Launch HN: Arva AI (YC S24) – AI agents for instant global KYB onboarding

Hi HN! We’re Rhim and Oli, and we’re building Arva AI (https://arva-ai.com). Arva uses an AI agent to automate the manual verification that every banking/fintech does when they get a new business customer. This is called “Know Your Business” compliance (KYB for short) and it’s a royal pain.

There’s a demo video here: https://www.arva-ai.com/loom , and if you’re curious to try it out for yourself, you can onboard onto ‘Arva Bank’ and see what happens when you upload some fake documents: https://platform.arva-ai.com/demo (but please don’t upload any sensitive documents!)

What’s this about? When a business onboards onto a bank or fintech (i.e. applies to be a customer), a human analyst has to conduct extensive manual verification a lot of the time. This causes long onboarding times, and there are huge costs in maintaining compliance teams and in fixing mistakes—because human analysts often deviate from procedure.

I (Rhim) formerly led the FinCrime product team at Revolut Business. There were hundreds (!) of KYB analysts conducting tens of thousands of manual reviews per day! Turns out this is the same across pretty much all fintechs in all jurisdictions around the world, and it’s a problem that can now largely be solved by a combination of AI, data and business logic.

Most of the manual verification work falls into three categories: document verification (e.g. articles of incorporation); figuring out the business activities (e.g. does the business do things that are allowed); and communication with the customer (to make sure they provide the correct pieces of information).

Handling all of this for a customer is time consuming. It leads to huge compliance overheads from having a manual team, which can also often make scaling very difficult. Days of back-and-forth over email with customers creates a terrible experience, leading to drop off and hence revenue loss. In fact, in the US, slow customer onboarding costs fintechs $10B annually in lost revenue (https://resources.fenergo.com/newsroom/poor-customer-experie...)!

The interesting thing is that this domain is so highly structured and formalized that it turns out to be a good match for the current generation of AI tools.

Enter Arva AI. We've built a highly compliant AI agent that instantly handles all low/medium risk manual KYB work in real-time as a customer is onboarding, cutting reviews to seconds and ops spend by 80%. Arva AI’s agent can operate globally and can either be used as standalone KYB agent, or layered into already implemented compliance stacks. We focus on reviewing documentation, websites and social profiles in realtime and then communicating with the customer so that they can rectify bad information whilst they’re in the onboarding flow.

Check out (https://www.arva-ai.com/loom) for an overview and try it out yourself if you want, at https://platform.arva-ai.com/demo.

We’d love your feedback, especially if you’re connected to the fintech space, and we look forward to everyone’s comments!

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?

Our agent implements every step of the procedure that a human compliance analyst would follow, step by step. As a result, every individual check that makes up the overall decision is documented with the reasoning and evidence the agent used.

You can also choose to bucket your customers by risk level and require manual review for higher risk customers (e.g. certain business activities like crypto or gambling, complex ownership structures or foreign UBOs). Ultimately the level of automation vs review is up to the risk appetite of the customer, as they are the one that has to answer to the regulator.

We use Gemini as they offer stronger guarantees around not training on your data ve e.g. OpenAI. We are also looking at self-hosting open source models in the future.

Looks awesome. Compliance in itself is a huge space that can benefit from a lot of automation. And the pain point of avoiding back-and-forth and giving quick feedback is real, because people usually don't read instructions, but rely on "feedback" to instruct them. All the best!