modes regulators—and threat actors—care most about.

Conclusion

While historical alert history provides a foundational understanding of a sanctions screening system’s behavior, it is inherently limited to the past. It tells you where the system has worked—or failed—but not where it might fail in the future. Synthetic data fills that gap.

By combining adversarial testing, precision tuning, and aligned regulatory validation practices, financial institutions can move from reactive remediation to proactive resilience. Synthetic data doesn’t just make validation more robust. It makes it future-ready.