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

In a world where uncertainty is the only constant, who are the people making the bold decisions to shape global markets? Welcome to Masters of Risk, the podcast that goes beyond headlines and dives deep into the minds of the leaders, visionaries, and strategists redefining how we think about risk. I'm Stuart Webster, your new host. Each month, we'll challenge assumptions, unpack complex systems, and explore the hidden forces driving credit, markets, energy, and beyond. From boardroom decisions to geopolitical shocks, this is where a risk meets insight. Grab your front row seat and let's get started. Welcome to Masters of Risk, season three, episode seven. Today, on Masters of Risk, we're joined by Michael Brauer, 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 strengths and blind spots in the system's 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 a belief that investors deserve to see inside the models they depend on. And 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. Thanks for having me. 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? Sure. 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 and our approach, and we're focused on small and mid-size firms. Oftentimes, private equity backed small and mid-size 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 government 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. So it'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, that many of these models have been around for some years now, and it seems like you're providing a more 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. Obviously, 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, you know, an invaluable experience to your new firm, transparency analytics. Again, you've held senior roles across the industry and compliance operations and risk management. How did that executive experience shape your vision for transparency analytics? So I learned a lot about the industry in those roles. You know, the trick, of course, is to apply that to startup life, where you're going from zero to one, and that's a very different experience than being an executive of an existing company that's already well on its track, just to effectively servicing its customers. So, you know, that poses challenges that also creates some opportunities. But, you know, all that time I spent, you know, in those various roles that you mentioned, you know, 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 zero to one, you know, in a startup, you need to be disruptive. So, you know, corporate experience is also helpful in terms of identifying where the industry is having trouble evolving, in meeting some very rapidly developing trends. So, you know, you've got this explosion in private credit, all of a sudden, which has caught everyone's attention. And oftentimes, you know, you're 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. Yeah, as you mentioned, you're going from zero to one, or, you know, zero 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, you know, I would say, you know, how did you build that large company from the ground up, if you will, right? And as you mentioned, this is going from zero to one. You know, one of the things I observed, you know, over the past, you know, decade or so, or decade or more, that had been in the industry is this fusion of company types. And what I mean by that is, you know, traditionally, you would 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, 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, that transparency and the lyrics, do you see yourself as more of a technology company, a financial institution, a credit rating agency, or maybe none of the three. You know, how do you view yourself as a company? Yeah, it's a significant challenge, raising money in the age of AI, where I think as we sit here today, roughly two thirds 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 two thirds of the dollars out of the equation, and now you're left with one third of the dollars. And some of those dollars focus on startup, 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 startup 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 this startup culture, and you have to bring them both to bear at the same time. Now, I would say transparency, it's critical in financial markets. If you're working on the public side, let's say you're a public asset manager, and you're constructed in 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 and 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 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 transparency at the center of your name and your mission. So, and you're also dealing in, I guess, you know, 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 open to so critical and credit and risk analytics? And so, private credit, you know, 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, you know, in this environment, you know, we believe that, you know, it's essential to provide market participants, you know, these insights into the plumbing and infrastructure of the private markets in a way that shines a light, you know, on things that are not going to be instantaneously visible so that they can make informed market participants can make more informed investment decisions. And I spoke about this earlier. I would say, you know, the traditional risk models that have been out there for some time now, the cash flow models, they're quite rigid, you know, 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 and legacy risk models that your approach addresses better. Yeah, and maybe, maybe blind spot in this context, the way that I look at it as an 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, you know, 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, you know, our position is only after you understand the cash flow metrics, you know, then you should delve into the more kind of, I'll say subjective retouchy, feely topics. But, you know, 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, you're quarterly mark already, you know, you're dealing in an information deficiencies, so to speak. So, right now, some funds are voluntarily, you know, working toward moving their marks from quarterly to monthly, I would argue that even monthly is not really sufficient. You know, 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 marked to myth, where there's a lot of leeway and a lot of subjectivity allowed and how you mark illiquid securities. You know, I think the industry could benefit from more transparency around how the accounting standards for valuing illiquid securities are actually applied, so that the main accounting standard for valuing illiquid securities being FASB ASC 820. And if you're, it's broken down into various levels, one, two, or three, 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. Yeah, 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 two right now, one's 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, you know, 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 terms. It's definitely, yeah, 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 those accounting standards for valuing a liquid securities. So there's a couple of ways where we're the industry can do a lot better. Absolutely. So on this season of the Masters of Risk Podcasts, 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, you know, people would be hard pressed to turn on the 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? 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. You know, 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, you know, we've thought about this extensively and we've written them and we've published about this extensively. You know, 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? You know, I think the biggest challenge with so many of these AI models going close source, it's very difficult to get an audit trail out of a closed source AI model. So, you know, 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, where you read the open AI 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, you know, or perhaps a less appropriate use of AI, you know, as compared to using AI for something like summarizing and distilling highly complex tasks 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, you know, over time increasingly more complex. It's almost have to flip that on its head sometimes with AI. Absolutely. And as you build this company, I'm sure you've had several different plans and I'm sure, you know, maybe you had to deviate a little bit or become more flexible and kind of adjust accordingly. So, what technical or operational risks have you encountered while building this modern data driven risk agent, especially under pressure to scale because you're starting, you're a startup, right? And you have the capability to scale the knowledge to scale as well. What operational risk have you encountered while trying? Yeah, as a startup, 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 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 know, 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 one. You can't replicate that through an outsourcing model. So, if you want to kind of as a startup, 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. 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 out-competed? What keeps you up at night? 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, you know, there's data that we get from clients, there's data that we create, and there's data that we buy, and we're 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, you know, all figuring that out is I think critical to our success. That was Michael Brower, CEO of Transparency Analytics, offering a rare insight look at what it means to build trust in the 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 longstanding conventions, and build systems that investors can understand, interrogate, and rely on with confidence. It's a powerful reminder that innovation and 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 at Risk. I'm Stuart Webster, and we'll see you next time.