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Launch HN: Bedrock AI (YC S21) – Using ML to identify red flags in SEC filings

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Re: Launch HN: Bedrock AI (YC S21) – Using ML to identify red flags in SEC filings

#11

Post by kbennatti »

fritzi wrote: post #9

There have been several popular books about famous fraudsters. I suspect that you come across some facts that would be interesting (if not profitable) not just to institutional investors but to the average nerd, or maybe someone looking for an idea for the next bestseller.

Like this? This Oxford City Football team, a boiler room operation, duped people by saying that they were bound by a voice recognition and proved it by using the phone to make some beeping sounds. https://www.sec.gov/litigation/litreleases/2017/lr23869.htm

Re: Launch HN: Bedrock AI (YC S21) – Using ML to identify red flags in SEC filings

#12

Post by scottydelta »

kbennatti wrote: post #4
scottydelta wrote: post #2

Hey guys, congrats on the Launch. Is there any API to get the metrics you guys calculate on SEC Filings? I recently launched https://quantale.io which is a web-based Bloomberg Terminal alternative and we monitor SEC filings in real-time to show them to users[1]. It would be great to show additional data related to the SEC filings if there was an API. [1] https://quantale.io/dashboard/sec-filings

Sweet! The world needs a Bloomberg alternative so props. We don't have an API atm. We're focused on supporting human analysts. The meat of the product is the red flags which are textual and not quantitative.

If you think you could use more data such as news headlines, reddit and twitter chatter, and forum discussions about the companies from the filings then let me know, Quantale can provide that. The additional data could augment the current fraud detection.

Re: Launch HN: Bedrock AI (YC S21) – Using ML to identify red flags in SEC filings

#13

Post by tomrod »

Very intriguing.

Can you give a sense of your model(s) metrics? Sensitivity and specificity in validation/test? If you're open to it, explainability assessments?

What is your market size? Can your models transfer to other compliance spaces?

Re: Launch HN: Bedrock AI (YC S21) – Using ML to identify red flags in SEC filings

#14

Post by kbennatti »

fritzi wrote: post #9

There have been several popular books about famous fraudsters. I suspect that you come across some facts that would be interesting (if not profitable) not just to institutional investors but to the average nerd, or maybe someone looking for an idea for the next bestseller.

Or this? John Rohner, claimed that he'd developed tested and patented a "plasma engine" fueled by inexpensive and abundant noble gasses. He also claiming that he graduated from Harvard at 14 and had 3 Phds from M.I.T. Somehow he managed to dupe 98+ investors

Re: Launch HN: Bedrock AI (YC S21) – Using ML to identify red flags in SEC filings

#17

Post by nodesocket »

scottydelta wrote: post #2

Hey guys, congrats on the Launch. Is there any API to get the metrics you guys calculate on SEC Filings? I recently launched https://quantale.io which is a web-based Bloomberg Terminal alternative and we monitor SEC filings in real-time to show them to users[1]. It would be great to show additional data related to the SEC filings if there was an API. [1] https://quantale.io/dashboard/sec-filings

How are you different than the native macOS WeBull application? WeBull comes the closest I have seen to a Bloomberg terminal.

Re: Launch HN: Bedrock AI (YC S21) – Using ML to identify red flags in SEC filings

#18

Post by kbennatti »

mynegation wrote: post #15

How would you address would-be filers from using your own product from iterating on their wording until the red flags are removed?

We don't sell to filers. Ever. Got to protect against conflicts of interest. That's one of the reasons we don't have a true free version of the product.

That said, we're using language models so replacing words with synonyms won't evade the model as long as its expressing the same thing.

Re: Launch HN: Bedrock AI (YC S21) – Using ML to identify red flags in SEC filings

#19

Post by paisible »

Congrats on the launch guys! Fellow Canadian from Montreal here :) I'm curious, what tools / methodology did you follow to generate the high-quality labels, and how many different labels did you end generating? I'm also very curious whether you view the discovery and generation of new labels (and accompanying high-quality training datasets) as a continuing and core part of your development going forward?
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