That is why the conversation around AML Resolution is starting to shift. The question is no longer simply whether AI can help with a task inside the workflow. It is whether institutions can improve how the whole case comes together: how evidence is gathered, how signals are connected, and how work moves across the systems they already rely on.
To explore that shift, we brought together Peter Tegelaar of SilkRiver and Debra Bonosconi, an independent advisor whose experience spans federal supervision, bank compliance leadership, consulting, payments, and fintech. Their discussion points to a more practical view of where AML Resolution is headed — and why the biggest opportunity may not be automating the easy cases, but handling the hard ones better.
One thing I’ve learned across all of those environments — as a regulator, inside banks, and later in fintech and advisory work — is that institutions usually are not judged by how fast they clear the easy alerts. They are judged by what happens when the case gets complicated. That is where the real exposure sits.
I think that’s exactly the right place to start. A lot of AI in compliance still gets framed around alert handling in general, as if the category is about speed on simple tasks. But AML Resolution rarely breaks on the obvious cases. It breaks when the case needs context from several systems, when the signals don’t line up neatly, and when someone has to build a narrative a reviewer can actually stand behind.
Right. And from a chief risk or compliance perspective, that distinction matters a lot. A simple false positive is workflow. A hard case is judgment. It may involve transaction activity, customer profile information, prior investigations, sanctions context, adverse media, maybe a complaint history, maybe a fraud signal somewhere else. The institution experiences that as one risk problem, even though the tooling often experiences it as four or five separate queues.
That is the gap. Most systems are built to process a workflow step. They are not built to assemble a full case. So the analyst becomes the integration layer. They pull the records, compare the histories, chase the context, and then write the narrative. When people talk about inefficiency in AML, they often talk as though the problem is the final decision. In a lot of hard cases, it isn’t. The problem is everything that comes before it.
I would agree with that. When I was a regulator, what mattered was not whether an institution had a slick tool demo. What mattered was whether the process held up in practice. Could the institution show what was reviewed, why a conclusion was reached, where escalation occurred, and how judgment was applied? And when I was inside institutions, the real pressure point was usually not the final call. It was getting to the point where someone could make that call responsibly.
Exactly. The bottleneck is case assembly. An experienced investigator or compliance officer can often make a sound judgment relatively quickly once the evidence is in front of them. What takes hours is finding the relevant evidence, reconciling what matters, spotting what changed, and drafting something coherent enough to defend.
And the reality is that those cases do not stay neatly inside AML. When I was in bank environments, and later in payments and fintech, you could see how often financial crime crossed functional lines. Something starts as suspicious activity, but then there is a KYC issue attached to it. Or it begins as a fraud concern and then raises AML questions. The org chart may separate those functions. The customer behavior does not.
That’s one of the most important points in this whole discussion. Financial crime doesn’t respect software boundaries. But most tools still do. Fraud sits in one module, AML in another, KYC somewhere else, sanctions somewhere else. So even when the institution has the data, the full picture is hard to see. The hard edge cases stay hard because the context is fragmented.
And that has practical consequences. At a bank, that fragmentation slows investigations and makes consistency harder. In fintech, especially in partner-bank or embedded-finance models, it creates a second problem: accountability becomes more complicated, but expectations do not get
simpler. Someone still has to explain the outcome to a sponsor bank, a regulator, an auditor, or a board committee.
That’s why I think the missing piece in AML Resolution is not another narrow point solution. It’s a layer that can gather the relevant inputs, organize them around the case, and move the work forward in a controlled way. Not to replace the human reviewer. To give that reviewer a complete starting point.
That distinction is important. I would be cautious about any framing that sounds like, “the goal is to remove the investigator.” In regulated environments, that is the wrong ambition. What institutions need is not less accountability. They need less manual assembly. They need less swivel-chair work, fewer disconnected handoffs, and more consistency in how the case comes together.
Yes. If a system can gather the transaction history, pull prior alerts, surface related counterparties, check external information, highlight the risk-relevant signals, and draft a structured narrative with traceability, then the human expert is still making the decision — but from a much stronger position. You’re not eliminating judgment. You’re letting judgment happen where it should.
And it should also be said that data access is part of the problem. Institutions are not irrational when they hesitate to move highly sensitive customer data into third-party environments. When I was operating inside compliance programs, that concern was always real. The compliance leader owns the consequences if something is mishandled. So if a solution depends on moving too much sensitive data around just to function, that creates another layer of risk.
That’s why architecture matters so much here. The hard cases are not hard because the industry lacks models. They are hard because the evidence lives in too many places, under too many controls, across too many workflows. Solving that requires a governed execution model: the ability to work across systems, stay within policy boundaries, preserve an audit trail, and route decisions to humans where needed.
When I was a regulator, a process earned credibility when it was disciplined and explainable. When I was in a bank, it earned credibility when it was consistent and could stand up under pressure. And in fintech, it has to do all of that while moving faster. That is why the hard cases matter so much. They are where all of those expectations collide.
And that is where the real differentiation should be. Anybody can show value on the obvious
cases. The real question for AML Resolution is: can you help the institution handle the cases that actually create risk, consume time, and expose the limits of the current operating model?
That is the right question. Because if you solve only the easy work, you may improve optics, but you have not really improved the control environment. If you solve the hard work better — with stronger evidence gathering, better cross-functional visibility, and clearer documentation — then you are doing something much more important. You are improving decision quality where it matters.
And that’s the shift. The future of AML Resolution is not about showing that AI can close more low-risk alerts. It is about building a better way to resolve the complex ones: the cases that demand context, synthesis, and control. That is where institutions need leverage most.
Closing perspective
The discussion around AI in financial crime often gets pulled toward the easiest measurable win: faster triage, higher throughput, more automation on straightforward cases. Those gains are real, but they are not where the category will be won.
AML Resolution becomes strategically important at the hard edge of the workflow — where the evidence is fragmented, the context crosses functions, the reviewer needs a defensible narrative, and the institution cannot afford to trade speed for control. That is where banks, fintechs, and other regulated businesses feel the real cost of disconnected systems and manual case assembly.
The institutions that move ahead here will not be the ones that merely automate the obvious. They will be the ones that solve the hard edge cases better: gathering the right evidence, connecting signals across the workflow, preserving human judgment, and producing decisions that are faster, more consistent, and more defensible.
That is the missing layer in AML Resolution. And increasingly, it is where the next real operating advantage will come from.
