Masters of Risk | Season 3 Ep. 7: What risks do you face when launching a business?

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Stewart Webster 00:00:00

Welcome to Masters of Risk Season 3 Episode 7. Today on Masters of Risk, we're joined by Michael Brawer, CEO of Transparency Analytics. A firm aiming to reshape how markets understand creditworthiness, risk and the integrity of financial data. Michael has served in several senior executive roles across the financial services industry, giving him a unique vantage point into how ratings, regulation and risk analytics have evolved over the years. Few people have had such comprehensive view of both the strength and blind spots in the systems investors rely on every day. Now as the leader of transparency analytics, Michael is building something unusual in the credit space, a company designed around openness, modern data infrastructure and the belief that investors deserve to see inside the models they depend on. In today's conversation, we explore what it takes to build a challenger firm in a highly regulated environment, how technology is illuminating persistent gaps in legacy risk models and why transparency isn't just an ethical stance, it's a strategic one. Michael, thank you for joining the podcast.

Michael Brawer 00:01:57

Thanks for having me.

Stewart Webster 00:01:59

Great. So let's go ahead and get started. I guess, can you start by talking about Transparency Analytics' business model and the value you're providing to clients?

Michael Brawer 00:02:07

Sure. So Transparency Analytics is a benchmark and analytics firm with a very heavy initial focus on the private credit markets. So we are very quantitative in nature in our approach, and we're focused on small and midsized firms, oftentimes private equity-backed small and midsized firms that are raising capital by way of a debt issuance. We tend to distinguish ourselves from some of the larger benchmark providers by taking such a quantitative approach and avoiding some of the more subjective elements -- so effectively, we're leveraging technology to provide clarity on credit measurements, and we're taking the position explicitly that your environmental score doesn't service your debt, your governance score doesn't service your debt. Debt is serviced by cash flow. So we're using technology to provide extreme clarity around the cash flow metrics pertaining to given companies to more quickly, more efficiently, and I would argue more effectively provide a higher level of transparency around the company's ability to service its debt.

Stewart Webster 00:03:23

That's very interesting. And it's great to see how you're using these quantitative risk models and I guess, evolving them, if you will. Many of these models have been around for some years now, and it seems like you're providing a more, I guess, evolved experience, if you will, around these risk models. Now I guess a little bit about yourself -- when I look at your background, I think there's only one way to explain it. And to me, that's financial industry pedigree. You've held roles as a Chief Compliance Officer, Chief Operating Officer and Chief Risk Officer. And these were all roles at notable financial institutions. So clearly, you're providing an invaluable experience to your new firm, transparency analytics. Again, you've held senior roles across the industry in compliance, operations and risk management. How did that executive experience shape your vision for transparency analytics?

Michael Brawer 00:04:16

So I learned a lot about the industry in those roles. The trick, of course, is to apply that to startup life where you're going from 0 to 1. And that's a very different experience than being an executive of an existing company that's already well on its track to effectively servicing its customers. So that poses challenges. That also creates some opportunities. But all that time I spent in those various roles that you mentioned, it's very helpful. It's helpful in terms of identifying areas where we can be innovative and where we can improve on the industry. So great experience. And now, of course, we're focused on applying that as we go from 0 to 1. In a start-up, you need to be disruptive. So corporate experience is also helpful in terms of identifying where the industry is having trouble evolving and meeting some very rapidly developing trends. So you've got this explosion in private credit all of a sudden, which has caught everyone's attention. And oftentimes, not always, but oftentimes with private credit, you're dealing, as I mentioned, with these smaller, oftentimes PE-backed firms. And these firms are -- they're moving quickly. They're looking to gain market share themselves from entrenched competitors. So that's a very different type of rating analysis. It's a very different type of product delivery as compared to rating and performing analysis on larger, more established firms.

Stewart Webster 00:06:05

Yes. As you mentioned, you're going from 0 to 1 or 0 to Infinity. And it seems like you're clearly building a large company, right? I think if we were to hold this conversation 10 years from now, I would say, how did you build that large company from the ground up, if you will, right? And as you mentioned, this is going from 0 to 1. One of the things I observed over the past decade or so or a decade or more that I've been in the industry is this fusion of company types. And what I mean by that is, traditionally, you have an advertising firm and then now they're starting to, I guess, incorporate more technology and then now they're an ad tech firm. And then you see the same thing with finance institutions. They're a traditional financial institution, but then they start employing technology and then now they're a fintech, right? So I'm seeing like this fusion between different

worlds. So from your perspective, at transparency analytics, do you see yourself as more of a technology company, a financial institution, a credit rating agency or maybe none of the 3. How do you view yourself as a company?

Michael Brawer 00:07:11

Yes. It's a significant challenge raising money in the age of AI, where I think as we sit here today, roughly 2/3 of all VC dollars this year are going into AI companies. So that takes -- if you're not native AI, if you're fintech, you're effectively taking 2/3 of the dollars out of the equation and now you're left with 1/3 of the dollars. And some of those dollars focus on startups, some focus on more mature companies. So the challenge for a fintech firm is getting attention from VCs in this environment where so much of the capital, the majority of the capital is being directed more to AI firms. And then, of course, couple that with the challenge of being a first-time founder and then raising funds is definitely not easy right now. But I think what's helped us in terms of fundraising success is this hybrid model that you alluded to, where we've got a strong New York-focused financial services vision, coupled with a very deep bench in California that it includes people who are veteran founders. It includes people who have start-up culture embedded into their DNA. And I think that's essential. You have to bring both the expertise that we see in New York and financial services and you have to bring the start-up culture and you have to bring them both to bear at the same time.

Stewart Webster 00:08:53

Now I would say transparency, it's critical in the financial markets. If you're working on the public side, let's say, you're a public asset manager and you're constructing an ETF or a mutual fund, you have to disclose those investments to your clients, right, so they can understand where that capital is being deployed. If you're working in accounting, there are financial reporting standards. If you're working in investment reporting, there's global investment reporting standards that we abide by. And those initiatives help kind of underscore the efficient

market hypothesis that we talk about. And it seems like that transparency, it's a school of thought that you also subscribe to because you put school of thought or you put transparency at the center of your name and your mission. So -- and you're also dealing in, I guess, it's a private space, right? It's private equity, private capital, and it seems like transparency analytics, you're providing transparency to an opaque market, right? So why is openness so critical in credit and risk analytics?

Michael Brawer 00:09:56

Yes. So private credit, as the name implies, is opaque. And every year, the public markets constitute a smaller percentage of investable assets. And every year, the private markets constitute a larger percentage of investable assets. So in this environment, we believe that it's essential to provide market participants these insights into the plumbing and infrastructure of the private markets in a way that shines a light on things that are not going to be instantaneously visible so that they can make informed -- market participants can make more informed investment decisions.

Stewart Webster 00:10:43

And I spoke about this earlier. I would say the traditional risk models that have been out there for some time now, the cash flow models, they're quite rigid. Our field of credit analysis, it can be highly regulated. And it seems like the credit analysis firms or just financial institutions in general, they're kind of less likely to be innovative, if you will. But it seems like you're positioning yourself where you can be innovative and flexible enough to capitalize on some opportunities. So what are the biggest limitations or blind spots in legacy risk models that your approach addresses better?

Michael Brawer 00:11:19

Yes. And maybe blind spot in this context, the way that I look at it isn't exactly the right word. But I think sometimes there's a little bit too much of a diversion into qualitative analytics. And they're not blind spots because I think on the contrary, they can be quite enlightening. I think in the private markets, however, it's important to start where you don't have transparency, where you have these very -- these murky financial instruments. It's important to start with hard facts first. So our position is only after you understand the cash flow metrics, then you should delve into the more kind of I'll say, subjective or touchy feely topics. But maybe one thing that is a true blind spot is the nature of marking every quarter, the investment marks, these valuations that we see, for example, in public BDCs, they're marked quarterly. And I think that is a major blind spot for the industry because once you're a day, a week, a month past your quarterly mark already, you're dealing in an information deficiency, so to speak. So right now, some funds are voluntarily working toward moving their marks from quarterly to monthly. I would argue that even monthly is not really sufficient I think for people to make informed investment decisions, we need to even do better than monthly. And so some of it is how frequently. And the other is there's a narrative now in the financial press that's not completely unfounded, this term mark to myth, where there's a lot of leeway and a lot of subjectivity allowed in how you mark illiquid securities. I think the industry could benefit from more transparency around how the accounting standards for valuing illiquid securities are actually applied. So the main accounting standard for valuing illiquid securities being FASB ASC 820. And if you're -- it's broken down into various levels, 1, 2 or 3, and those -- depending on which level you're able to use, you may have a more market-driven approach to setting a mark. or you may have a more cash flow-based approach to setting a mark. And I think for market participants to better understand, is this mark derived from what we've seen in terms of trades on similar assets? Or is it derived more from a discounted cash flow modeling standpoint? I think all that would be helpful in terms of dealing with some of those blind spots as well.

Stewart Webster 00:14:20

Yes, that was an interesting point you brought up about measuring performance and making it more real time, right? When you look at developing an investor benchmark, there are certain requirements. And I'm thinking of 2 right now. One is investability and measurability, making sure that the investments are measurable. And that's kind of what you're getting at where we should be going from quarterly to monthly to perhaps weekly or even daily, which is quite an undertaking in the private company space, but you would think with the advancements of technology and technological reporting, this could be something that we could start implementing to provide more transparency.

Michael Brawer 00:15:01

It's definitely -- yes, there's no question about it. It's definitely achievable. We can do better in terms of timeliness, and we can do better in terms of transparency, meaning exactly how we apply those accounting standards for valuing illiquid securities. So there's a couple of ways where the industry can do a lot better.

Stewart Webster 00:15:23

Absolutely. So on this season though, the Masters of Risk podcast, we've been talking a lot about technology. We've been using a lot of buzzwords, whether it's artificial intelligence, and there's a lot around AI, right? I think people will be hard-pressed to turn on to TV and not see anything about artificial intelligence, especially around financial news reporting. So are there any new technologies like AI or machine learning that you're leveraging to improve your analytics or risk models?

Michael Brawer 00:15:52

Yes. As you might imagine, with our office based in the Silicon Valley area and that being kind of part of our DNA, we're Pro AI. I think the key factor is knowing

when to use AI in financial analysis and knowing when not to use AI in financial analysis. And so we've thought about this extensively, and we've written and we've published about this extensively. Some of it is understanding when do you need a high-level overview of a complex topic? Or when do you need a near perfect level of accuracy. When do you need an audit trail? I think the biggest challenge with so many of these AI models going closed source, it's very difficult to get an audit trail out of a closed source AI model. So there's another kind of challenge that's a little counterintuitive. Over the last several decades, it's become a business norm to start with kind of mundane tasks when you're deploying a new technology and then upscale over time. I think with AI, sometimes you're almost better off doing it in a completely reversed fashion. So if you need perfect accuracy on a series of mundane tasks and the best models, when you read the Google Gemini paper or you read the OpenAI technical paper, the best models are achieving 93% accuracy. If that's not enough in financial services, some of those repetitive tasks where you need near perfect accuracy and you need a near perfect audit trail are perhaps a less appropriate use of AI as compared to using AI for something like summarizing and distilling highly complex and providing optionality of approaches on a number of highly complex tasks. And then oftentimes, that's where AI really shines. It's summarizing incredibly complex documents or multiple complex documents, multiple options, summarizing benefits, summarizing drawbacks associated with complex approaches. So sometimes you're better off using AI in more complex ways as opposed to kind of the traditional way of starting mundane because you're a little nervous and then getting over time, increasingly more complex. It almost have to flip that on its head sometimes with AI.

Stewart Webster 00:18:30

No, absolutely. And as you build this company, I'm sure you've had several different plans, and I'm sure maybe you had to deviate a little bit or become more flexible and kind of adjust accordingly. So what technical or operational risk have you encountered while building this modern data-driven risk agent, especially under pressure to scale because you're starting -- you're a start-up, right? And you have the capability to scale, the knowledge to scale as well. What

operational risk have you encountered while trying?

Michael Brawer 00:19:02

Yes. As a start-up, we -- it's critical for us to get our engineering right the first time. We need to avoid rescaling due to an erroneous initial build, for instance. Our focus has to be customer first in all of tech and operational processes. Our Head of Engineering is fond of saying that different engineering cultures give rise to different incentives. So if you take, for instance, outsourcing IT, that gives rise to certain types of incentives. If you have a seemingly limitless supply of cheap labor, you can just throw more labor at a problem, you may be incentivized to kind of deploy something quickly, identify problems and fix them over time. Whereas our tech team is based in Silicon Valley, that's expensive. It has to be perfect the first time. So they have a series of incentives. Culturally, Silicon Valley has evolved that way, where they know it's too expensive to keep throwing labor at something and fixing it repeatedly because of an erroneous initial build. So it has to be really robust the first time. It has to offer a great user experience the first time. And my perspective is you can't replicate that Silicon Valley culture where it has to be perfect and robust and offer a great user experience on day 1. You can't replicate that through an outsourcing model. So if you want to kind of as a start-up, you want to mitigate that significant operational risk of an erroneous initial build, you're better off going where the culture is so demanding in terms of tech engineering, and that's Silicon Valley.

Stewart Webster 00:20:58

And I guess for my last question, looking ahead, what do you worry about most? Is it regulatory changes, data access, client behavior or getting outcompeted? What keeps you up at night?

Michael Brawer 00:21:10

What keeps me up at night in the fintech space is data access. That's always top of mind, whether it's traditional analytics or whether it's AI, value requires a continuous flow of useful data. So there's data that we get from clients. There's data that we create and there's data that we buy. And we are always kind of thinking and I sit awake sometimes thinking about what data is useful now. What data is not necessarily useful as it arrives at our company, but what data can be transformed to make it useful and figuring that out is, I think, critical to our success.

Stewart Webster 00:22:00

That was Michael Brower, CEO of Transparency Analytics, offering a rare insight look at what it means to build trust in a market where trust has become one of the most valuable and scarce assets. His insights remind us that risk doesn't just show up in markets or portfolios. It shows up in the assumptions we make, the data we select and the models we depend on without always questioning what's underneath them.

As Michael highlighted, the future of credit analysis belongs to firms that embrace transparency, challenge long-standing conventions and build systems that investors can understand, interrogate and rely on with confidence. It's a powerful reminder that innovation in finance isn't only about technology, it's about reshaping the infrastructure of trust. If you enjoyed this episode, don't forget to like, share and leave a review. Thanks for listening to Masters of Risk. I'm Stuart Webster, and we'll see you next time.