Model Validation Must Evolve Alongside the Models It Tests

In the last decade, financial institutions have raced to modernize their sanctions screening programs to keep the rapid pace of evolving sanctions. With the rise of artificial intelligence and machine learning, screening systems are becoming more precise, more efficient, and in some cases, even adaptive to changing risk landscapes. False positives are down. Match accuracy is up.

But as screening models evolve, a critical part of the equation remains overlooked:

Model validation

Validation—the process of proving that a screening engine works as intended—is still, in many institutions, a static and manual function. Despite widespread regulatory emphasis on model risk management (MRM), validation practices lag behind the innovations they are meant to evaluate.

This gap is more than a technical oversight. It represents a fundamental risk. As noted in SR 11-7, the Federal Reserve's seminal guidance on MRM, model validation must be "independent, rigorous, and ongoing." The OCC (Office of the Comptrollers of the Currency) reinforces this through its own model risk management guidelines, which define a three-pillar framework for validation: conceptual soundness, ongoing monitoring, and outcomes analysis. This is the same framework implemented by Yanez to ensure end-to-end oversight.

And, yet too often, validation is treated as an afterthought or a periodic compliance task.

At Yanez Compliance, we believe validation must be as modern as the models it tests.

Why Traditional Validation Falls Short

Many current validation practices were built for rules-based systems. Test datasets are hand-curated. Outcomes analysis is spreadsheet-driven. Documentation is often a slow, manual process that struggles to keep pace with model updates and data drift.

This approach introduces several blind spots:

• Limited scope of test cases that fail to reflect the diversity of real-world name variants and typologies.

• Infrequent testing cycles that allow performance degradation to go undetected.

• Inadequate explainability, especially in ML-based systems where outputs are not easily traced to explicit logic.

In short, static validation methods are mismatched to the speed, complexity, and opacity of modern screening models.

A New Paradigm: Intelligent, Adaptive Validation

Emerging academic and regulatory literature reinforces the need for change. In recent years, scholars have explored how AI can support model governance, from stress testing to explainability (see Rudin, 2019; Doshi-Velez & Kim, 2017). Meanwhile, the BIS and FATF have emphasized the importance of algorithmic transparency in financial crime controls.

Yanez Compliance brings this thinking into practice.

Our approach to model validation is built around three pillars:

1. AI-Augmented Test Data: We generate synthetic screening scenarios that reflect global diversity, fuzzy matches, and edge-case risks. This goes beyond traditional regression testing—it actively probes model weaknesses.

2. Continuous Oversight: Our validation framework is automated to the extent possible. We detect drift, benchmark performance, and surface anomalies by entity type, jurisdiction, or list source.

3. Explainable Outcomes: Yanez uses fine-tuned large language models (LLMs) to analyze and summarize documentation that supports the design and implementation of the screening model. This helps ensure alignment with regulatory expectations, internal governance, and model design principles. Additionally, we apply the OCC's outcomes analysis framework to quantify model performance by comparing production and testing environments, surfacing where real-world behavior diverges from expectations. This approach enables a more rigorous and structured performance narrative—one that stands up to scrutiny.

Together, these pillars enable validation that is rigorous, repeatable, and responsive to change.

Compliance and Competitive Advantage

Modern validation is not just about satisfying regulators. It is a competitive advantage.

Institutions that invest in intelligent validation are better positioned to:

• Detect vulnerabilities before they become compliance failures

• Reduce unnecessary alert volume without compromising coverage

• Demonstrate effectiveness and fairness to regulators and stakeholders

With regulatory frameworks becoming more detailed and data-driven, model validation becomes a signal of institutional maturity. It separates those who react to risk from those who manage it proactively.

The Yanez Difference

In studying the broader landscape of compliance operations, we identified a structural gap—model validation. We saw an opportunity to bring intelligence, scalability, and rigor to an important function and, in doing so, help elevate the entire compliance ecosystem. We saw an opportunity to elevate how institutions validate, govern, and ultimately trust these systems. The importance of model validation extends across the entire compliance operation—from sanctions screening to transaction monitoring to KYC. As financial crime controls increasingly rely on complex algorithms and data-driven systems, robust validation becomes foundational, not optional.

Yanez is building toward a unified validation framework that supports multiple compliance functions, ensuring every model used in decision-making is rigorously and continuously evaluated.

We don’t just test models. We interrogate them.

We don’t just measure performance. We explain it.

We don’t wait for failures. We look ahead.

As AI becomes more embedded in compliance workflows, validation must evolve alongside it. At Yanez, we’re building the future of oversight: scalable, transparent, and intelligent by design.

Because in modern compliance, it’s not enough for a model to work.

You have to prove it.