u/ExpressIce8477

This week, a new vendor case study claiming 90%+ false positive reduction across transaction and customer screening.

I've seen 3 of these this month alone. meanwhile my team is closing 500 alerts a day with a 94% false positive rate. same number as before we bought the tool that was supposed to fix it.

The vendors aren't lying exactly. the gap between a controlled proof-of-concept and plugging something into a real TM system with 7 years of badly tuned legacy rules is just wider than what makes it into the press release.

The half-year number is never the one they publish.

If you're trying to figure out what AI actually does to alert volumes in production rather than in a demo, there's more honest conversation happening in ComplianceOps than in any vendor case study i've read.

FinCEN dropped a proposed rule on April 7 that basically says examiners will stop asking "do you have a policy" and start asking "does your program produce results." false positive rates, SAR quality, investigation timelines, whether your risk assessment actually drives decisions or just lives in a PDF nobody opens.

On paper i love this but in practice I'm skeptical.

I run compliance at a crypto exchange where half my risk assessment is built around wallet screening heuristics that change every time a new mixer protocol launches. producing stable documented outcome metrics for a program operating in an environment that shifts quarterly feels like it was written by someone regulating banks, not VASPs.

The other thing nobody's talking about: "significant or systemic" failures as the new enforcement threshold sounds like a win until you realize the teams most likely to have systemic gaps are the ones running 3-person BSA programs with 94% false positive rates who literally cannot afford to build a metrics layer on top of their existing alert queue.

The rule rewards teams that already had resources to measure outcomes and exposes everyone else.

Comment period closes June 9. still deciding what to say in mine tbh.

The proposed rule drops uniform monitoring requirements across the board. sounds like a win until you read what replaces them.

Now you need a documented risk tiering framework that's defensible under exam, connects directly to how you allocate analysts and TM resources, and stays current as a living artifact. examiners aren't checking whether your policies exist anymore. they're pulling outcome metrics like unreviewed alert rates and unfiled SAR counts.

The 400 low-risk alerts i was clearing every week? sure, maybe those go away. but somebody at my shop has to build the tiering methodology that justifies why those customers are low-risk in the first place, map it to our TM rule thresholds, and keep updating it every time our product mix or customer base shifts.

We don't have a risk modeling team. that somebody is me and my manager with an Excel file.

Comment period closes June 9 and I don't think most regional banks have even started thinking about what this actually requires on the ground.

I applied for a seat at a KPMG-hosted event billed as cutting edge AI for financial crime.

The description mentioned explainability, real-time KYC, agentic compliance workflows. exactly the stuff I've been trying to figure out for the last year.

The talks were mostly about regulation, not even how to implement, just what it says, content that I could've read on FinCEN's website on a Tuesday afternoon.

There was one vendor I paid close attention to because they're supposedly a notable player in explainable AI for AML in the DACH region. the CEO couldn't answer a straightforward question from the audience about how their model actually produces explainable outputs. His response was vague enough that anyone with basic ML knowledge would've clocked it immediately.

The networking part was better. rooftop, good food, decent conversations. but even there, every panel speaker I talked to could identify weaknesses in everyone else's presentation while being completely blind to the same gaps in their own. nobody could meaningfully distinguish between a strong and weak AI approach, they were just selling.

I tried to bring up knowledge graphs, deontic logic, real-time data integration. one KPMG rep advised me not to apply for a job there, which was at least honest.

The gap between what vendors are pitching in compliance AI and what practitioners actually need to know is wider than expected. and the conferences designed to close that gap aren't helping.

there's more honest conversations happening in r/ComplianceOps than I heard in that entire room.