Alt Goes Mainstream: The Latest on Alternative Investments, WealthTech, & Private Markets
00:09:07 · Yann Magnan, Michael Sidgmore
[00:09:07] S1: My view is that you cannot automatize that work partially, but only partially. A significant component of any valuation process is actually the human judgment that goes into it. And you would have a different view on certain parts of a business that I would have. It is very important that we have these different views. The valuation process is more like a language that helps you articulate a view on the business and, in the end, form a view on the value of this business. But your human judgment is going to be very important. My human judgment is going to be very important. I don't think that we should automate that part of a process. Automating the calculation, I mean, that's, of course, that's the simple things that you have to do. Automating how you get or facilitating, I would say, other than automating, facilitating how you get to a faster human judgment, how you get faster of information that will help you form a view as to how this business is really comparable to that other one, which by the way, that does not exist. There's not one business that fully compares to another one. So how do you understand the nuances faster so that you can come up faster with the adjustments that you need to make in your valuation processes and in your valuation analysis so that in the end you come up with the proper construct of this solution. [00:10:24] S0: Do you think AI will eventually be smart enough where it will actually do a better job of the qualitative side of what you just described, the human judgment around valuation?
00:10:24 · Michael Sidgmore, Yann Magnan
[00:10:24] S0: Do you think AI will eventually be smart enough where it will actually do a better job of the qualitative side of what you just described, the human judgment around valuation? [00:10:34] S1: Possibly. But I will not bet on that. And I think we are years away from that happening if it happens. The work we are doing at seventy three Strings facilitating the valuation for the owners of the non listed assets. Ultimately, objective of this work, the objective for clients is to report their valuation to their LPs, to their investors. [00:10:58] S0: And do it faster.
00:10:58 · Michael Sidgmore, Yann Magnan
[00:10:58] S0: And do it faster. [00:10:59] S1: And do it faster. So do it faster, technology can help. The issue when you say you want to report something to a third party, and we're talking about money here, and the real money, and very significant money. The issue is that someone has to be accountable for that. You can't just spit out a number. You have auditors that are at play as well that will look into what things are being done. So can AI come up with a very good estimate of the value of an asset? Yes, probably. I would actually be ready to bet that there's already some very interesting hedge funds that are doing that quite effectively. But here is the thing. Again, the the world I live in, the issue is not just about doing the validation, is providing a certain level of comfort to the LPs that it's done properly and that someone is responsible and accountable for that. So the moment that you put an AI algorithm into it that just spits out a number, you're defeating the purpose. So what I'm saying, will AI get there? Potentially, but the key thing for AI to get there will be to find a way that the other part of the equation, the LPs and their auditors and the regulators as well, will be comfortable that the AI is coming up with the right answer. Because if they are not, who are they going to put the blame on if something goes wrong?
00:12:19 · Michael Sidgmore, Yann Magnan
[00:12:19] S0: I think that brings up a really interesting question related both to 73 strings, but also more broadly around technology being built in private markets. You rely on different stakeholders and technology solutions to be able to do the work that you do at 73. Where do you feel, at least right now as a business, your work starts and stops at 73? And then where do you include and partner with other service providers? [00:12:47] S1: Our work starts with the data or the documents that are sent from the portfolio companies, the borrowers to our clients. That's where we're picking up the work. We're picking up these documents. We have developed a set of technology that can read any type of document, PDF, PowerPoint, Excel, email. What am I missing? Well, probably a board deck, a quarterly report, an annual report, whatever. And it can read [00:13:11] S0: someone's mind. [00:13:11] S1: And and [00:13:12] S0: it can read someone's mind. [00:13:13] S1: Maybe at some point. And collect these documents, identify the relevant KPIs, collect the KPIs, standardize this information, create consistency. And then that information is used for evaluation. So from that perspective, what we need is, well, not much. Eventually, we need a Dropbox from the portfolio company, eventually that could accelerate certain things. But most often, we are seeing that the portfolio companies would send these documents by email to the investment teams, and it would be automatically forwarded to our platform, and it's done.
00:13:13 · Yann Magnan, Michael Sidgmore
[00:13:13] S1: Maybe at some point. And collect these documents, identify the relevant KPIs, collect the KPIs, standardize this information, create consistency. And then that information is used for evaluation. So from that perspective, what we need is, well, not much. Eventually, we need a Dropbox from the portfolio company, eventually that could accelerate certain things. But most often, we are seeing that the portfolio companies would send these documents by email to the investment teams, and it would be automatically forwarded to our platform, and it's done. [00:13:45] S0: Even if different companies report their metrics in different ways, you're still able to handle all that? Because I think that's part of the challenge when you think about different categories, accounting is one example. Yeah. Part of the challenge with something like automation of certain things like accounting or same here where it's valuations is everybody may have a different way of reporting something.
00:14:06 · Yann Magnan
[00:14:06] S1: Yeah. Everybody has a different way of reporting something. And the GPs typically have one way... They want to have one way of looking into each and every information because we want to be able to look through their portfolio, look across their portfolio, and understand when they invest into one specific strategy or one specific sub industry. They want to be able to benchmark companies. They want to be able to understand or to learn from one company and replicate with the other one. But if you want to do that, you need to make sure that the data that you get is very consistent and standardized. Otherwise, you just can't do anything out of that. The beauty of technology is that you can do that automatically now. We have 98.6% accuracy when we do that. It's just like massive. And so it's done automatically. It saves a lot of time. That means that these very smart people that work in these GPs, they don't have to spend like two or three hours at night just collecting these numbers and putting that in the proper standard and proper format. And that's a lot of time saved that they can use to do something else, much more value add. So that's a bit the basic value proposition here. But what we are seeing happening is what I was saying as well is that once all these data is in one place, properly structured, probably standardized, There are so much more that you can do out of that. That's why you can start thinking about AI algorithm that can be like early warning signals, red flags, can you inform the business back with certain information that are not obvious to see if if the information is scattered across multiple multiple folders, multiple Dropbox, multiple Excel spreadsheet. It's just undoable.