From KYC to ESG: Why Fund Managers Need A Unified Data Strategy

Fund managers face mounting pressure for transparency across both KYC (Know Your Customer) and sustainability reporting. Yet ESG (Environmental Social Governance), KYC and CDD (Counterparty Due Diligence) disclosures are typically managed through separate processes, teams and systems, which in turn, lead to inefficiency, duplication and data quality challenges that slow down operations and increase risk.

In this session, experts from fund operations, ESG and KYC technology discuss how firms can move toward a centralized, reusable, and governed data foundation that supports regulatory requirements, investor expectations, and operational scalability.

Topics include:

• Data ownership + governance

• LP transparency expectations

• KYC + CDD workflows

• ESG reporting obligations

• AI + automation in fund operations

• The future of integrated data management

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Speakers:

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Daniel Toledano

Managing Director and Global Head of Sustainability

Quilvest Capital Partners

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Fanny Hantute

Head of ESG Consulting

Greenscope

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Anders Meinert Jørgensen

CEO and Founder

Avallone

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Executive Summary

KYC compliance and ESG reporting have long been treated as separate disciplines inside fund management. This webinar makes the case that they are converging and that the fund managers who recognize this earliest will have a significant operational advantage.

Avallone founder + CEO Anders Meinert Jorgensen is joined by Fanny Hantute of Greenscope and Daniel Toledano, Head of Sustainability at Quilvest Capital Partners, a GP who has worked on both the investor relations/KYC side and the ESG side of the business. The result is a candid, practitioner-led discussion covering the questions most compliance, ESG, and operations teams at private equity, private credit, and fund-of-funds managers are already grappling with.

Key takeaways include: Why KYC and ESG data collection are structurally the same problem at different stages of maturity; how the shift from qualitative to quantitative ESG reporting is increasing the burden on GPs and portfolio companies alike; what strong data governance looks like before you invest in a platform; and how AI is beginning to change the economics of due diligence and LP reporting, but only when the underlying data is clean.

For fund managers navigating SFDR, CSRD, and growing LP due diligence requirements, this session offers a practical framework for building a unified data foundation that serves both KYC and ESG obligations.

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Transcript of Webinar Recording

The transcript below has been auto-generated and may contain errors or inaccuracies.

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Anders Meinert Jorgensen: Welcome to this webinar from KYC to ESG. Maybe a hardcore topic for the July month and the summer, but not least an important topic and something I think that has been kind of people are dancing around the topic, but no one is really approaching it. What's actually the difference and how do they converge? So the idea of today is to have a more discussion session with myself, Fanny and Daniel. So we won't be showing slides, we won't be demoing, we won't be doing anything other than discussing. So I hope you can live with that. But I actually think that's going to be a really, really fruitful discussion considering the competences from Fanny and Daniel. And we're kind of gonna, go around three themes. One starting out is, is, why KYC ESG is converging, another theme around what's the reality inside the fund managers. And then a theme around building a unified data structure, data foundation. And I think, I think we kind of, everyone has this feeling that there is a problem. And I think especially LPs investors, they don't have a feeling, they know there's a problem, because they're gonna… they're being approached from several sources about delivering data. And at least me personally, I've never seen a mapping saying, hey, what's actually… what's similarities, what's the same data points, and what's different data points. So that's some of the stuff we're gonna try to dive into today. But, before going down that road, I think we should just hand it over for introductions. So why don't we start with you, Fanny?

Fanny Hantute: Thank you so much, Anders, and nice to meet you all. Thank you for inviting me to this webinar. I'm very happy to be here. So I work at Greenscope. We're a leading ESG solution in Europe. And prior to that, I was working as a consultant in ESG and sustainability. Hence my presence here. I've been in charge at Greenscope of the head of the Customer Delivery practice, for the past two years at Greenscope, and I'm now working on our international and European expansion. So, very happy to be here and to discuss, the future and pending topics on ESG and CAUIS. Thank you.

Anders Meinert Jorgensen: Perfect. Thank you so much, Fanny. And Daniel, over to you.

Daniel Toledano: Sure. Thank you, Anders, and thanks to both Avallone and Greenscope for having me on the webinar. It's great to be here. And so, Daniel Toledano, I'm the head of sustainability at Quilvest Capital Partners. We are a global private equity platform. We manage private equity, private credit, and And fund-to-fund strategies primarily, primarily. across Europe and the US. And before moving into ESG, I spent years on the investor relations and the KYC side of the business at Quilvest, so I genuinely lived on both sides of the data problem, so that we are here to talk about. So, yeah, so happy to discuss how similar the headaches actually are, and looking forward to bringing that GP's view into the conversation.

Anders Meinert Jorgensen: Perfect. Thank you so much, Daniel. And I would say you are absolutely the superstar of the show because you exactly know both sides of the problems. Typically. People don't typically either kind of KYC for life or your ESG for life. So I think this is a really, really crucial part of it. So, and then briefly about myself. Yeah. My name is Anders. I'm founder and CEO of Avallone. Before that, more than 20 years in corporate institutional banking. The last 7 of, on the dark side of banking, I usually refer to it, so Head of Operation Compliance for Nordea out of Stockholm for 3 years, and Head of Compliance for Danske Bank out of Copenhagen. So, seeing so much money laundering and KYC frustrations that it's worth a couple of beers, but I think more importantly, also probably being part of creating the problems that we're not trying to solve, right? A big part of the KYC is coming from the banks and kind of that's where they started in the most highly regulated areas. Anyway, now we're trying to solve it in Avallone where we focus on more enterprise KYC or complex KYC, KYB some would call it. Anyway, that's me. So let's, let's kick off with the first theme, why KYC and ESG and converging. And if we start out on your side, Fanny, kinda like what are you seeing on the ESG side, right now? Where are the requirements coming from? What's happening on that stage at the moment?

Fanny Hantute: Yes, very interesting question. Actually, as we see the data is growing, I think it's a burden for everyone. It's a bit like what you were saying in the introduction. It's coming from all places, CAOIC, ESG. So my answer would be why is the data growing and why is it interesting? And it's the same reason for CAOIC. There are three main reasons. One is that the data is more granular. So ESG went from a handful of narrative themes to hundreds of structured machine readable data points and based on several types of regulations. So SFDR, EU taxonomy, even scope one to three methodologies needed to be detailed. So there are so many more, but this is one A second one is that it's more frequent and there are more angles to collecting the data. So you will have EDCI reporting, but you will have LPs requesting data from you. You will also have SFDR reporting that is well known and that we work on. And all of these increasing the frequency and hence increasing the data growing and last but not least the data is also scored it's not just collected and I think it's the same for how I see risk rating but you will probably need to say a bit more about this Anders later on and therefore it's crucial that this data point is a qualitative one because it will be used For several purposes, for scoring, for benchmarking, for different types of reporting. And so this is why it kind of raises the bar on quality. So to answer your question. ESG is definitely evolving, towards something that is a bit more, con… consistent and, very… And to conclude, I think what we need to bear in mind are also the regulations. So contrary to what we might think, there are a lot of regulations and we have heard that there is a sustainability backlash. There is the Omnibus Directive that came into force in March 2026, and that narrowed the CSRD and therefore there are a lot of companies that are looking at this. that were below the threshold and no longer needed to report on the CSLs. But contrary to what we think, there are still a lot of regulations. And even though the CSRD has been narrowed down, this SFDR 2.0 is now shifting into a more product categorization regime. There's still the CSRD and ESRS for some companies, the EU taxonomy and the EDCI. Therefore, even though some portfolio companies are now below the threshold. It seems that the pressure has moved from the regulators to the investors that will still need to collect the data at some point. So this is my understanding of things, and I'm curious to also hear about you, Anders, on KYC and due diligence. Do you see the same happening, or what do you feel?

Anders Meinert Jorgensen: Yeah, it's, it's funny because, when you, when you, when you use the abbreviation ESG. It's actually a bigger thing, right? And I would typically put KYC under the G, under governance, right? But people don't, it's as if when you use ESG, we're almost like environmental things and maybe social, right? But I think it's a huge umbrella, right? And when I listen to you, it's more or less the same, right? Because I guess it's about transparency. If you really put it up at the highest abstraction level, it's increasing transparency across and between enterprises. So what we are seeing is, complexity increasing. Absolutely. And that's because, it's evolving from it started out KYC starting out being a regulatory discipline, not becoming a risk discipline. Right. And I think that's the typical maturity you will see. When things start out, you start just reading the text of the law, and then you start to understand, and then you start to think about, hey, what's actually the outcome of this? And then you start to adapt, and I think that's where we see KYC going now, that it's becoming really a… a risk discipline. And that's also why in the legislation, less and less you can actually read what you need to collect. It's much more around, hey, what's the outcome you're looking for or we're looking for as a regulator? And then it's up to you to set that process, right? Obviously, as you say, it's been used for risk scoring for a long time. I also think the The two way street is really also a factor now where 10 years ago you would see banks asking, right? And then other regulated firms, funds started to ask, but now you start to see everyone asking each other and everyone performing due diligence on each other. So, so, in investor, oh, sorry, fund down to investment target fund up to investor, investor down to fund sometimes investment target up to I mean, so everyone is kind of like doing it on each other, right? And I think what's driving it is. Less regulation. I think the regulation is pretty mature. I think it's more geopolitical uncertainty. And there we, in KYC, have the sanction screening, right? Which is the big no-no, right? So that's why, hey, we don't want to deal with any people that are sanctioned. Obviously, that's the worst case. So we need to understand who they are. But I see also, and maybe that, that, that could also be interesting to hear you on, Daniel, but I see also more and more non-regulated firms. actually starting to act as regulated firms, right? So again, it's becoming more of a risk discipline than a legal discipline. But hey, let's move to the practitioner, Daniel. What are you seeing? You worked on both sides. What are you seeing in reality out there in the market?

Daniel Toledano: Having sat on both sides, yeah, I think the underlying problem for me is quite identical. I mean, you basically, you are doing the same process. You want to collect, you want to verify, you want to audit the data on the different entities and counterparties, and you want to have a clear audit trail behind all these data points. For me, when I came into ESG, I think the difference I saw was maturity, not really the nature of the two processes. I think, as you said, and KYC has had, I don't know. Roughly speaking, 20 years to standardize the standards, the formats, the checklists, the processes. I think ESG, we are not in the infant phase anymore, but we are still building those reference frameworks. And that's why it feels a bit messier today than KYC. And and, as you say, Fanny, I think the extra layer of complexity that Kyc didn't really have to deal with in the is the framework segmentation fragmentation between Europe and the in the Us. So in Europe. Some could say we are very mature. You have SFDR, you have CSRD, you have the EU taxonomy. They are all moving at different speeds and different scope, but they are there. In the US, the approach is quite lighter touch, even though that I mean, it's shifted back and forth, depending on the political cycle. And on top of that, you have different, I would say, angles as well, depending on the strategies. For example, you don't collect the same type of data for a buyout fund, than for a credit fund or, or for a funder fund, even under the same regulation. I think, as you were hinting at. I think the encouraging part that we are seeing some convergence things like Edci Csrd hopefully, that are going to push corporates toward a more common taxonomy. But I mean, honestly, I think today it's still fairly chaotic. We are still asking portfolio companies. kind of overlapping, but not identical frameworks, and that's exactly the kind of fragmentation, I would say, that KYC has solved, or tried to solve, years ago. So I don't know if it feels the same on your end as on the platform side, Fanny, but yeah, it's still a bit fragmented today.

Fanny Hantute: Yeah, it's true. I mean, so at Greenscope, we're implementing a lot of AI to facilitate the fact that portfolio companies would not have to fill in the same data point for different purposes. But it's true that we have seen, and it's also the customer to purchase, we see that the real complexity is also portfolio companies needing to fill in data, the same information over and over for different kinds And it loops back to what Anders was saying about the multiplicity of stakeholders, what you're saying, Daniel, the multiplicity of frameworks, even though some things are converging. So I guess my question to you, Daniel, would be. like, who owns… who is responsible for the data? Who owns the problem? Like, what is going to take more time on your side? What… what's the… what's the real ESG burden increasing, and what impacts does it have for you?

Daniel Toledano: And. I mean, who haunts the data? That's I think that's 1 of the key issue you want to solve at the beginning. And that's why it's so much messier when you don't have, when you don't really have tackled that at the beginning, and and and why it's getting so quite messy. So yeah, it's a matter of governance. It's a it's an issue as well of of making sure that you have processes in place. Because, what happens frequently, I think, at the beginning is that, like 5% are on the same data, but not the same version. They don't have the same audit trail. And so you end up having to reconcile it. the same data, and that ends up costing you hours and headaches, and hopefully you have to circle back to LPs or to the regulator, which is not always a good thing to do. And so that's why I would say trying to solve that issue of having a unified process, unified data lake, unified. Yeah, the platform of data helps solve all this mess. And we've seen that in Kyc. I think we are definitely getting to see that in Esg as well, and definitely, if you compare between before we had the platform versus after we had the platform. You are. You still have issues for sure, but it's not the same. And you have. You are not in the same category of mess.

Fanny Hantute: Hmm. Interesting.

Anders Meinert Jorgensen: Hey, I was just thinking, and this might be a naive question. What's the role of fund admins here? Because I think. We've seen in KYC the fund admins sitting pretty hard on KYC data, but not in a necessarily very flexible way. So, so I think it's something, something funds are learning now is that, hey, we actually need to own, now we're talking data ownership, right, and governance, we need to own that data ourselves, because otherwise, regulators have started to ask, hey guys, do you even understand what you're collecting? Can you access it? And if you can't access it, and you have to wait 3 days to get it from the fund admin. Are you in control? And now I'm asking because I really don't know what's happening on ESG. Is there a fund admin move there or is that happening at all?

Daniel Toledano: I mean on my end. I haven't seen that. But I'm super interested to have your opinion on this. It's true on the Kyc side. It was quite I would say tangential to what they have been doing in the past. So it was maybe natural for them to propose, a delegation of, of the activities to, to. I mean, two GPs, two GPs. So I think it's very common today to see a fund admin who has a dedicated team who are owning the operational side of KYC, even though you still have a compliance team of GPs who oversee that. On the Phd side. I haven't seen that. Yes, yet. Typically. they would check the consistency of information when you are doing reporting yearly. So to make sure that what you are reporting on Esg doesn't contradict what could be in other parts of the financial statement annually. but funneling, getting into operationally managing the data part of Ehd. I haven't seen that but maybe maybe on your end, Fanny or Anders, it's something that you have seen.

Fanny Hantute: So you haven't, you haven't seen it, but you, who, who then will be accountable for the data? You think it's the ESG? Lead of the fund, then? Not the… not the investment team, except for maybe funds that have both financial heads that are also in charge of sustainability, for example, like, very, like, earlier stage funds.

Anders Meinert Jorgensen: Mmhm Makes sense. Yeah, dual head, yeah. But I would say I'm not seeing it either. And actually, I think I'm seeing a little bit of the opposite move. And that's more related to KYC because we see now that. Originally, you would do KYC on your investors, right? Now, as the regulator's expanding, and the risk understanding's expanding, they're focusing more and more on the downstream of funds, so focusing on the investment targets, right? And typically, fund administrators are shying away from this. They're like, hey, we don't want to do this, because it's harder to find a regulatory standard operating procedure to follow, right? Because a target can be anything. Like for you guys, Daniel, fund of fund, what's actually… So should we then perform KYC on the fund underneath, and then also on the investments underneath that fund? No, probably not. So how far do you go in the chain? What's the difference when you are a PE fund versus an infrastructure fund? And so the assets are so different, right? So that's why I think the fund admin is like, hey, this is becoming too… weird and too messy, right? And I could foresee the same with ESG, because ESG is just… such a big thing and less mature. So it's harder to operationalize and standardize, I think.

Fanny Hantute: Yes.

Daniel Toledano: I maybe won't, but so far. I haven't seen the Compliance team at GP either. Or at an admin asking for Es data on events on investees when you enter into a transaction. Though that may be the case in the future, because, as you are saying, both are about risk management, so it wouldn't shock me that you would get… you would ask this kind of data from the investors. side. So far I haven't really seen that. But maybe they are just relying on off the shelf information that GPs already have. For example, like Esd due diligence. But I am not sure, because I think otherwise I would have been informed. But yeah, no, it's interesting.

Anders Meinert Jorgensen: And now we're talking, kind of. Data points and what's the same and what's not the same. What would you say that you collect in ESG that you do not collect in KYC and vice versa? I mean, I'm just trying to approach the question because that can also help us identify all the things that are the same. But is there something where you say this is completely two different things? In the collection part.

Daniel Toledano: Yeah, I think… I think the shift on Esd has been done. Initially, it was more about qualitative data. Oh.

Daniel Toledano: like describe your ESG approach, your ESG policy. Today, because GPs have matured, and then the framework on reporting is also, even if it's in Messier, I mean, there are certain areas where you know that you are going to To have the quantitative data. There's been a shift between from quant qualitative data to quantitative data. People ask, specific metrics, comparable figures. And so I I think that. The cost is a bit the same as on Kyc, because you try to reformat the same exact data, the same exact underlying data for different audiences. Once it's for an Lp report, when it's for a bank due diligence questionnaire, when you want it for a co-investor or for a regulator. It's so. It's frustrating because it's genuinely the same data every time. But the packaging is different. And so you spend so much time on trying to map the initial data to the different similar requests, which I think is a bit less the case on KYC, because those frameworks are a bit more, are a bit more standard. And so. So you spend less time. But I think that today you still have both quantitative and quantitative data, which was not the case a few years ago on ESG.

Anders Meinert Jorgensen: Mmhm Yeah, and I'm thinking… I'm thinking in KYC, the special thing, I guess, is ownership data. Or is it? Do you collect them? No, you don't collect that in ESC, right? So I think that's this thing of trying to understand you have all of these, especially in funds and in the whole investment world, you have all of these complex structures that are designed for different purposes, and… but you're kind of, all the way, kind of trying to understand all the way up to finding some flesh and bone, right? I need a… I need a physical person That's what you're looking for. So it's just kind of a Sherlock Holmes kind of mystery finding or not necessarily mystery. Right. But you're trying to determine who the owner is, right?

Daniel Toledano: I was. I was more thinking about the nature of data. But yeah, you're definitely right on Kyc. At some point, you end up having to ask for very personal data about physical persons. Whereas on ESG, I think most of the data is quite transparent. So you end up on KYC having to manage all these issues related to Sometimes the intermediary doesn't want to give you that data at the top of the pyramid, as you were saying. But you need that. So you have to kind of find ways to manage the risk without managing the identity of those persons, and that I mean, that's time consuming as well, for sure.

Anders Meinert Jorgensen: Fantastic. So, hey, Daniel, you've tried to come from, like, on the ESG side, coming from Excel sheets and the very start of it into a more, like, structured approach. Could you try and share with us and the audience kinda like, how did that journey look like for you? That maturity journey.

Daniel Toledano: Sure, I mean, before we had the platform, for sure, like everyone else, we managed data on spreadsheets, and that was the issue I was mentioning a bit before, multiple versions floating around. No real traceability. Dependency on a few people. We were the only ones to know where the real, actual version of the right data was. And… I mean, the good thing is that we only did that for a few years or a few months. So it was not about. We didn't have that legacy, that data lake of data to to be transferred, which I think can be a very, very big issue. If you've been doing that. 4 years, and so the turning point was a very deliberate choice, because we We wanted to have a proper platform, not from day one, but let's say day two. rather than accumulating years of technical knowledge. I don't know if technical debt or data debt and paying for that later. So So that and we knew the pitfalls from Kyc. So we knew that that would unlock traceability version control which matters a lot. As soon as you want to compare data from a year end to year n minus one when an Lp. A bank or regulator Don't just ask you to state something, but also to prove some claims, which makes it a lot easier. So, I mean, direct experience of having that audit trail. turn out what made the big difference between the work pre platform to the work post platform and what could turn into like a how long or days longer chase into a 5 min lookup on the platform. Basically.

Anders Meinert Jorgensen: Yeah, in the end, quality and efficiency, right? Yeah.

Daniel Toledano: Yep.

Anders Meinert Jorgensen: Okay. Let's move on to the next theme. So, or the 3rd theme, actually, which we call building a unified data foundation. And, Fanny, why don't we start with you kind of like looking into we talked a little bit about data governance, but maybe doing a little bit more deep dive on that. You know, how strong ESG data governance looks like in practice and maybe also. What separates well-performing firms versus less well-performing firms in your mind?

Fanny Hantute: Yes, thank you. And actually, just to also jump back on what Daniel was saying, I think it's a very good transition. Daniel very modestly is saying that the platform has helped structure the data process and Daniel is actually a customer of Greenscope. But a key component and a prerequisite, I would say, is also the data maturity and the data governance, as you're asking me, Anders. And so actually, as you were saying, Daniel, you built on your past experience on CAIC to take these good practices and put them in place for your ESG reporting. And what we see is that actually before talking about an ESG solution or having an ESG solution. What's important is to have a strong governance. and the ESG solution will not solve all of your data management problems. So, what you… there are basically, like, 4 questions that you need to ask yourselves, and I will describe them. You could say the who, where, how and why basically. So let me just explain what's behind all of these questions. Who is the single ownership. So who has the data? That's what we were saying before. Each data point needs to have one owner and be in one system record. So this is sort of a data management good practice, if I may say. Where is… where is the data? The data is entered once, but it can be used in a lot of frameworks for many different uses, so that's something that we need to bear in mind as well. And then how is more like how is the data? What is the quality of the data? Is it consistent? Is it plausible? Has it been verified? Are there some accidents? Or errors that we need to correct and that we can spot very easily. And this is facilitated with a platform, but it's important to also define what a qualitative data point is for the fund manager. And the why or the what maybe is where can you trace the data? What is the proof of the data point? So you need to define your audit trail, which is precisely what Daniel was saying, and to know how you can trace back to this data point. So, what separates the firms that, have a good ESG reporting and, and those that struggle with it is basically also this governance and being able to, put technology above a clear governance and data lake. Otherwise, it could also complexify your data management process. And what we do at Greenscope, which is also interesting, is that, we don't only bring technology to our customers, but we also bring a diverse squad of operational data scientists and ESG experts so that the customers that approach us with different problematic being ESG reporting, but also governance problematic, because they need to find the… to answer these questions. Then we are able to support them, and to facilitate the fact that they will integrate the platform, all while having done this prerequisite work.

Anders Meinert Jorgensen: Amazing. Thank you. And… It's funny, this with the… Yeah, sorry, I think…

Fanny Hantute: Yeah, no, no, I'm curious to hear about you. So, can I see the same,

Anders Meinert Jorgensen: Yeah, no.

Fanny Hantute: Sure.

Anders Meinert Jorgensen: No, I was just smiling because when you described also that you deliver more than a platform, you also deliver expertise. Right now on LinkedIn, it's all over the place that also Palantir is talking about data discovery kind of thing for deployed engineers, right? But I think well performing software providers have been doing deployed engineering for many years. It's a matter of selling more than the software, of course, yeah. No, I think on KYC, we see the same, and especially this talk about data ownership and data governance is coming up. And I think the, the bigger firms are starting to have, like, like one, one source of truth, one, one, like pure data lake that they didn't find somewhere in the backbone. So I think The mature firms are starting to focus more on what the tech stack looks like. And then instead of maybe having the data in different systems, they like it. they have it in one system or one platform they own themselves, which is then a data lake, and then they hook up with APIs, right? So that you can actually start to reuse data points. But it requires a little bit more, obviously, right? And it's not necessarily the place to start. But then I think also. coming back to, like, your questions, like, what are you actually asking? We're starting to see more and more firms We've been asking these 55 questions for the last 5 years, right, or 10 years, right? But actually starting to kind of do a revisit and say, hey, does it even make sense? And could we start to unify some processes in one questionnaire or in one process? Which, of course, then requires collaboration internally, because then you have to reach out, hey, the guys in the tax team, you always ask 9 questions, and you, by the way, have 3 forms, and the guys in the ESG team, and then you have the KYC team, and… and doing that collaboration internally is surprisingly hard, it seems like. Which, again, I think is just, a signal that the governance is not really clear, right? And that you have maybe areas that are moving in non-parallel ways in terms of maturity and what they need to find. But I think… But I think the good, the good news is that, also to Daniel's point from before, that you've seen ESG moving from a very qualitative through a much more data-driven discipline, right? I think that's also why in the beginning, she saw the head of ESG, they typically came from marketing, which is like, okay, which can be great, by the way, no offense to marketing people, but you see these trends that typically develop when you then say, okay. ESG is just not just a communication thing. It's not just a marketing thing. It's actually real stuff that needs to happen underneath. Right. And so I think that it's more and more that the people on the team that's driving these things. Hey, Daniel, if you, now you've been on the journey, you've been on both sides. you tried to set up a platform on the ESG side, and probably also on the KYC side, but if you would be starting a fund from scratch, or advising someone, I don't know if you would have the energy for the first one, but anyway, if you would be starting or advising someone that was starting out a fund from scratch. Like, what would you do there? I mean, this is not necessarily the right thing just to jump on a platform day one, right? But what's your think about that?

Daniel Toledano: Yeah. No, good question. I think the real first step before, as you were saying, talking about platforms, I think the real first step is defining the need. obviously understanding the frameworks that you see right now. Sfdr, Csrd, Dci, all the more local requirements. And. But also mapping the request that you have… you are currently receiving, That's how we did. So basically, we look at all the RFPs or DDQs from LPs, from banks, from co-investors or regulators, and try to understand where they overlap. to understand. Yeah, all the duplication. Also talking about the peers, talking with the peers, spend some time talking with, other GPs, also talking with the platform providers because they, I mean, they work with dozens of firms, and so normally they, I mean, they genuinely understand, you know, how this frameworks. interact in practice. And and and I mean, you don't have to reinvent the wheel. Basically, when you do that. So I think, yeah, my, my, the 1st piece of advice would be trying to map the need and understand the need. And then I think I would go to them to the process that that that you and Fanny mentioned like governance. source of truth, ownership. Maybe the last, the last piece of advice would be like from day one, try to design for reusability, for reusability, because you will have To, to collect. and share data that you have collected once for an LP report for Ddq. For a filing for regulators. And so you need to account for that from the beginning. Basically. I think that answers your question.

Anders Meinert Jorgensen: No, no, I think it does. And well, it's easier said than done, of course, right? But no, no, no, it's super good. And also, you know, when you sit in a fund, you are probably bombarded daily with technology providers trying to sell you amazing things, and everything will be easier, and… But have you also any advice on that selection part? I mean, what are you wary of when you select technology providers, and what are you looking out for? Is it maturity, length in the game, agility, innovation? What should one be careful of?

Daniel Toledano: Yeah, I mean, I think the pitfall is related to what I was mentioning. I mean, have you seen already, like, firms buying KYC technology? before they agreed internally. You know who owns the data and what data do they need? And I think that that leads to a nightmare of an implementation project, because you end up like mid-project and really understanding what you are going to need. And so you have to. I mean, basically, you lose months. That never ends well. So. So yeah, understanding the needs for sure. Yep. I know that I knew. I remember that for us one of the things that we wanted was, I mean the main thing that you're trying to do on Esg or in Kyc is obviously risk management. But and so basically, it's how we can downside the risk for you and your investor and your investments, and how we can create value. And I know that on the Esg. You start. You are starting to get benchmarking data. And for us, it was very important at the time to be able to tell our investments, our portfolio companies, look, this is your data, and this is what your peers are doing and where they are. And so we wanted to have a provider that could help us, you know, understanding that competitive landscape and give us this benchmark. And I know that at the time, it was one of the things that made us choose Gainscope when we became a client. So understanding the need is very important. Obviously, it's a technological tool. So don't work in a silo. We are a very small team in GPS. But you Now you are working. You typically also have an IT department. So you need to put them into the loop. To make sure that these work well with the other, technological, tools. at your firm, and then it's about trying to do it themselves. And I think the 4th part, which is equally important for me, is the fit. With the chimps, you are going to start to work very closely with your platform providers, year after year. So it's important that you, you, you can feel that you're going to have this kind of good relationship. Because there's going to be, like, very good moments, but there are also always going to be, like, some moments where they are a bit more tense. You need to make sure that you have a good relationship.

Anders Meinert Jorgensen: Yes.

Fanny Hantute: And we're happy to have a good day.

Daniel Toledano: And I hope you can see that.

Anders Meinert Jorgensen: Exactly. That's good to hear. Yeah, exactly. Exactly.

Fanny Hantute: But I completely agree with Daniel, maybe Anders, if I…

Anders Meinert Jorgensen: Please.

Fanny Hantute: I can join, to your point, Daniel, I think that it's not tech failures, but government failures with a software logo. So, once again, completely agree with you, it's about defining the data ownership, it's about Doing all of these things that we mentioned before and having the right governance. And so you can expect an ESG solution to fix your problems, but of course it's important to also ask for help if you need to structure your data before starting. And I think a good way to start, and what we see with some of the customers that approach us and that are happy to start their ESG reporting, but don't necessarily know where to start. Is to start with maybe just with one questionnaire, one framework. and one campaign to start collecting data before you scale and see how it works, see how you can start benchmarking this data, as Daniel was saying, see how you can define a score on this specific framework and data that you collected, and then And then drive from that. And I think the pitfall would be to have several data lakes. and not rely on a clear good foundation before scaling. So this is what we see.

Daniel Toledano: Yeah, no, good advice. Starting small is always good advice, I would say.

Fanny Hantute: What about you?

Anders Meinert Jorgensen: Sure, yeah.

Fanny Hantute: And at Avallone, is this something you see as well?

Anders Meinert Jorgensen: Yeah, no, absolutely. And also now I try personally to be both in very large organizations and then smaller ones, right? And we have some of the largest funds and corporate clients across the world, right? And it's always the same that… that people… that people want to go from 0 to 100 in the first step, right? So they plan and plan and plan and plan and plan, and then nothing is happening, right? So… so typically, it's also… And I guess that's the agile approach. Like, okay, just get started. Right? And and even if getting started is, hey, why don't we structure the data a little bit better in an Excel sheet? And then we start to work from there because to Daniel points, then once you get to buying software, you're actually much more ready and you understand what you actually need. Right? So I think that that's the more successful partnerships we see. And sometimes we come out to customers where it's like. super, super… I mean, hyper-structured, right? But everything is running very manually, and they have an army of people doing it, and I'm always amazed about that, because it requires a certain brain to set that up, right? That everything is just running, but it's running in, like, intrinsic SOPs and all of that, right? So it's always a pleasure there, as long as you are not too fixated on trying to replicate that process you have into a platform. Because once you go out and buy a cloud or software as a service, you have to kind of cut a toe or a heel. That's maybe a Danish expression, but you know what I mean. You have to be flexible and say, okay. this process is maybe a little bit specialized for us, we're gonna skip that, but we take the standardized process more and more, right? And that's also where you get economies of scale, because you're then many funds doing more or less the same, right? But hey, let's… let's, move on to the last segment, and then… and then into any potential Q&A. We… We typically try and close these webinars, like, 5 minutes before, because then people also have a, hey, great 5 minutes in… in the present. But… The last one is about what's coming. So kind of like, what's the near term reality? And I think. That's extremely interesting in the… I'm going to sound like a record player, but in the age of AI, right? Because it's just crazy, the development we've seen of AI. So, and not necessarily only talking about AI, but kind of like, where is it heading over the next couple of years? And if I may start so myself, So… Just, and I think, I guess you all know that, but like our engineering team, they stopped writing code at Christmas, right? So that's less than six months ago. No one is writing code, right? And that's how fast the models have been developing, right? And even though we started implementing AI in the platform. A year and a half, two years ago. It's… it's just… it's just at a crazy scale what's happening now, right? So I think also the question becomes really difficult, because it's… it's easy to be naive, but it's also easy to overestimate The development because maybe we are in a bit of a, in a bit of a bubble, but it doesn't feel like a bubble though when we see some of the concrete results. So I, I think. I think, actually, that standardization is less of a problem going forward for KYC, because even if it's not standardized, you will have engines that can just heater for that complexity, right? Complexity becomes less of a problem. And I think, so that's one thing. I think it doesn't matter whether we standardize or not on the KYC data points. The second part is that I think we'll see more quality. Because the annoying tasks that are taking time away from senior KYC analysts, they will be done by AI. And then you will see more of these that actually require thinking, more research, more like, hey, who's that guy? Let's understand this will go up. I think that's just my take on it. But, Fanny, what from the other system provider, what's what's your take on in the future?

Fanny Hantute: But it's very interesting because just before, to your point, when you were talking about this, I was… I could see that KYC is maybe more mature than ESG, or more present, and so there is this tendency to customize. So, according to you, actually, even though we customize, this is not an issue. Like, AI will grasp all of this data and make it standardized. But then, of course, the question is how do you make sure the interpretability is the right one? How do you make sure you trace back to the right data point and you don't hallucinate? So I think these are things that we see at Greenscope while deploying our own AI as we try to develop models that were, for example, recently we've developed a due diligence model. that can scrap external databases and internal databases and take into account the methodology of the investment manager. So you enter the methodology, you ask how you want to perform your due diligence process and then you get an IC memo all the while scrapping as many databases as you can in addition to what the vendor might have given you on this specific due diligence. And here our main objective, at least the objective of our developers, is to try to really prepare the databases so that when we look into Grinscope's database to first analyze and screen the asset that is being due diligenced, it goes and it doesn't make any error in looking for the right data point. And this is done with AI by structuring your database. So, we're talking a lot about AI, but data management and databases are as important as AI, and AI cannot work on data that is not clean. And so it's… Even though people can work and prepare their own AI on their side, it's never going to be the same as if you're joining a SaaS, for example, a solution that already has an extensive data lake and confidential information on this specific asset. that they can share, and that will bring you additional information on this asset. So I think this is really where we're trying to go. And, yes, and I think AI is a great opportunity also to… to compare, to benchmark data, to have more context on a data point. We were talking about reviewing the data and being and making this and having people who are accountable for the data. This will be, I think, the second biggest challenge of AI is, how do you review the data that has been maybe read by an AI that has, the AI might have shown a lot of inconsistencies on the data point, but how do you train your teams to be able to identify when the AI is not showing the right type of inconsistency and how it has done that, how it has led to this result. So I think Yeah, I would say that scrapping a lot of scrapping information and having more context will be a great opportunity. And our capacity to verify and to understand how the AI got there would also be like a key skill for tomorrow.

Anders Meinert Jorgensen: Fantastic. Thanks, V Daniel, what's on your mind? Either future and AI, or maybe that's both the same. I don't know.

Daniel Toledano: Well, it's one of the big topics right now, I mean, not only on EEG, of course, I think you mentioned a lot of the things that were on my mind. Maybe I could also mention the fact that AI hopefully will allow us to interact with data in a much more natural way. You know, asking questions directly, manipulating and cross referencing rather than manually. digging through spreadsheets, and every time you need an answer. And I think we are almost there. So I can definitely see this coming, and it will help. reduce, I would say, the workload load on what you are doing with the data post annual survey.

Fanny Hantute: Yeah, I completely agree. And I think maybe, I don't know if you all agree on that, but maybe AI will also change. And maybe, Andrew, you have a take on this as well. The way our platforms are given or shared to our customers tomorrow, SaaS solutions could also be like the backbone infrastructure and each fund manager might just ask an agent for the data. And aggregates all of the different platforms from which the data.

Fanny Hantute: And so the future looks very different as well. It's not just going and calling different databases in one solution, but it could also be, you know, making all of these solutions consistent and just using the same database.

Anders Meinert Jorgensen: Money.

Fanny Hantute: to do that.

Anders Meinert Jorgensen: But I think, yeah, but there's so much in this question that that would be hard to cover in the next few minutes. But I think because for us, when we look at public data and also what you said about scraping Fannie, which I think is very natural in the ESG world, in the KYC world. At least in Avallone, we're much more bearish on public data, unless you have public data from a public register source, right? But the reason I say this is it ties into AI. You know, you have this feeling when you have AI Claude or OpenAI to formulate text. It's so confident and that tricks you a little bit like, wow, this is really good. It's really confident. But if the data sources it's pulling from is not accurate, it's just hallucinating because it wants to give an answer, right? So I think that's kind of the trick in it. So the underlying data, also to your point, Fen, it becomes extremely important because if you can give it. really high quality underlying data with segregation that's like, hey, you only look at this data. you avoid the hallucination to a big degree. And then you need to train the models that, hey, if you don't have the answer, please don't answer, right, at a certain interval, right, of probability. So… So I think more. I think more that you will see, you will see firms that are these verticals that are niche. They will create platforms that use multiple models, really successful. Because right now we're just talking about the big models, but when you start to look. At the big models, there is stuff they do really poorly. And I have an example from our side. So part of the Avallone platform is that you can inject any PDF, any KYC questionnaire, a Word document, and it digitizes it. But digitization of a PDF, that's an art form. we learned, that… that in order to get quality, you actually have to use multiple models, also smaller open-source models. So I think right now we just focus on the big models. I think there will be a myriad of models, which is good because it's going to drive the token cost down, which is also good for equality in the world, that it's not just a few men in the US earning all the money. So I think it'll be kind of an art form to have the right data and combine the right data with the right combination of models. So that's why you will see more and more, I think, these orchestration layers that are between the underlying models and the outcome you're achieving, and then they're pinging the models in the right way and in the right sequence. That's at least what we're seeing right now, gives really good results. But… You know, taking time back a year ago, I would never have imagined we are where we are. And it opens up so many questions, that that that it's that it's hard to predict. But I kind of like that sometimes I get scared as well on behalf of my children, but I kinda like from a technology and outcome point of view, I like the direction right now of this in the enterprise space. Any last comments? We are closing in on the hour. There's one question, which I think we already answered, about data governance. So we talked a lot about that, but let me try and rephrase that, and then ask the two of you. If you should point to one person that should own the data in the fund. Who should that be? I mean, not by name, but by title, obviously. Who would be the best person to own data? Across KYC ESG.

Daniel Toledano: It's… I think… I think, ideally. And maybe we will converge to that. I mean. And that will only come when we stop treating this as a reporting burden. But when we start treating all this data as an asset supporting value creation, it will sit under the investment team or the value creation team. because that's what it is in the end. No, it's risk management. It's potentially value creation. It's not there for now, but I think ultimately that could be That could be the answer.

Anders Meinert Jorgensen: Amazing. What do you think, Fanny?

Fanny Hantute: Well, I agree with Daniel, because it's becoming a strategic asset, but maybe just for the sake of the debate, I will say that I think maybe the ownership is the one that produces the data. So, for example, at Grinscope, the way we design the platform is by having a real ownership of the portfolio company. The real, the underlying asset, the person answering the data point is the owner in the end. But then, of course, the ESG manager, and then tomorrow, the operating partner, and then maybe tomorrow, the head of the fund, because it's become so strategic, is gonna look at that data point, but for me, the owner is the one that produces the data.

Anders Meinert Jorgensen: Yeah, good point.

Fanny Hantute: Yeah, it's a bit, just for the sake of the debate.

Daniel Toledano: We have French, we like to do that.

Anders Meinert Jorgensen: Yeah, you like the debate, I know.

Anders Meinert Jorgensen: I, I think you need to go further back kind of in the, in the stack. I think there needs to be some sort of data responsible for it. And I know that's been done with limited success in the past, I think. But I think if you find the right person down there around the IT architecture, now we're talking bigger firms and bigger funds, right? But I think some sort of head of data governance or something like that, because then that's the person who can drive through and say, hey, listen, guys, in ESG, I know you have all of these theoretical opinions, but this is how we're going to do it. And the same to the KYC guys that like thinking they're solving world peace by collecting this, like take it down a notch, guys, this is the same data point. You can reuse it, right?

Fanny Hantute: Like having a DPO or the data product.

Anders Meinert Jorgensen: Yeah, yeah, and then even the deforestation.

Anders Meinert Jorgensen: Yeah, exactly. It's more like not so much the protection of the data subject, the one delivering the data, but more like internally, like how do we get efficiency, right? Okay, we could clearly continue for hours. Fanny, Daniel, thank you so much for joining. It's been a true pleasure. Also for the participants joining on the webinar and for those listening in on the recording, you're obviously welcome to reach out with any questions or comments to any of us. So if we don't speak before, have a fantastic summer and thanks for joining today.