Transcript for "Edited Port Intel":
Hi, everyone. Thanks for joining us today for the Portfolio Intelligence and Deal Analytics, demo here at Orpheus Systems. My name is Humphrey Wood. I'm joined by Nate Eisenberg. Before we get started, just a few housekeeping notes. So feel free to submit any questions at any time. You can use the q and a tool in Gold Coast to do that. We'll save some time at the end to answer your questions. And if we don't get your questions today, somebody from our team will reach out to you with a follow-up. You can find today's presentation materials and download those in the handout section. We're also recording session, and we'll send the replay to everyone who registered, at the webinar. Finally, when we wrap up, you'll see a short feedback survey. We'd really appreciate you taking a minute to share your thoughts. So what are we going to look at today? Today, Nate and I are gonna show you the latest capabilities across portfolio intelligence and deal analytics, including how you can benchmark against private credit market data, monitor portfolios and borrowers, generate AI powered insights, and bring more of that work into a connected workflow across your monitoring cycle. We'll also give you a look at where we're headed with one view on new AI native layer for private markets. With that, let's get started. And I'm gonna hand over to Nate to talk about the private credit landscape, and insights. Great. Thank you, Humphrey. You know, I think, just as a quick kind of teaser before we jump in here, you know, I think private credit in particular, there's been a lot of kind of, you know, news in the market over the last kind of year and change and, you know, I think it's an area of of heightened focus and, you know, I think what we've heard from from our clients in the market is, you know, a particular need for greater transparency and greater insights into, you know, what's really happening within private markets and in private credit in particular. And so what we're gonna show you around the portfolio intelligence and deal analytics is really all views, kind of vision for how we empower, you know, smarter portfolio decision making and investment decisions across our client base. With that, Humphrey, feel free to dive in. Okay. Brilliant. Thank you. So auto intelligence is, a reimagining with, some core new tools that and technologies that exist today that we've created for our clients. So we are taking what is typically a kind of data heavy, difficult monitoring process across portfolio companies and making that empowered with artificial intelligence, empowered with deals, analytics that give you market context, and scores to easily visualize and gather insights on your portfolio to both understand the risk of that portfolio and also to communicate to investors or stakeholders, the status, the health of the credits, in more, of your investments. What we find is that many of our clients spend a huge amount of time drawing together disparate resources into monthly or quarterly portfolio reviews. They also need to spend a very significant amount of time, understanding, where the risk is in their portfolio, where to focus time and effort, where to be efficient, and then how to best communicate that data to their stakeholders or to their investors. And what we found as well is that often questions rather portfolio become a project, so ad hoc invested EDQs can be keep become very, very time consuming. So with Portfolio Intelligence, we've reimagined what that workflow looks like and what the solution for those problems are and how to upgrade our clients' ability to monitor and communicate their portfolio health. So we have, tied together a workflow that goes all the way from portfolio company document ingestion through to extraction of insights via AI. So the journey that we are launching with Portfolio Intelligence is using DocumentIQ to scrape financials, credit agreements, and other documents related to your portfolio companies, taking them into our Nexus data platform, which combines data across, front and back office solutions. Visualizing that data within one view on new AI native layer where there's a dedicated workspace for analysts, IR team, investment relation teams, and middle office teams to view, interrogate that data. We then take AllView's proprietary deal analytics data and tools to place that data within the market context. And then finally, we use AndyAI or in house, analyst agent to be able to extract insights from that data to allow you to better focus on credits that are in weaker health and to be able to turn routine monitoring into a much more efficient task. So you have a in Portfolio Intelligence, you have one workspace for the full monitoring workflow. So that really is to be able to monitor the, financials, covenants, commentary, of, and attributes of your portfolio company to be able to compare that to, to get intelligence tools and to use those insights to extract the relevant information to get to the actionable information quicker. And, really, that is one layer across the credit life cycle for our clients. And where we see it really applying, is across underwriting investments. So understanding, what your current portfolio looks like, understanding where new investments fit within that portfolio and being able to place those potential investments against the market benchmarks to monitor your portfolio. So if your existing investments to be able to understand the health and the risk and communicate those, and then finally to communicate those those investments. So for your investor relations teams as well. I'm then going to hand it over to Nate to talk about the, deals analytics proportion of the offering. Awesome. Thank you, Humphrey. Yeah. So I think that Portfolio Intelligence, you know, foundation is is really critical because everything that we're talking about in deal analytics is really designed to extend from the same workflow with a market lens built on, AllView's proprietary Nexius intelligence dataset. And so for the next thing that I just wanna focus on kind of how we are bringing private market context, AI enabled access, and risk signals into portfolio intelligence. And, really, you know, we're doing that across underwriting, portfolio monitoring, and reporting context. At a high level, you know, deal analytics really gives your team a dynamic market reference for private credit. And the goal here is, you know, not to create some kind of stand alone data interface or another place to export data, but this is really to bring relevant private markets context directly into the portfolios and deal workflows that your teams are already using. And that means starting with, you know, a loan or a borrower you already know, then comparing its operating performance, leverage, loan to value, covenant profile, and other key terms with a relevant private credit cohort. The benchmark layers currently includes over 150,000 assets and securities and more than 200 KPIs and has been built across a timeframe of kind of twenty to twenty five years of of private markets history. And that foundation, I think, really enables, you know, three practical capabilities. The first being kind of comparables, benchmarking at the borrower and soon the portfolio level. Second, the ability to create custom benchmarking. And third, risk analytics and AI insights directly within your data and your portfolios. The key idea here is pretty simple. You know, your own portfolio remains a starting point and deal analytics adds the external context needed to help interpret and understand it. So to kinda double click into those three capabilities, you know, as Humphrey mentioned, we think about kind of portfolio intelligence as as powering, you know, workflows throughout the full deal life cycle, you know, from underwriting through monitoring to valuation and investor communication. And likewise, we think about the data in the same way and really helping to contextualize and provide, you know, more intelligent decision making for each of those points within a deal life cycle. So, you know, during underwriting, your team can compare borrower performance, capital structure, covenants, and other key terms side by side with the relevant private credit cohort. And when the workflow begins in credit for an office, that deal context can pass directly into portfolio intelligence so the user does not have to, you know, recreate that analysis in a separate market tool. The second capability is, you know, the ability for users to create and persist their own custom benchmarks. So every credit is different as as you all know well, and oftentimes, the broad market median is is not good enough. Right? And this builder really lets user granularly dial in the relevant cohort. That cohort can then be saved, refreshed, applied to new investments. And, you know, it really creates a repeatable market lens to power, you know, quarterly review, evaluation discussions, you know, investment committee reviews, or even kind of specific LP questions as they come up. The third capability is, you know, really around risk analytics and AI powered insights. And you, the team, you know, can evaluate risk signals such as, you know, covenant actuals, cushion, breach rates, and historical trends against the market, and then use, you know, AI as just a commentary to help frame what changed and and what matters. You know, for example, a user might see that a borrower's leverage remains within its own kind of covenant threshold range, but is, you know, deteriorating relative relative to comparable credits. And that is a, you know, more useful signal than looking at a compliance for a particular borrower, you know, in isolation. And really so across these three capabilities, the objective is is not just more data. Right? It is faster interpretation. It's stronger internal discussion. It's clearer evidence behind the investment team's judgment. So I wanna talk a little bit about how we're, making NextEase Intelligence data and analytics built, you know, in house here at AllView, and making that accessible to your AI tools. You know, we're excited to announce the launch of our first MCP, which empowers users to ask, you know, really sophisticated private credit questions in natural language and ground the response in all views proprietary NexSys intelligence dataset. I think that grounding part is is, you know, maybe the most important part and that the answer is not, you know, based only on, you know, a general purpose model. The question is is really matched to the relevant kind of private credit characteristics, trends, you know, particular credit analysis or performance and other risk indicators before the response, you know, even comes back. This can, you know, easily shorten the research and and risk monitoring workflows that would otherwise require multiple filters, exports, calculations, and manual interpretation. I think a good example to this is is the question shown here on the screen in in this middle column. For instance, where is the refinancing wall colliding with weakening credit? Answering that well, you know, requires bringing several threads together, you know, understanding upcoming maturities, leverage and coverage trends, and amendment activity, and then identifying where the signals overlap. So the MCP allows the user to initiate that type of analysis with a single business question instead of navigating each dataset separately. The output is, you know, really intended to be sourced and decision ready, but it is still an input into the team's established review processes. We don't see this kind of replacing human judgment, but, you know, really kind of complementing, the decisions that your teams are making on a day to day basis. I think the broader opportunity is really to make differentiated private credit intelligence available beyond just kind of predefined dashboards, you know, without requiring every user to be a data specialist here. And I think it's helpful, you know, just giving some of the newness of MCP functionality to the market to just give a quick walk through of kind of what that process would look like for a user. So it really does begin with with the ask. You know, the user stays inside their own AI tool that their team is already using on a day to day basis and enters, you know, private credit question in natural language. The next is is kind of connection. The MCP providing that connection, in standardized way that, you know, routes the analytical request to the Nexeus Intelligence dataset and analytics engine. And then we ground the question. Nexeus Intelligence identifies the relevant market trends, comparable credits, borrower performance and other key indicators needed to address the request. Fourth step is really analyzing, you know, the capability filters and assembles deal level evidence built specifically for private markets. And this is where a broad question, you know, can be translated into, you know, the underlying metrics, cohorts and analytical steps required to, you know, really answer it thoroughly. And finally, the answer returns to the user's AI workflow as source decision support, something the team can directly evaluate, challenge, and use within its own existing processes and research work streams. I think a helpful way to think about this is, you know, this distinction here is, you know, the NCP is the connection layer. Nexius intelligence is the data and the intelligence layer behind it. For clients, the value is that private credit context can meet the user where the work is already happening, and the team can move from question to evidence more quickly while preserving the governance, confidentiality, and human judgment that sophisticated credit work requires. So we jump to the next slide. I'm excited to, tell you about, the forthcoming feature to Portfolio Intelligence, and this is the an in house built, analytic that we're calling the all the risk score, which is really designed to answer a practical monitoring question. Across a large portfolio, where should the team look first and where and where should they prioritize their review and and attention. So the all view risk score, you know, really converts several dimensions of borrower performance and credit pressures into a, you know, single borrower quarter signal. And those inputs, you know, include kind of high level, financial performance, I e revenue and EBITDA trends, leverage, as well as covenant trends, loan to value, covenant stressors and breaches, and the persistence of duration over time. So the output is both kind of that zero to one adjusted risk score as well as, you know, several different categories of of kind of drivers to help provide context and clarity on why a given borrower was scored at a particular level and what has led to that kind of trend line over time. Score is paired with a trend, a confidence indicator, and then specific drivers behind that result. And we're designing this really, you know, to to try to optimize for for trust and stability. So trend persistence helps reduce noisy category changes from one quarter to the next. And confidence weighting really makes it clear when the available inputs are more limited and where your teams can perhaps kind of provide additional clarity to improve the confidence weighting of that score. So, importantly, I think this is, you know, an early warning and prioritization tool. It's not intended to replace an internal rating or, you know, formal credit opinion or certainly the investment team's judgment. But, you know, when embedded in portfolio intelligence, it really can support portfolio triage, borrower review, watch those discussions, you know, risk committee reporting, and covenant early warning workflows. And the objective is really to help your teams focus their scarce attention, you know, sooner on the things that really matter and understand the drivers faster and create a more repeatable risk narrative across your portfolios. So I think this naturally leads into, you know, an interesting discussion of, you know, what's kind of next for DNA powered in one view and what we're bringing to portfolio intelligence. You know, looking ahead, I think there are a few really exciting opportunities for us to deepen our data and analytics, powered workflows for private credit throughout OneView. The first is, you know, evaluated private credit loan pricing. So this is, again, built off of the unique proprietary all view private credit dataset in partnership with, you know, market, participants and vendors who have had, you know, years and and decades kind of pricing fixed income and and credit assets, but really, really grounded in a specific private credit context and understanding. The second is, you know, predictive credit risk intelligence, adding things like forward looking risk signals, point in time probabilities of default, and other early warning context alongside of the portfolio monitoring workflows here. The third is is really kinda deeper derived analytics and something that we're, you know, spend a lot of time thinking about here at AllView is ways in which we can take our unique data set and and really build kind of net new, innovative new insights and signals based on, the interaction of the, you know, our proprietary dataset with client data, how we can, you know, unlock greater insights for you across your portfolios. And fourth is, you know, broader access to not only your own data but also to, you know, the all view proprietary dataset through Genesys workflows and and APIs. I think MCP or Nextiva Intelligence is an important first step, but, you know, certainly, we see a significant number of opportunities for, you know, MCP type connectors to, you know, certainly your own data and then even to specific use cases as well. I think the, you know, roadmap principle as we look at portfolio intelligence and the context of kind of harmonizing client data, all new data, and AI powered functionality is, you know, really a a consistent theme across kind of key, four key areas, connecting new data, new partners, and new signals, and then activating them through analytics, through API, through AI and MCP. Those are the areas we are really actively pursuing and, you know, we welcome any and all feedback as, you know, helping us prioritize, you know, the next next areas of focus here is is tremendously valuable for us. So I think just to bring it home, you know, portfolio intelligence really creates the connected monitoring workspace and workflows. While deal analytics adds that private market context, AI enabled access through NTP and new risk signals such as the all due risk score. With that, I think it'd be helpful to bring it life a little to life a little bit by transitioning to a demo for a few minutes here. Great. Thank you, Nate. So on my screen here, you can see portfolio intelligence within OneView, which is our new AI native layer for private markets. This is where users will land within Portfolio Intelligence, and they can immediately see a breakdown of key metrics related to their portfolio alongside a dashboard that brings together corporate key portfolio information, portfolio company data information from, your individual deals and those the issues related to those deals from revenue to leverage and interest coverage ratios, compared with the AllView data and analytics benchmarks within that dashboard. They used to have the ability to select the data they want to see and to dynamically recalculate the KPIs on the fly depending on the data that is of interest to the user. From here, this is where our clients, can then start to extract insights from that information using Andy, our in house, agent that allows our clients to query that dataset. We've preloaded Andy with a number of recommended prompts. And to show you that today, you have, four key categories that allow our clients to hone in on risk signals or portfolio health to be able to spend the most time on the deals that need the most attention. So if we try to select within Andy, analyzed financial anomalies, this will return all of the credits that meet certain thresholds around leverage ratio or interest coverage, and will then give the ability for the user to dive directly into the credit information for that individual ordeal, by quickly extracting the insight on that information. Here, you all can also track covenant health. So, for example, showing covenants with less than 20% headroom, compare credits versus their peer cohorts and the data and analytics benchmark that's associated with that deal and perform quantitative analysis across the whole portfolio. So summarize commentary across all flat credits or for the last two reporting dates across the portfolio that you're tracking so you can easily get a one shot answer to what is the latest performance of companies, what is the current commentary, and what are the the risks or outlook for that business based on what your team members have entered into the solution. So from the overview page where our clients are able to view their overall portfolio and all of the deals that they're monitoring, taken from information ingested from DocIQ and from the deal life cycle. They can extract, insights and information to allow them to get quick one shot overview on their investments or identify investments they need to dive deeper into. So as a next natural step, the team members typically may be a deal team member, will then wanna dive deeper into the information during their portfolio review process. So I'm able to easy search on the overview page for a particular name, and I'm gonna open up the, the name, and I'm gonna be able to dive deeper into the data that's present for that particular deal. So here again, you'll see the same KPIs at the top, an AI driven borrower snapshot, details of the deal which are used as context for the AI agent, a financial analysis panel that shows you a quick delta between ACLs to latest LTM information as well as deltas between period to period, as well as a complete exportable financial history, with quick filters that will allow you to find the information that you need as quickly as possible. We also have a commentary review section, and then we have two d sections that, start to combine in detail the your own data, own portfolio company data to deals and analytics. So we have a pit pit benchmark snapshot that allows you to select the relevant benchmark to compare the key deal data points against that benchmark. And we also have a covenant five summary where you can quickly see whether a individual deal is within compliance, what the thresholds are, and then comparison against the median numbers as well as the breach rates within the sample within the benchmark sample. Now let's just dig a little bit deeper into the AI capabilities that we've combined with this functionality. So at the top, you can see a Boris snapshot that gives a very short AI summary on the current deal and its health. As on the credits overview page, you also have the ability to open Andy and ask questions with the same four key criteria, but with an expanded number of prompts out of the box. So, for example, being able to show revenue and EBITDA trends or to show leverage and interest coverage trends. As well as that, we deepen the individual credits comparison with the benchmark data. So for example, Covenant Health versus peers. We've also added the ability for our clients to directly get AI insights from individual components of the solution, so individual parts. So for example, for financial analysis, I if I want to expand the information from that, I can use AI to explain the variances in the data. And that will then provide me a current RTM versus Actuos and highlight any variant signals around risk, right, that beyond or below a certain threshold in terms of business performance. So one example here is adjusted EBITDA versus free cash flow, which is showing that there's a delta between what the, you know, private market standard measure of earnings is versus the real cash generation of the business, which is a core concern of any lender in the market. We also have the ability now to provide our clients with the ability to use all of the context within the credit history, the commentary, and the financials, and covenants to generate commentary during the review process. Right? And we don't see this replacing the judgment of the individual analysts. But what this does do is that for any of the routine tasks around marching a deal and generating commentary, A analyst can use this tool to generate the first draft from clean, consistent entity linked data that is extremely reliable from a consistent set of context and then generate that first pass of commentary, potentially edit it. Now if the analysts need to dive deeper and provide more color or there's actionable steps that need to be taken alongside that commentary around deal health or opportunities with further opportunities with that deal, then the analyst can use that starting point to add more color, market context, or critical action points that need to be made by their stakeholders or portfolio managers. I am now going to hand over to Nate who's gonna take us on a deeper dive into the deals analytics capabilities as part of portfolio intelligence. Great. Yeah. Thank you, Humphrey. So by default, Portfolio Intelligence will ship with kind of a a generic standard benchmark that we call the all new benchmark. Really, this is kind of a a North America focused, you know, kind of lower middle market, filter on the private credit dataset that we have here at AllView. But I think the important thing to note here is that we're giving the maximum flexibility to users to, you know, set up and maintain their own benchmarks that are relevant for them, whether that's, you know, distinct borrower level benchmarks, you know, kind of granularly filtered down, to be relevant to that particular investment or, you know, let's say you have kind of two or three, you know, key industries that that you're primarily focused on as a as a direct lender. You know, you can establish kind of a benchmark per industry and then kind of flexibly apply those benchmarks to, you know, kind of future investments that you enter into the system. And, you know, really, I think the high level here is that whichever way it makes the most sense for you to slice and dice this for true comparability and and maximum insights, it's within your power to kind of to use it however you need to. And I think it's important to call out as well that these benchmarks are persisted. So, you know, when you're act interacting with the Andy Insight tool, you know, as somebody was showing you on the the prior page and kind of comparing, particular borrowers' financials to their benchmark, it's still leveraging that customized benchmark that the user set up behind the scenes. And now what we're looking at is is kind of the core deal analytics landing page that really compares a single borrower to that benchmarking cohort that was filtered down on for for that particular borrower. And really comparing across a number of key operating metrics, risk and compliance, I e covenant performance, and then some key deal level details as well and just really showing, users kind of in context, how that particular borrower is performing relative to the market. So you're not looking at kind of isolated or in a vacuum reporting, but you're really having an understanding of how a particular name, a particular investment stacks up against the rest of the market. And I think, you know, there's a number of natural extension points and and kind of evolutions that we see for the deal analytics benchmarking piece. You know, I think we've gotten great feedback already from from some clients. I think, you know, we're we're eager and and willing to, you know, receive any additional feedback as this gets rolled out further and and kind of your team start using this in in action day to day. But with that, I think just a couple of questions that we can we can jump to here. So let me take two of these here. So one, how does deal analytics improve on the comparison or comparative analysis that private credit firms like ours do today? And I think this is a a great question, and, you know, I I think it really it really starts with the fact that comparison through deal analytics is really leveraging kind of actual private credit deal level data, not just public company proxies or generic market averages. And again, the, you know, users can kind of create relevant cohort baseline, you know, kind of highly granular field characteristics so that you're showing only true comparability to to private credit. Another question, we could just touch on here. Can you elaborate on the 100,000 asset securities and what these actually represent? Are they all private credit specific, direct lending only, or other sub asset classes too? So this this kind of Nessus Intelligence credit data, corpus, does include assets beyond just direct lending. You know, there are some they've always indicated loan and kind of other public credit oriented assets and security peer. But, you know, I think important to emphasize that we have tens of thousands of kind of direct private credit loans, borrowers, and think of deal data going back, you know, nearly twenty five years at this point, and, you know, really see that as, you know, a kind of a critical unique asset for us upon which, you know, we're building these these innovative new analytics. Worth worth adding there is that, you know, it's not only kind of credit or loan assets too, but also, you know, that corpus would include, yeah, potentially equity, co investment participation by the lender into a, you know, a credit credit deal. And then one last question I think I can take here. So I can external benchmarks be sourced and integrated into portfolio intelligence? Absolutely. You know, the the vision behind OneView is is really kind of the connection point for your data, all of your data, and AI powered functionality. And that includes kind of document, Doc IQ initiatives that we have, I e automated data scraping from documents as well as kind of, you know, third party data feeds from other market participants and and vendors. Just one last question here. Does it sit within IA or FA? So, you know, actually neither, Jamie. OneView is is really kind of the, you know, the AI native layer native layer that sits across all of our kind of core underlying product modules. And, you know, there are kind of use case by use case ways in which we will kind of deploy packages that are relevant for our client base here. Portfolio intelligence is obviously, you know, kind of highly focused on private credit, and kind of that front in middle office monitoring today. Great. Wonderful. Well, thank you everyone for joining, and, we'll close it out. We'll start by