Benefits of AI in Preventing Financial Crime: Model Validation with GenAI

Yet in many organizations, FCP models still follow the same rigid model validation (MV) process used for credit risk models, a process designed for stability, not agility.

While this ensures consistency, it leaves little room for the frequent updates these models desperately need. With regulators pushing for both effectiveness and compliance, and criminals moving faster than ever, the traditional approach is under pressure. Could generative AI, one of today’s most disruptive technologies, be the key to bridging the gap between strict validation requirements and the need for agility? Could it help us strike a better balance between speed, control, and cost?

The Current State of Model Validation in FCP

Currently, FCP model validation largely follows the framework used for credit risk models, but it faces several critical limitations:

• Manual, time-consuming and resource-intensive: Highly skilled validators must carefully review documentation, code, data pipelines, and monitoring outputs. Currently, it can take two people 6-8 weeks to complete a single validation, leaving them with little capacity to handle additional validations in parallel. In the time it takes to complete a validation, new criminal patterns may already be emerging which require updates or the development of a new model. In addition, delays in feedback between the validation team and developers could further extend the overall timeline.
• Costly: Compliance already consume significant budgets, and under cost-cutting pressures, it becomes increasingly difficult to justify allocating large teams to a single validation. 
• Inflexible: Current validation approaches, built around credit risk frameworks, are primarily designed to safeguard capital adequacy. While effective for that purpose, they are not structured to keep up with the fast-moving and evolving patterns of financial crime.
• Capacity constraints: Given the size of most financial crime model portfolios and the frequency of validations required by internal policies, current resources are unlikely to meet long-term expectations. With model inventories expected to grow, this gap will only widen unless capacity is increased.

These challenges highlight a pressing reality: the current validation approach is no longer sustainable. Without significant improvements, organizations risk falling behind with both regulatory expectations and the fast-moving tactics of financial criminals. 

Where Can GenAI Make the Biggest Impact in Validation?

In most organizations, models are assessed based on risk, complexity, and probability of failure. This tiered approach helps determine where automation, such as GenAI, can be applied most effectively.:

• High-risk, complex models – Models that are mathematically sophisticated, critical to operations or compliance, and have a higher probability of failure require thorough human validation and expert judgment due to their complexity and potential impact. Therefore, they are not well-suited for GenAI-based validation.
• Low-risk, simpler models – Models that are less mathematically complex, more standardized, and have a lower probability of failure. These are ideal candidates for GenAI, which can handle repetitive validation tasks, review documentation, and generate draft reports.

With GenAI taking care of routine checks on simpler models, human experts can dedicate their time and judgment to the models that truly require deep expertise.

The Role of AI in Financial Crime Model Validation

In practice, how can AI be applied to support model validation? At its core, AI is best suited to take on the repetitive and manual aspects of the process, the tasks that consume valuable time but add little judgment-based value. Instead of replacing experts, AI acts as an efficiency enabler – a “second pair of eyes” that enhances consistency, speeds up routine checks, and leaves human validators free to focus on areas where their expertise is irreplaceable. 

Generative AI opens new possibilities for how validation might be approached, with AI driving the validation steps. Instead of starting from scratch, it could make it possible to ingest large volumes of model documentation and generate draft answers to validation questions which are informed by guidance documents, policies, and historical validation reports. It may also be possible to highlight areas that need further clarification, suggesting relevant follow-up questions for discussions between validators and model developers. Where responses are already sufficient, GenAI could enable the automatic closure of open points, keeping the process moving smoothly. Beyond Q&A, it creates the possibility of drafting validation findings based on prior patterns and even producing structured, section-by-section draft validation reports, giving validators a strong foundation to build on, rather than a blank page to start with. Final review and submission are always completed by the validator. 

This shift highlights a clear evolution in validation practices. Currently, validation is often characterized by long checklists, manual document reviews, and labor-intensive report writing. With AI support, validation can become faster, more consistent, and highly scalable, allowing humans to focus on important aspects such as judgment, oversight, and final decision-making. With routine and repetitive tasks automated and accelerated through AI, and waiting times between interactions significantly reduced, validators could manage multiple validations in parallel. For model developers, this also means less time spent waiting on feedback, and therefore model developers can drive the speed of validation by submitting evidence faster. In short, AI doesn’t diminish the role of the validator – it elevates it, ensuring their expertise is applied where it delivers the most value. AI will transition the validator from an executor to a supervising role. 

How would this Technically Work?

The goal is to create a simple, working version of the idea that shows how GenAI can support validators by automating repetitive steps while keeping humans in full control. It’s not about replacing expertise but about giving validators a smart assistant that can read complex documentation, provide the right information, and draft initial outputs they can refine.

At the center of this setup is an agentic framework built around two main parts: a Retrieval-Augmented Generation (RAG) system and a prompt creation engine. The RAG system helps the AI pull the most relevant content from internal guidance, policies, and historical validation reports. The prompt engine then turns that information into focused, context-aware prompts, so the AI can generate accurate, useful drafts. Everything runs securely in the organization’s existing cloud environment (for example, in Vertex AI on GCP) to make sure data stays protected and traceable.

The process could look like this:

• The model developer submits documentation, and the AI reviews it to identify relevant guidance and validation standards.
• It drafts initial responses to validation questions, giving the validator something concrete to start from instead of a blank page.
• If information is missing or unclear, the AI compiles structured follow-up questions that the validator can check and send back to the developer.
• When new evidence comes in, the AI reviews it, links it to the open items, and flags what can be closed or what still needs attention.
• Finally, it pulls everything together into a draft validation report with structured sections and proposed findings, ready for the validator to review and finalize.

In this target setup, the AI tool sits between the model developer and the validator. It manages the flow of documents and questions, keeps track of progress, and helps draft findings and reports. Validators remain in charge of every decision but can move through the process much faster and with more consistency. Developers, in turn, get clearer feedback and shorter waiting times.

The outcome is a smoother, more efficient collaboration where AI takes care of the manual groundwork, and humans focus on judgment and oversight, the parts that really matter.

Balancing Benefits and Risks

The potential of AI in model validation is not just theoretical; it comes with tangible benefits. First and foremost is efficiency: automation can significantly reduce the time spent on repetitive validation tasks, freeing experts to focus on higher-value activities. With a basic introduction of AI into the validation process, teams can achieve time savings of around 30%. When introducing a more advanced option, we believe an estimated time saving of up to 80% can be achieved. This naturally translates into cost savings, as the overall validation burden is lowered without compromising quality or increasing headcount. AI also promotes consistency and transparency, applying the same standards uniformly across models. Finally, it offers scalability as organizations can handle a larger portfolio of models without needing to increase headcount, a crucial advantage given current cost-cutting pressure.

As with any innovation, AI in model validation comes with significant risks that must be managed. Generative AI itself carries model risk, including bias, opacity, or “black box” behavior, which could undermine confidence if not carefully controlled. Additional concerns include autonomy risk, where AI might generate outputs without sufficient human guidance, leading to decisions that may be inappropriate or misaligned with validation standards; hallucination risk, where it produces information that seems plausible but is factually incorrect, which could mislead validators if not carefully checked; and incompleteness risk, where AI may overlook parts of a model or validation requirement, resulting in partial or insufficient coverage.

These risks can be managed by humans actively supervising AI and regularly reviewing its outputs. Mistakes are far less likely when experts double-check results, make sure nothing is missing, ensure all information provided is accurate, and stay in control of key decisions. Regulatory acceptance is also a consideration, as supervisors are likely to scrutinize the role of AI and require organizations to explain and justify its use. Finally, careful implementation prevents over-reliance on automation, ensuring human validators remain central to decisions where judgment is essential.

When implemented with care and proper oversight, AI can bring significant benefits to model validation. By combining AI’s capabilities with human judgment, organizations can work more efficiently, handle greater scale, and reduce costs, all while maintaining the trust and rigor that model validation requires.

Our FCP expertise:

Zanders brings a unique combination of expertise in both traditional and AI-driven model validation, helping to navigate the evolving landscape of financial crime model oversight. As a trusted advisor in risk, treasury and finance, Zanders combines deep regulatory knowledge with practical experience, ensuring that solutions are not only innovative but also fully compliant. More importantly, Zanders focuses on pragmatic, regulator-ready designs that bridge cutting-edge technology with compliance requirements. Zanders helps organizations work more efficiently while still meeting the high standards of rigor and trust that regulators expect.

Conclusion

In this FCP series, we have explored bias and fairness, explainability, and model and data drift. Each represents a vital aspect of building models that are not only powerful, but also responsible. Together, they remind us that the real challenge is not just creating models that work, but creating models that we can trust, understand, and sustain over time.

This is what makes AI-enabled model validation a natural next step. As models become more complex, risks evolve faster, and regulatory expectations increase. Organizations need human experts to focus on the areas where their judgment and oversight have the greatest impact, while AI handles the routine tasks.

As we conclude this series, the message is clear:

Organizations that embrace GenAI in their validation processes are not just improving efficiency, but they are shaping the future of model risk management.