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Comment by zebomon | Hacker News Reader

This is very interesting. It calls to mind some advice I heard someone associated with YC (maybe PG?) offer a while ago: (approximately) the best ideas right now are the ones that will become more useful rather than less as AI gets better.

I say that because although it's been about ten years since I worked at a financial institute, this task seems like it must be still barely too complex for AI alone in its present state to match human performance but perhaps within reach if we can soon expect further slight advances in consistency and attention across longer context. Does your experience line up with those suppositions? Or with the high structure/formalization you've mentioned, do you see this as an alternative to all those KYB analysts today?

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Yes, I would definitely put Arva in that category.

For now, we limit the amount of decisioning that is made by an LLM and make as much of the business logic as we can concrete in code. It's mostly used to extract information from documents, crawl websites and identify specific fraud signals.

Very cool! Congratulations on the launch, and best of luck to you!

Thanks!

So what you're saying is that you could've done this startup a decade ago without LLMs using traditional NLP and ML techniques. Or even just with straight up procedural code, OCR and a rules engine. Especially since as you say everything you're dealing with is highly structured.

I work at a bank and everything you mentioned was solved many, many years ago. So the more interesting question then is why are fintechs still using manual techniques despite having the capability to automate it.

Not quite!

Fintechs often still have humans review docs, websites, perform web due diligence etc. Efficacy has vastly improved at these validation steps with the assistance of LLMs.

Interesting to hear that your previous bank has automated all of low/medium risk already, from what we have seen more traditional banks are far behind fintechs and are more risk averse in using new technologies. Nice to see that's not the case with all traditional banks.

> Efficacy has vastly improved at these validation steps with the assistance of LLMs.

Is that "efficacy" as the (customer-hostile) bank defines it, or is this more holistic interpretation that also factors in false-positives?

i.e. can you assert that things are better now for everyone, including the completely innocent people who often get caught-up in Kafkaesque KYC ("KKYC?") loops?

Yup exactly that! One of the benefits of what we're building is that fintechs/banks can now approve good customers quicker. So the innocent ones benefit greatly from Arva.

> can now approve good customers quicker

Well, that's half of it.

What about people who get disapproved because they were flagged by some automated screening? ...they end-up getting stuck in limbo because they were flagged, so they can't even (for example) close-out and withdraw any other accounts they have with the same institution - and they can't get any help because of the "we-can't-tell-you-how-to-evade-KYC" rules.

Stuff that happens all the time: https://www.nytimes.com/2023/04/08/your-money/bank-account-s...

Decision making is also more accurate, human analysts often deviate from procedure. Also why banks/fintechs often spend so much on QA teams just to observe how the human analysts have performed.

Transaction monitoring is different, that's post account opening.

When people are going through an onboarding flow it is their first account!

They do use automation, OP is just straight up clueless.

Depends on the fintech! 99% don't have 100% automation for all low/medium risk.

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