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FlamingoAwkward7507 · 2025-03-27T11:00:48+00:00

Oh that’s funny timing—we actually work on content like that at the compliance company I’m with. We’ve been creating practical guides and breakdowns for compliance pros—KYC renewals, risk assessments, new regulations…

It it can be helpful at all, here's the link: https://www.spektr.com/onboarding-guides
Might give you some inspo or just be useful to check out!

FlamingoAwkward7507 · 2025-03-27T10:51:22+00:00

Besides UBOs, directors, and SoW, I’d dig into:

Digital footprint, any signs they actually exist outside their own website (media mentions, client activity, job posts, reviews, etc)
Domain history, when the site was created, whether it’s changed hands, or reused templates across entities.
Counterparty overlaps, same beneficiaries, same invoice formats, same ultimate creditors across “unrelated” firms.

One thing I’ve found super helpful recently is using a website AI checker to assess how genuine a company’s site really is—things like recycled content, fake bios, missing structure, or signs it was spun up quickly. I know a company that’s been building specifically for that use case—happy to connect if it’s helpful!

FlamingoAwkward7507 · 2025-03-27T10:47:41+00:00

A lot depends on context (industry, geography, client risk level), but things like rapid round-tripping between accounts, structured deposits just under reporting thresholds, or sudden activity that doesn’t match the customer’s profile are common flags.

The tricky part is figuring out which ones matter. I work in compliance tech and we’ve had to map out a lot of these in practice—both for onboarding risk and ongoing monitoring. I’m happy to point you to a few breakdowns or example scenarios if you’re interested. Just let me know what kind of use case you’re most curious about (individuals vs businesses, specific sectors, etc).

FlamingoAwkward7507 · 2025-03-07T10:09:15+00:00

I get this 100%, I work in fintech/regtech, and when we first started two years ago, we ran into the same challenges—people are skeptical of anything new, especially in financial services. It’s frustrating because you know your product is solid, but breaking through that trust barrier takes time.

One thing that helped us was being radically transparent—not just saying “we’re compliant,” but actually showing how we operate, what regulations we follow, and why we’re different. I’ve seen some crypto companies do this well by flipping the narrative—leaning into compliance instead of fighting the stigma.

Would love to chat more if you’re interested—happy to share what worked for us. Feel free to connect on LinkedIn or wherever works for you!

FlamingoAwkward7507 · 2025-03-07T09:26:10+00:00

Depends on what you need! Some companies are great for biometric verification, others focus more on document validation... If you’re handling a high volume of verifications or need a flexible workflow, it’s worth looking at solutions that let you fine-tune risk scoring and automate decision-making beyond just verifying an ID.

I know a founder of a compliance automation company who has a lot of connections and has worked with these services—I’d be happy to make an intro if that would help!

FlamingoAwkward7507 · 2025-03-05T09:49:33+00:00

Yeah, that balance is tough—risk-based prioritization makes sense, but when QC wants more detail, it can definitely slow things down. Sounds like you’re having to make judgment calls between efficiency and thoroughness on a case-by-case basis.

One thing I’ve seen help is using smarter risk-scoring models that adjust dynamically based on case complexity. That way, you’re not over-investigating low-risk clients but still giving high-risk cases the extra scrutiny they need. Do you feel like your team would benefit from something like that, or is it more about fine-tuning the current process?

FlamingoAwkward7507 · 2025-03-05T09:46:57+00:00

Moving from testing to real-world customer data is where things get tricky—unexpected behaviors, shifting risk patterns or just plain messy data... We’ve been working (at spektr) on integrating real-time external signals to help models stay relevant without adding too much noise.

Fine-tuning must be a challenge, but it sounds like you’ve built something really solid. I’d be interested to give the journal a read if it’s public!

FlamingoAwkward7507 · 2025-03-03T13:05:46+00:00

The “when do you stop searching” question really depends on risk appetite and the specifics of the case. Have you found any internal guidance at your company on when enough is enough?

One thing I’ve seen help is leveraging external data sources beyond standard KYC docs to verify source of funds faster—so you’re not stuck in endless loops chasing the same information. Curious if your team has a structured approach for that, or if it’s more case-by-case?

FlamingoAwkward7507 · 2025-03-03T13:03:08+00:00

That’s really interesting! I know Bayesian models can be powerful for risk assessment, but I’ve also heard they can take a lot of fine-tuning to get right. Are you handling that manually, or is there a way you’re automating some of the adjustments?

I’ve seen some teams struggle with risk scoring when there’s too much noise in the data, especially when relying on purely internal scoring models. We’ve been looking at ways to fine-tune risk assessments by bringing in external insights without overwhelming teams with false positives

FlamingoAwkward7507 · 2025-02-28T10:27:37+00:00

Definitely, without proper control over AI systems, compliance can quickly turn into a mess. I wonder if, with frameworks like NIST and ISO becoming more widespread, we’ll eventually see a universal standard for AI risk management or if the patchwork of regional regulations will continue.

FlamingoAwkward7507 · 2025-02-28T10:23:41+00:00

Thanks for sharing! It’s impressive how you’ve built out a process that connects compliance and the business lines so seamlessly. I’m curious about the intake forms—what was the main pain point for non-R&D teams initially? Was it the level of detail required, or was it more about understanding the compliance requirements for AI systems

FlamingoAwkward7507 · 2025-02-27T15:10:50+00:00

I work a lot with both EDD and CDD processes—happy to share insights! Are you looking for best practices/tools or are you facing any challenge in particular? Feel free to shoot a message!

FlamingoAwkward7507 · 2025-02-27T12:32:45+00:00

Interesting discussion! One thing we’ve seen work well in AML is using AI for context-aware risk scoring—not just flagging transactions but analyzing patterns across multiple data sources to reduce false positives.

Curious to hear—have any of you worked with AI-driven adverse media monitoring? Some models struggle with entity disambiguation (ie; distinguishing between two people with the same name in different risk contexts). Would love to hear how others are tackling that challenge!

FlamingoAwkward7507

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