theCUBE + NYSE Wired: The FinTech Exchange | Steven Hunter, 9fin

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Steven Hunter

CEO

9fin

play_circle_outline 9fin origin and name derived from 'leveraged finance' (LevFin)

play_circle_outline Global Cross-Asset Platform Modernizing Debt Markets with Real-Time News and Private Company Data

play_circle_outline AI-Powered Financial Extraction and Conversational Agents for Private Documents and Data Rooms

play_circle_outline Venture-backed 500-employee Loan Intelligence (founded 2016): Detect Distress, Find Refi Candidates, Win Deals, Draft Credit Memos — 150% YoY

play_circle_outline Data strategy: aggregating private/public data, client holdings, permissioned multi-tenant access

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Steven Hunter, 9fin

Steven Hunter

CEO 9fin

This episode examines modernization of credit markets through artificial intelligence-driven data platforms and agent workflows. Steven Hunter of 9fin appears with theCUBE hosts John Furrier of theCUBE and Dave Vellante of theCUBE to discuss 9fin's transition from automated financial data extraction to a unified platform for private and public credit data. Hunter outlines 9fin's AI-native approach, including real-time call transcription, natural language search, harmonized datasets and agent-driven workflows that streamline origination and credit analysis acr... Read more

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What is the origin and meaning of the company name "9fin"? add

How did you address the legacy-technology, security/AppSec and poor-data-quality problems you saw in finance—what strategy did you pursue to solve them, and what progress or momentum have you achieved? add

How is AI being integrated into your product/platform, and what does that integration look like in practice? add

Possible neutral questions the text is answering: - How does your product use natural language/AI to help customers interact with and analyze complex financial data? - How large is your company currently? Please share key facts and growth metrics (founding year, team size, funding, growth rate, and expansion). add

How does your platform aggregate, manage, and provide access to private-company debt/credit information, client holdings, and analytics? add

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Steven Hunter, 9fin

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(INTRO)

John Furrier

>> I'm John Furrier, host of theCUBE here in theCUBE's NYSE studio. Of course, we have our Palo Alto studio connecting Silicon Valley to Wall Street. This is our FinTech exchange here. We talk to the leaders who are out there making it happen because in the financial world, There's a lot of entrepreneurship, a lot of young companies, startups, and growth companies changing the game on how finance works. And when you add intelligence and technology to the game, all verticals are impacted. Finance is one of them. Our next guest here is entrepreneur Steven Hunter, CEO of 9fin. Great URL, only a few letters. Steven, great to have you on theCUBE. Thanks for coming on.

Steven Hunter

>> Thanks for having me on, John. Great to be here.

John Furrier

>> So, love the 9fin name. So explain the name 9fin. First of all, it's a short URL, so it's good. but nine means something. Fin is finance, we get that. What's 9fin?

Steven Hunter

>> Sure, so the company started based on my frustration in banking at JPMorgan, working in one of the debt teams. And whenever we were starting the company, we were trying to decide what we wanted to call it, and I used to buy websites. And so I had 9fin.com, and I thought this might be useful one day. And fin, obviously, like you said, short for finance, so when we were starting the company and coming up with the name, we had to back-solve for how we'd sort of explain the nine. And where we got to was that there are nine letters in the word leveraged, So Leveraged Finance, 9fin, and that's the name of the old team that I used to work in.

John Furrier

>> I mean, the debt market's huge, about $150 trillion. You're tackling that market. And it's one of those examples. I want you to explain the vision because it's a classic example of ripe for disruption. The infrastructure, the mechanisms, the software, the systems, they're either antiquated or outdated or brittle. We're seeing that, except for maybe the 1% of finance that has pretty solid IT. If you've got a $10 trillion balance sheet, $10 billion budget like J.P. Morgan, maybe you have some system. But still, legacy is everywhere.

Steven Hunter

>> It is.

John Furrier

>> What's the opportunity? Share the vision.

Steven Hunter

>> Absolutely. So when I went to work at J.P. Morgan, the LevFin team, and then when I worked in asset management afterwards, I felt like I'd been teleported back 30 or 40 years in terms of the technology that we were using. In my personal life, you had Facebook, you had consumer apps, you could call a cab on your phone with Uber and I would go in and to get the price of a loan, you'd have to open up Internet Explorer 6. You'd have to wait five minutes for this little Java applet to load to actually get the price. And a lot of the way that the market operated, because a lot of the companies are private, the data is very poor quality and the technology is very old school or almost non -existent. And so that was the original frustration was, why is it so difficult to do my job to get the data that I need?

John Furrier

>> the software legacy created the friction. They had to have the security, the AppSec review bar was high, people had to nail those, so as a result, you have these disparate systems. How did you attack this? What was the strategy, and give us an update on some of the momentum you've got now?

Steven Hunter

>> For sure.So we started in Europe, and the original product offering was getting financials out of private company documents. So when a bond gets issued, companies report, we'd go into their data rooms, we'd get the financial information, extract it using technology, and we used, back then, AI was machine learning and computer vision, Now it's LLMs, but that was the original product. And then over time, the company has developed to be more of a platform play. So you can get news flow, financial information across all the asset classes and globally as we now expand into the US and Latin America and Asia as well.

John Furrier

>> In today's market, AI has been the theme obviously. Where's intelligence being injected and what does that look like?

Steven Hunter

>> So I think there's a couple different areas where the new technology in terms of LLMs are disrupting our space. First, extracting information is easier than ever before. But also the big change for us is how people are interacting with data and with products. Using a chat interface, a natural language interface, to interrogate complex financial information. And how we've leveraged that is, for our customers, it's completely changing how they interact with our platform. So instead of looking up a database or trying to apply 15 filters, you can just say in real language, hey, show me refinancing candidates or companies that might be in distress. And within a couple seconds you get a really curated, well -defined list. So I think it's going to completely change how people originate business, and how they analyze companies in the data space.

John Furrier

>> And how big are you guys right now? Share some stats on, obviously been founded in 2016.

John Furrier

>> Yep.

John Furrier

>> It's 10 years ago, we're now in a whole world of high velocity. that data layer's kind of getting harmonized, you're starting to see graph databases come in, you're starting to see these corporate brains be built, on -premise data sets now getting ingested, unlocked. A year ago, it was like, oh, not a lot of adopters. So a lot's going on. Tell us what you guys are up to and the business organization.

Steven Hunter

>> Absolutely. So before COVID times, we were 10 people as a company total. And since then, it's kind of been a rocket ship. We found product market fit. We raised multiple rounds of venture. Most recently, we raised from Spark Capital, who backed Anthropic and Claude, and also from a host of other investors, including HarbourVest, CPP, big financial players. So that was earlier this year. and really that was to kind of accelerate and scale the company and the vision. So now we're about 500 people globally scaling in the U.S., growing 150 % year over year over here, and really doubling down on the engineering team that we're building. So we've had a roller coaster of a ride, but scaled very quickly across geographies and asset classes.

John Furrier

>> It's a nice alignment when you have investors that are in Anthropic, one of the big success stories. We think over $100 billion in valuation they're hitting. So it's nice to have that kind of pedigree, but how does that change what you guys do? Tell me about the product market fit. What specifically is clicking? What's the value proposition?

Steven Hunter

>> For sure. So for clients, it's helping them win business. So the pitch for us to Goldman Sachs or J.P. Morgan is use our platform, leverage the data and insights that we have and the tooling that we can give you, and do you think we can help you win one more deal in the next 10 years? Even one deal will justify the product many, many times over. So we leverage a lot.

John Furrier

>> Kind of an easy sell, actually.

Steven Hunter

>> That's kind of the pitch. We just have to prove it to them. And we've got a good track record of doing that. So it's about winning business for the banks. Can we help you spot a refinancing candidate? Can we help you spot the next data center that might need financing that you might want to go and support, and as a result, that generates business and deal fees. So it's a competitive advantage for people. And then for folks who are in the trenches doing the work, we can also help them be much more efficient. writing a credit memo at a fund, if you think about it, three years ago, when you think about engineers, they were all coding. Today, no one codes. Everyone just coordinates agents. And I think in credit markets and in the investment world, that's what we're going to try and enable as well.

John Furrier

>> Explain some of the before and after, old way, new way, scope, the size of the value. Old way, what was the process, how long did it take, how much grinding, what was the pain and suffering levels? And then the new way, natural language interface, connected databases, either on-prem Or maybe public. You got this new layer. Or what's the before and after picture look like?

Steven Hunter

>> So things that used to be very painful and take weeks, take a couple seconds. So whether that's finding...

Steven Hunter

>> Weeks to seconds.

Steven Hunter

>> Weeks to seconds is changes in unit of measurement. one of my old jobs was, go and find me all the small companies that have issued debt. We're looking for comparables. We want to go and pitch for business. You know, there is no EDGAR SEC filing system for private companies. Yeah. So the information's disparate. It's in data rooms. It's spread around.

John Furrier

>> It was a slogfest.

Steven Hunter

>> we try to...

John Furrier

>> Go gather the...

Steven Hunter

>> We try to aggregate it all. and that used to take me three, four days or a week at JPMorgan and now in 9fin you can click a button and you get all that information. Another good example is earnings season. you're maybe responsible for 40 to 50 investments when you're a credit investor and all the companies host their earnings calls at exactly the same time on the last possible day. And so you have your portfolio manager who'll come over and tap you on the shoulder and say, hey, can we buy 50 million more of this bond? What are earnings like? What did they say on the call? And I used to say, I can't be on 50 calls at once. I'm on one or two, I'll listen to the replay, I'll catch up later. Now with 9fin, we dial in all the calls, we transcribe them real time with AI, we give you insights so the PM can simultaneously see what's going on in all the calls without even needing the analyst to be dialed in.

John Furrier

>> Alright, explain the data strategy because you've got the ability to use AI to bring fragmented data documents together, aggregate all kinds of calls, get real time speed, insights, not high frequency trading, you get high frequency insights, love that. How much is public and private data? Are you all public data insights? Are your customers leveraging their own private data? Is that an integration layer? Explain that piece of it, help us understand.

Steven Hunter

>> Sure, so there's a couple different buckets. A lot of the companies who are issuing debt, they're private companies. So they're not listed like an equity would be. So whenever they put out their prospectus, it's distributed by email. And then when they report their results, it's in this kind of password protected data room or portal which makes it really difficult for people to monitor and access that information. So we automate and centralize all of that information, the whole way from new issuance through to earnings season, aggregate it into our platform. So that's private company information. And then we permission.

John Furrier

>> And they're a multi -tenant user of your system, basically.

Steven Hunter

>> Yes.They get full access to the data room. They get pretty full access to everything on our platform. And we can also permission it for you so that you can look at fully private deals that are in the private credit market or the loan market that only you can access. So it's kind of like the operating system for how people analyze credit in their day to day. And we use that plus we produce a lot of our own analytics. So we've got large teams of credit analysts, lawyers, journalists, and they're producing sophisticated market -moving analysis that people also consume through the platform. And then the final bucket is pulling together the client's own data. So what investments they're in, what their holdings are.

John Furrier

>> That's on their side.

Steven Hunter

>> That's on their side, so we will pull that into the platform too, and then you have an AI layer over the top of it all.

John Furrier

>> You're a marketplace of data, basically. That's it. You bring all parties together, the tech sorts out all the guardrails, people want a single tenancy feel. They get it, but it's truly multi -tenant scale.

Steven Hunter

>> One -stop shop, everything pulls into one place, makes your life easy. What we used to say to people is, what we were replacing is very fragmented, old -school systems and media companies. And we would say, wouldn't it be easier if you just had one login to get everything you needed? That was kind of the pitch.

John Furrier

>> What was the escape velocity moment and what was the key to success? Was it having enough private companies on board? Do they self serve? Do you guys get the data from them? What was the key thing?

Steven Hunter

>> So it takes a long time to build up the breadth of data that you need. So if you're providing data and intelligence and there's 15 bond deals launched on a Monday, you can't just cover one or two. You have to cover them all from a data perspective or from an analytics perspective. So building up that repository of data, that access to all the new deal information with our clients, that's what takes a lot of time.

John Furrier

>> Is there a fire hose of deal info? Is there one data source for that? Or do you have to go wrangle the data?

Steven Hunter

>> We have to wrangle it manually from a lot of the clients. We get it directly from some of the banks. But sadly, there's not one API you can plug in to display the new issue.

John Furrier

>> Well, you are.

Steven Hunter

>> We are.

John Furrier

>> You've done it.So you hit a critical point of momentum once you have that flow going. And then now people are opting in with private data because it's become a benefit to both sides. If I'm the private issuer, it's my incentive to join the network, is that right?

Steven Hunter

>> Yes, the way that people make credit investment decisions now is through looking at a company profile on 9fin. And the same way that in the 70s and 80s people would have plugged into Bloomberg to look up an equity, we're kind of the same but for credit.

John Furrier

>> You just locked down a really good position, and you got the self -service, you got the automation. What other tech you got going on? You mentioned agents earlier. Talk about how you guys are looking at that. obviously a perfect use case for agents for your customers?

Steven Hunter

>> Yes. So I think a large proportion of what's done in debt markets by either bankers or lawyers or analysts is very manual aggregation of data. And we can do that very quickly through technology today. But the really interesting bit is when you apply an intelligence layer on top. So we will pull all the data for a company and we'll say, there's this particular company that's got debt in Europe and US, how would you refinance it? And we actually have an agent now that can get pretty close to the answer that I would have given when I was a banker. Which is amazing.

John Furrier

>> One of the things I want to get your thoughts on is, I want to get to the private markets. we think we've been covering private markets in Silicon Valley for 17 years here now. There's a huge private liquidity market. That's happening, there's a lot of activity. But if you look at the AI infrastructure, all these neo -clouds, they're getting massive capex leverage on the real estate, so they're taking a lot of debt deals. So the banks are kind of like, okay, let's set up these new desks. I've heard many rumblings across most of the big banks that, set up an AI financing desk within debt. Is that kind of in your wheelhouse or will they be customers? Because they are going to probably put together instruments for massive billions of dollars of financing that might have other downstream changes. It's an asset class. Essentially, it's an asset deal, but it's financed in with the energy. NVIDIA's backstopping a lot of deals, that's well known, so you kind of have NVIDIA's like a bank. They're guaranteeing some of the debt. Yeah, they're guaranteeing some of the debt, so that changes some of the mechanics for the banks, because if I'm a bank, why wouldn't I jump on every deal that NVIDIA's backstopping, there's almost no risk.

Steven Hunter

>> Absolutely, I think we're at a really interesting point in terms of data center and power build out, and those kinds of companies, whether it's a CoreWeave who listed and raised debt at the same time, whether it's an Oracle, whether it's any of these other hyperscalers, the whole way down to the actual infrastructure projects themselves, which are issued in the high yield market or in the leveraged loan market, there's massive demand for capital. And people are ultimately going to fund through whatever capital mechanism they have. The cool thing for us is we don't just cover one particular silo of credit. We cover investment grade, we cover ABF, we cover CMBS, we cover high yield loans. So no matter where you finance yourself in terms of the AI CapEx build out, there's a pretty good chance that that's going to be involved and we're going to have some coverage of it.

John Furrier

>> And it's a lot of headroom for you guys too on the offering, you can just vertically integrate the new services, you must have a product roadmap out there. What's the hot next area you're going after that you see that you could share?

Steven Hunter

>> So I think the agents for making really high value decisions is probably the most exciting thing on the product roadmap. And we were saying beforehand, there's never been a more exciting time to build. You can build at crazy speed with amazing tools. So we launched a suite of AI features to basically try and help people find deal opportunities, win business, and automate ultimately 80 to 85 % of what people will do in their day to day. And then you have that key human element focused on judgment at the end, supervising an agent.So I think that's the most exciting thing that we -

John Furrier

>> What I love about your story is you're kind of a nice showpiece. You take cloud scale, native technology, you apply AI native to it, you have now a platform, and you're kind of resetting the private markets that traditionally is not a market because it's not a trading market. So there's a lot of data, but it's all over the place. And another opportunity to connect that together. So you have that whole private market dynamic. Talk about what's changed the most in the private markets, because we're seeing companies like Databricks, Series M financing, that looks like a mega IPO from a fundraising standpoint. I mean, their market cap's $188 billion. That's like 10 billion below IBM. That's the magnitude of these privates, and that's an extreme example, but seeing a lot more activity as people ramp up their escape velocity from zero start to valuation, what private stock is worth, so that's the risk management, then that's the financial transaction. Talk about that dynamic. I think this is an untold story.

Steven Hunter

>> So I think there's been so much capital that has flown into private markets. And whether that's on the later stage, venture capital, private equity side of the market, or whether it's on the credit side of markets. And one of the big changes that I saw is at the start of my career in the debt space, you had very clear silos. You had a boring bank loan product that was low leverage, low margin. You had an unsecured bond product. And then you had a syndicated loan product with covenants. And then you maybe had a small mezz team that would do the funky stuff. Now all those clear lines are completely disappearing. You have Apollo, can do anything. It can do a syndicated deal, it can do a private credit deal, and it can also do equity or pref. So you're seeing the kind of clear lines and silos between the different debt asset classes and between debt and equity itself, often blur.

John Furrier

>> Well Steven, congratulations on a great opportunity. I love what you did. You brought together the data, you got critical mass, that drops, the shoe drops, hit escape velocity. What are the goals for you now? What are you optimizing for? Obviously, as the flywheel goes, more opportunity for you guys. What's the vision and what's your next step? What's going on?

Steven Hunter

>> So we want to be the only platform that people need in credit. So no matter what asset class you service, no matter where you are in the world, we want to be able to give you the tools you need to do your job. Whether that's on the platform, whether that's API, MCP, providing you the data you want to plug into your internal systems. So being the kind of one-stop shop with kind of AI-native operating system for credit is the ambition. I think that's at least a billion dollar revenue business. We still have a lot of wood to chop to get there, but that's the size and scale of the opportunity. And we're running as hard and fast as we can to get there. And near term, priority is the US. Big market for us, scaling fast here. And then also massively investing in engineering and R&D, and that's why we raised capital early this year.

John Furrier

>> From building websites to changing the financial game, you're also a great case study in this entrepreneurship moment in finance. It's a really product-led, technology-driven market. Share your thoughts for folks watching that have domain expertise, whether they're young or old. People have these skills, they see their own company a year behind, It's like, hey, I could start a company. It's a lot easier. Share your perspective on this nuance of entrepreneurship in the financial stack.

Steven Hunter

>> It's never been a better time to start a business. I think I was probably a little bit unusual as a founder because I could code but also had some domain experience. but the kind of code that I would write is terrible. It's all since been erased from the platform and replaced with much better stuff.

John Furrier

>> If you had today's tools, what would you think?

Steven Hunter

>> If I had today's tools, Claude or ChatGPT or Codex, the things you can build are startling. And the speed at which you can build them is incredible. So the barrier to entry for building new tools and products and services has kind of never been lower. And that's a really exciting moment for people.

John Furrier

>> It's kind of like AWS when I started at SiliconANGLE 17 years ago. Put the credit card down, didn't have to get a data center or buy a server, wasn't sure it was going to work. You could test. So this is kind of the velocity gain and the risk of a startup is that, will the dog hunt? Will this work? And with that kind of speed, you can see it in action faster. That would have taken, what, a year, probably six months to a year, just to figure out prototype, product market fit now, you just come in.

Steven Hunter

>> Absolutely. It's startling, the speed at which you can ship and deploy new products and features. And I think a lot of entrepreneurs and companies are basically all in on AI in terms of the speed it can help them build new products, experiment, try things like you said would take six months or a year, now you can do it in six days and get it in market and see what people think.

John Furrier

>> Well you got a great set of investors, obviously Anthropic backers, the TAM is huge, checks all the boxes, and you got the AI intelligence in there. Looking forward to following up, congratulations.

Steven Hunter

>> Thank you so much.

John Furrier

>> All right, I'm John Furrier, host of theCUBE. This is the FinTech Exchange Series, looking at how AI and technology are changing finance, and the technology stack that's changing around it is a huge opportunity for entrepreneurs, existing businesses who want to be agile and make that transition to the AI era. We're doing our part here on theCUBE. Thanks for watching.