Frameworks are only as valuable as their execution.
In financial crime compliance, where scrutiny is high and risk tolerance is low, introducing Generative AI requires more than good intentions or vendor promises. It requires thorough structure. Control. Documentation. And above all, trust in the model and its makers.
That’s why we developed GRACE, a governance framework purpose-built to help compliance leaders deploy Generative AI responsibly and defensibly. GRACE was designed not as a set of theoretical principles, but as a practical, step-by-step approach to operationalizing AI in high-stakes environments.
If you are planning to integrate AI into your screening, triage, or investigative workflows, this is where you start.
Too many pilot programs begin with the technology. The smarter, and ultimately more successful, approach is to begin with the problem.
What specific decision-making process are you trying to improve? Who is the decision-maker? What inefficiencies are you trying to mitigate: manual alert triage, duplicative media reviews, inconsistent case narratives?
GRACE begins by framing AI not as a capability, but as a tool in service of a clearly defined outcome. Everything else, including model architecture, thresholds, and explainability, flows directly from that purpose.
This is not about deploying a chatbot. It’s about solving workflow bottlenecks with measurable gains in accuracy, efficiency, and risk control.
A common failure in early-stage AI deployments is the absence of clear, pre-determined thresholds tailored to specific business use cases. This leads to two unfavorable outcomes: the model either flags too much, wasting analyst time, or flags too little, exposing the institution to risk.
GRACE requires that risk appetite be defined up front:
These are policy decisions—not model tuning parameters—and they must be owned by your compliance leadership team. The model should conform to your standards and work for you, not the other way around.
No AI system should be deployed without proof of performance. That proof must be built on comparison.
GRACE mandates parallel testing against human analysts before a model goes live. This means running the AI in the background while your analysts continue with their normal reviews, then comparing results:
This is not just QA. It is how you build trust with your team, your auditors, and your regulators. And GRACE includes robust testing protocols to ensure you get meaningful insights, not just raw numbers.
The goal of AI in compliance is not to replace human oversight, but to enhance it. Every action the model takes must be explainable, reversible, and governed by humans-in-the-loop.
Under GRACE, your team must be able to answer:
If your administrator can’t explain the model’s logic, or if your QA team can’t trace how a decision was made, the model isn’t ready for deployment. Full oversight is a prerequisite, not a tradeoff, to model autonomy.
AI isn’t a one-time deployment. It is a system that must evolve rapidly alongside your policies, your people, and your risk exposure.
GRACE requires a structured feedback mechanism that includes:
Think of your model like a new team member. High-performing professionals still need coaching. Your AI is no different. The organizations that will succeed with GenAI are the ones that treat oversight not as a temporary phase, but as an ongoing discipline.
AI is not inherently trustworthy. But it can be governed in a way that earns trust internally and externally.
GRACE provides that structure. It enables compliance leaders to move forward without compromising control. It transforms risk-averse environments into innovation-ready ones. And it ensures that as your AI evolves, your standards never erode.
If your team is preparing to evaluate, test, or deploy Generative AI, the question is not “Can it help?” It’s “Are we ready to govern it?”
GRACE is how you make sure the answer is yes.