[
  {
    "startMs": 12600,
    "text": "Um, thanks to everyone for being here. I'm so excited to be doing this talk. So, um, kinda quick intro on me. So my name is Harneet. Um, I'm the founder and CEO of Axim Intelligence, and at Axim, we're building the intelligence layer for distressed credit investing. So also what we're gonna be talking about today, um, and the title of this talk is Can AI Understand a Distressed Credit?"
  },
  {
    "startMs": 42580,
    "text": "So I think the first question, um, on everyone's minds, if you're not, you know, in the world of credit, is what does distressed credit really mean? Um, credit is pretty simple. So credit is just a bond or a loan, um, you know, of a, of a, of a corporation. The bond of Coca-Cola is a credit. Um, now what does it mean to be stressed or distressed? Right? So"
  },
  {
    "startMs": 72660,
    "text": "stress really happens if there is any sort of underlying risk in the company that would lead them to either, one, run out of money, two, not be able to pay back their debt. So every portion, uh, every year, some portion of the credit market faces some level of dis- uh, stress or distress and caused by cyclical issues. So let's say construction, you know, the construction market is down, housing market is"
  },
  {
    "startMs": 102460,
    "text": "down. Um, it can be caused by operational issues. If you're a restaurant business, there's a big food recall. Um, it can also be caused by, um, secular disruption. So if you're a software business today and  maybe seeing some AI disruption, right? Um, some businesses face challenges and then come right back up. Some businesses face challenges and then get to a place of distress. What does distress mean? It's actually not straightforward. Bankruptcy is one"
  },
  {
    "startMs": 132060,
    "text": "outcome of distress. You can also have a liability management exercise, and these are terms I can get into them, but just know that's another, uh, thing that could happen and then... or you can have out-of-court restructuring, negotiated exchange, um, are a couple of the options. Um, so the next slide."
  },
  {
    "startMs": 154720,
    "text": "So as you saw, it's not that simple, right? Historically, the engineering question for distressed was pretty binary. So you can predict a bankruptcy. E- either you predict it goes to a default or it doesn't. Today, that's become a lot more complex. Um, so a lot of these traditional machine learning method- methodologies just haven't kept up, and a lot of the scoring that was pretty"
  },
  {
    "startMs": 184660,
    "text": "important, um, maybe ten, fifteen, twenty years ago, um, uh, the big one is the Altman Z-score, um, has become now very static, um, in terms of a situation that changes, you know, sometimes monthly, quarterly, yearly, um, and also is in a much more opaque market. So if you've ever been following credit, the rise of private credit and some of these other areas have caused, you know, a lack of pricing. On the other hand, you have a"
  },
  {
    "startMs": 214500,
    "text": "very qualitative method of understanding and analyzing distressed credit, um, which is as a distressed credit investor, you can, you know, analyze financials, understand what's going on in the lender side, read the credit docs. Basically, you know, all the things a normal, you know, hedge fund investor or private, private equity investor would do, um, to understand a credit. This process is super manual, very hard to scale, obviously. Right? Now, the question is,"
  },
  {
    "startMs": 244560,
    "text": "um, you know, part of starting Axim and a question I thought about is what can-- how far can AI get at understanding a distressed, um, a distressed credit and the type of paths you can take? Um, and can you systematize what is effectively a pretty complex process, right? Um, so now what does it mean to understand a distressed credit, right? Like, as"
  },
  {
    "startMs": 274520,
    "text": "an investor, we have a lot of questions we're trying to answer. Um, the, the things that you think about, um, from a financial side, right? What is, um, you know, what is liquidity? What is the capital structure? And then you have questions from the legal side. How... You know, what's the structural priority of the debt, right? Like, or do you have higher priority or lower priority? And then you have operational issues like,"
  },
  {
    "startMs": 304380,
    "text": "okay, how do you understand the business? What are the revenues? Are the revenues gonna go up or down tomorrow based on fundamental, you know, fundamental analysis of the business, right? Um, hist-- right now, what we've seen in the market, you know, if you're following Frogo or Harvey or some of these, you know, big AI companies, what they've done is really think about benchmarking all these different questions, right? Like, how good is AI at answer- answering,"
  },
  {
    "startMs": 334240,
    "text": "um, wha- what part of the debt is structurally senior, um, and then kind of do rigorous benchmarking and make that, um, you know, call it the big finance bench. But I wanted to think about it slightly differently, right? Because the way I thought about it is the true understanding, right, of credit, and particularly distressed credit, is can you predict what happens next, right? Can I figure out and have a"
  },
  {
    "startMs": 364240,
    "text": "probability distribution of what path this takes in the future? Um, so kind of an interesting exercise I thought, step one, you know, you should do  is take all the... You know, I chose a company. So I chose iHeartMedia, which is a company that filed for bankruptcy in 2018. So it's, you know, it-it's been through the rigama-rigamarole. Um, it also went and did"
  },
  {
    "startMs": 394304,
    "text": "another stress transaction back in 2024. So this is a live situation. This is happening today. No one has a real, you know, answer for what's the future, right? Um, there's no ground truth. But what I did was, let's throw it in to the smartest, you know, models. I did Astra 6 on Max Intelligence, um, and Opus 5.5. And what I-- w-what was a really interesting kinda"
  },
  {
    "startMs": 423704,
    "text": "conclusion is that they disagreed. Like, they disagreed on what the probabilities of the future would be, um, given a structured context of the data, right? And so, uh, if you look here on, um, negotiated restructuring, Astra thought that was the best option, um, for a bankruptcy, um, Opus thought that that was the best option, right? Now, how do you, how do you say which is right or"
  },
  {
    "startMs": 453324,
    "text": "wrong? Y-you can't, 'cause I don't know what the probabilities are, right? I don't know what's gonna happen. I don't know if it's gonna be a bankruptcy. I don't know if it's gonna be a out-of-court restructuring. I don't know if it's gonna be a refinancing. I have my best judgment on things. I, I would say I lean towards Astra 6, but, you know, w-we're all human. And it's a kind of an interesting conclusion, right? Like, okay, wait, wait, they disagree. Um, so the second thing"
  },
  {
    "startMs": 483444,
    "text": "I thought about is, okay, how do I improve at least an assessment of their process, right? So I can look at, um, whether I agree or disagree with the different assumptions. Um, and so basically, I've created this sort of benchmark, um, where you, you know, we benchmark each layer. So obviously, these are-- at each layer, the LLM could mess"
  },
  {
    "startMs": 513384,
    "text": "up, right? On the extraction layer, I would say with public companies, it's, it's pretty, it's pretty solved. I would say with private loans and, and when you have, like, Excel documents and PDFs, that can, that can still be a challenge, right? Then you have the reconciliation or derivation layer. So extraction would be you got EBITDA. Uh, derivation would be, okay, now you have EBITDA and you have debt, now you can have leverage. Um, and then you have a judgment. Okay, now you have the leverage. Is this a"
  },
  {
    "startMs": 543024,
    "text": "distressed credit or not? What's your..."
  },
  {
    "startMs": 550384,
    "text": "Uh-oh."
  },
  {
    "startMs": 558184,
    "text": "Uh."
  },
  {
    "startMs": 563404,
    "text": "Is there, like, a... Okay, I'll just gonna-- I'll just keep talking.  So, um, basically the question is, is this..."
  },
  {
    "startMs": 574524,
    "text": "I think my computer died."
  },
  {
    "startMs": 582884,
    "text": "Um,"
  },
  {
    "startMs": 586364,
    "text": "sorry, one second. I'll tell you the- Do you have a charger maybe? I do have a charger, but it's not here."
  },
  {
    "startMs": 599244,
    "text": "This one? Uh, yeah. Looks like on the other side. No. Okay, I'll just, I'll just talk. That's fine."
  },
  {
    "startMs": 613484,
    "text": "Oh, okay. It's working."
  },
  {
    "startMs": 620064,
    "text": "Okay, so, so you got the judgment layer, right? And then, um, then, and then you can start... Okay, if you can assess each layer, right? Once you get to the prediction layer, you've assured the data points are good. You've assured that, um, the reconciliation layer is good. You've assured that there's some sort of reasonable judgment, right? And then once you've assured those things, then you can get"
  },
  {
    "startMs": 650084,
    "text": "to the prediction layer, which is really how, you know, as a human analyst, you think about a-all of these steps, right? You, you-- If you build a judgment on a bad data point, that's not gonna be a good judgment, and that's, that's just the way this works, right? So, um..."
  },
  {
    "startMs": 673084,
    "text": "Oop, here we go. So basically what we found, and maybe this is not surprising, but, um, thought it was interesting, is that once we actually created the structured reasoning process, you, you actually had both the intelligence, both the models actually converge in terms of what they believed the future probabilities of the business are gonna be. Right? So now,"
  },
  {
    "startMs": 703064,
    "text": "instead of saying sixty-two percent bankruptcy with Opus and twenty-five percent bankruptcy with Astra, now both these models are saying there's actually only a quarter or twenty-five percent chance of bankruptcy, and both models agreed that the negotiated restructuring path is actually the more likely path of the business. So what are kind of the big takeaways here, right? Um, one, I would say the first takeaway is when given-"
  },
  {
    "startMs": 733532,
    "text": "Non-verifiable questions, right? With the same structured context, organized context as a human. I mean, do these models have a predictive edge? And you have to say no, right? Because there are, in effect, even agents are human, right? In, in some sense of the word. Um, I won't get into the controversy there. Um, the second is, but a structured reasoning process can"
  },
  {
    "startMs": 763492,
    "text": "get the two models to, or, you know, different intelligences to converge on effectively the same view of the future and effectively a similar understanding of, of a business and the paths it could take. Um, but is that the correct path? We don't know, right? Um, and this is really only at the truest measure, right? 'Cause otherwise any sort of historical analysis would create some sort of look-ahead bias."
  },
  {
    "startMs": 793292,
    "text": "Um, but I think the, the really interesting thing that I've been thinking about is, um, the convergence is pretty interesting, too, right? Because if you can systematically build a process that actually converges on the same probabilistic outcomes, that is effectively what is a price in a market. Um, and so I, I thought that was an interesting takeaway. Um, so that, that's the last thing. And then just a quick kinda"
  },
  {
    "startMs": 823312,
    "text": "spiel on me. So my name's, like I said, Harneet. I'm building Axim. Um, kind of background on me. I spent 10 years in finance, um, the last few as a distressed credit and special situations investor at a, um, a distressed PE/hedge fund. Um, and then this is the QR code if you're interested in Axim. Um, and that's all I had. Thank you."
  }
]