Why Tokenized Assets Need On-Chain Risk Ratings

of okay, now I'm tokenizing the product, meaning that the characteristics of this underlying asset is changing because I'm introducing a technical risk layer conducting it in a way that the rating for token is always individual per network where the token is being admitted because every network has a different set of risk characteristics. Why are we looking at it? Because from an institutional perspective, there's like a huge risk if the issue of the smart contract can just update the smart contract overnight and maybe changes the rights I have as a token holder. So, the typical rating score is reflecting the risk of a total default. That's different for us because we are identifying the risk of can I still redeem the token against the underlying asset.

I do admit it to calculate with 200 to 250 hours to invest per token to understand what's happening there. There have been lots of fear in the market but only because of intransparency and the lack of understanding. I mean >> All right, let's kick it off. Welcome everybody to another episode of Beyond Yield. This one with Particula.

Particula provides independent risk ratings, analytics and monitoring for asset-backed digital assets. And join with me here is Carson Harman who's the co-founder and CTO of Particula. So, Carson, welcome. Thank you for joining us. >> Hi Dylan.

Yeah, very much happy to be here today. Thanks for the invite. I mean, we already had a couple of talks in the past also like on stages I can remember on panels. So, yeah, nice to be here and continue virtually today. >> This is true.

Carson and I have a history of public speaking and being on the panels and we've been collaborating for quite some time. So, exciting to bring this to stage in the Beyond Yield episodes here. So, as we always do, I like to kick it off with just a background on yourself. You know, kind of your journey in crypto and web 3 and how you ultimately came upon the opportunity to co-found Particular. >> Yeah, of course.

Um I mean, uh I have like a long history in general as a developer. So, I'm actually trained developer, started like 18, 19 years ago uh developing like web applications. Back then still everything by hand, HTML, CSS, JavaScript, PHP, and so on. Really every line by hand. Nowadays, it looks a little bit different.

Um but yeah, I always was really uh excited about software and tech in general. And like from this whole web design, web development stage, I more progressed into like application with software development, like applications for touch tables and so on, but always about like modern interfaces. And I was always looking for what's new, you know? What did people not tackle yet? And back then, it was building interactive interfaces for like large uh touch devices, touch screens.

And in 2017, uh I discovered like the blockchain uh space uh because a very good friend uh told me about it. I think that's how most of us discovered it the first time. I was like really skeptical at once, but then, you know, I really started to understand a little bit the tech behind it, and then I was basically getting more and more to this direction. So, you have to imagine between the years of like 2017 and 2020, when I was then actually founded my first uh crypto-related or blockchain-related business, I was like a slight move every day a little bit more towards like from web 2 and the web 3 space. And then 2020 or even late 2019, um I had founded uh together uh with two friends or people I knew by that time uh like We Trade, which is about the tokenization of carbon credits and also environmental assets in the wider sense.

That was like the first touch point also with yeah, now handling, tokenizing by ourself, but then handling a token as product, a token as asset, integrating into protocols, but also marketed to institutional clients, asset allocators, or we uh or we call them. And this was really fascinating but very difficult by the time. Not only because of this tokenization aspect as an additional technological wrapper, but also because of the underlying asset carbon credits by the time. There were lots of fraud happening in the market and actually my goal was using blockchain to solve that, to create that on-chain audit trail. Where is it really coming from?

How was it sourced? What's really the worth of it? And then we worked like with bigger institutions back then, but it was really hard and now in the retrospective, I wish I could have like a rating back then because it would have everything much so much easier to interact with institutional clients. But based on this experience, being on the side of the token issuer was now one of our target client profiles. I then met my co-founders Tim and Nadine.

I think it was the first time at the Crypto Asset Conference in Frankfurt in Germany beginning of 2023 and then very couple of weeks afterwards we did 1 particular actually starting with like indexing tokenized assets on the market, then also starting to enrich the data like with on-chain activity, off-chain documentation and so on. Then running analytics on it and then a couple of months later we start with such approach to do like a scoring, like an assessment and then a what like a risk score to it. So it was like an incremental development from just discovering the market, then assessing, benchmarking and then also really thinking about what's a like rating framework that makes sense. But you can see I started 2017 more like on the personal side and 2020 business side and now I think there's no way out anymore. >> Yeah.

It's a one-way funnel. Right? And and so in 2023 when you guys came together to start building out Particular, what was the market like in terms of the opportunity for risk ratings at the time? Probably a pretty nascent market for the idea of even risk ratings for digital assets. >> Yeah.

What was the what's the opportunity that you guys saw or you know, the um the lack of support within the industry that you found an opportunity to found particular. >> Yeah, I really I like that question. So, thanks a lot for bringing that topic up. The market was basically non-existent. I mean, people were focused either only on like on-chain activities, smart contract risk of what like an auditor is covering like performance and so on.

A little bit of security side as well. And then of course, seeing also that in the traditional world, we have already like ratings in place for the underlying assets. So, why is there something new needed? So, when we first started with like Tokenyze asset ratings, most people were asking us why? Why are you doing it?

Why is it necessary, you know? And then we had to provide a lot of education. I mean, we're working like on the first rating projects like within like the first year, then also launched like or released our first rating report. And I think with every rating report we made public, people started to understand, okay, wow, yeah, I also need to consider this part and this part. So, I think it was like a natural evolution of the last three years to educate the market and show them, okay, it's needed to look beyond the underlying asset level, beyond credit risk.

There's so much other dimensions. And I mean, we have seen many cases in the DeFi space when it comes to like hacks. We've seen I mean, newest one is like Cat Tower. We've seen Drift and so on. We've seen so many cases which led also to having people on the market get a little bit more aware of what can actually happen.

And in lots of those cases, these are like access control related problems, you know? These are like topics which are not typically covered by a smart contract auditor because it's really about how the whole structure is being set up, how many parties are involved. So, it's a little bit more complex and also what we see like in the institutional finance space that a lot of functionalities and administration procedures are still happening off-chain. So, it's a combination of off-chain and off-chain processes. And typically, everyone only looks at what happens off-chain.

And we bring it into perspective and combine it, but to create that value on the market, um I think that's the second part to it. We also started to have like a very cooperative approach with issuers. So, we do like pre-launch assessments where issuers come to us before they bring their product to the market, but in general, we talk actively with them, we share our findings, and we have like several management discussions before the rating is actually being released. So, our goal is also to contribute to the market. I think in terms of like DeFi and on-chain risk, we also shaped the market, co-created the market with other players, but also our goal is really to give token issuers the chance at these insights to improve the issuance quality, to mitigate possible risk uh because of course it's also in our interest that we do not see any kind of like technical problems, hacks, and so happening with the issues we are interacting with.

So, therefore also this built up in a more like incremental way, and yeah, I think now looking now I think 3 years later, the market has basically, yeah, accepted the need for tokenized risk ratings, for risk data in general, for DeFi products, for DeFi actions, and also looking now to the traditional financial space, we can see with what's happening that also this understanding of, okay, now I'm tokenizing the product, meaning that the characteristic characteristics of this underlying asset is changing because I'm introducing a technical risk layer, and I'm also introducing some more complexities when it comes to regulation because the underlying asset can be differently regulated that the tokenized version. And there you can see the need, the justification to have like a rating separately for the tokenized asset, for the token built up, and is now fully present. >> Yeah, thank you for that. Very well explained, and as you mentioned there, I mean, risk ratings frameworks are very well established in the traditional finance sector, uh but the risk profile of an asset changes once it comes on chain as you're mentioning. So, very curious to get your background or your insight on what extent uh do, you know, traditional risk ratings frameworks apply to on-chain digital assets?

Um and to what extent did you guys have to adopt uh new frameworks, right? And so, Partior introduced the um Partior digital asset risk framework. Can you tell us more like what this encompasses? What does this look like? Um Yeah, maybe you can I've got a bunch of uh kind of follow-ups to go deeper on this, but I also want to maybe hone in on and get your feedback to share which specific types of digital assets are covered within this framework.

>> Yes. So, maybe start with the second question first. Um we're fully focused on asset-backed tokens. This can be like tokenized real estate, tokenized gold, tokenized ETFs, money market funds, and so on and so on. So, it's pretty wide.

And I think something we're looking also at right now are vaults, especially if those are vaults handling tokenized assets. So, you can see the major direction is tokenized assets, although not ruling out that also like general cryptocurrencies, crypto tokens will be covered at some point uh in the future. Um but so far it's really asset-backed tokens because they also introduce this additional complexity of having an additional layer to it because there is of course with smart contract auditors, with risk analytics providers, and so on, there is already like a bunch of players on the market who cover part of this on-chain side. But we see the biggest value in combining on-chain and off-chain data points. And looking at the traditional financial sector, traditional rating companies, they're looking of course at off-chain data points, makes sense, but also they're looking mostly like at credit risk, and also a little bit around the like the legal structure, and so on and so on.

But if you just imagine like an iceberg where only like a small part is visible. That's basically what's currently covered in traditional finance, the tip of the iceberg. And everything under the surface, that's building up, that's what we are covering as well. So, we have more than 360° perspective, and therefore we're structuring also our risk assessment on like the issuer level, which is more like counterparty risk, then also the token level, which is more like a structural risk. I mean, considering the token and the product structure, and the underlying asset level, which is then the actual tangible underlying financial asset or the asset or whatever you want to call it.

And looking into each of those levels, we're assessing over 300 data points. That was in the framework. Some of them are just to measure also in general data transparency, availability, data completeness, but others or most of them also really rating relevant, so almost 200 of them. And we're looking at like I said, like the legal structure. Sometimes it's even like a multinational structure in place that you have like a company maybe sitting in the US, but you're offering a structure through like a BVI or Cayman Islands company, or there's also like a distribution dollar company located in Europe.

So, it's really multinational. We're mapping all of that out. Also looking at like which kind of players are involved, like a fund administrator, custody provider, asset manager, and so on. So, typically there are several parties involved in turn like an tokenized asset product issuance. And also of course, we're looking at the internal build-up.

So, what's the team like? What's the track record? Looking at financial audits, we're looking at what they have in place in terms of disaster recovery policies, business continuity. So, it's like a really wide field. And of course, also which kind of licenses they're holding.

So, are they actually allowed to release this product to the market? And what are like requirements they have to fulfill to do so? And this is directly being then transitioning and mapped to the token level, because again, you some cases depending on how the token is being structured, different kind of permits and licenses need to be obtained. So, we're also checking this. We're checking also like which kind of KYC AML procedures are required.

And here comes really the important point. We're looking at I mean KYC is like my favorite example to map out what we're actually doing there. We're looking at the regulatory requirements. So, what does the issue and needs to fulfill? Then we're looking at the terms and conditions.

So, what is actually being promised to the investor who's reading it? And how is it being enforced operationally like on the smart contract level? How is it really implemented? Do I need to provide like KYC and AML features only during the token minting process or also during the redemption or like uh transfer or secondary market sale. So, we put everything in perspective and connect all of those different dots.

And you can imagine that like looking at traditional financial product structuring, this comes with a lot of different features and a lot of different characteristics. So, sometimes we're looking at 800 even 1,000 pages of documentation to skim through. And therefore, it's really good that we have in general with the P-D with the rating framework a very like a structured finance approach on one side, but also like we did completely translate everything into say like a programmatic way to handle it. So, we have predefined options. Everything is possible to compute it.

So, because we want to not calculate this rating scores in a manual way. Of course, we need to assess everything and so on and so on. But we're talking about dynamic ratings. So, all of that is constantly changed. We can talk about this later on because this is interesting also when it comes to our on-chain infrastructure.

So, just let's get back to the token level. So, we're looking at what I mentioned how regulatory requirements are being fulfilled on the operational side. We're looking at access controls on all different parties involved. Also, how risk is being distributed across different roles interacting with the smart contract. We're looking at for example the fee structure, fee governance.

We're looking at security aspects on the token level, but also on the network level. This is also the reason why we are saying or we are conducting it in a way that the rating for token is always individual per network where the token is being admitted because every network has a different set of risk characteristics looking at governance, looking at operational security, looking at in general their performance. So, there's several elements to it and therefore each rating is individual to the network where the token is being launched. And next to this, we also look at, for example, which third parties are integrated in the smart contract architecture like Oracle providers, for example, is there like a phobic solution in place, is there benchmark with like what's happening on the market, we're looking terms of stable coins as an asset class, we also looking at like the deep tech history, deep tech performance, and the mechanism behind that. So, it's a pretty, pretty diverse set of metrics and data points we're assessing, and all of that is constantly changing.

For example, one metric within our technical framework is the upgradeability of smart contracts. Why are we looking at it? Because from an institutional perspective, there's like a huge risk if the issue of the smart contract can just update the smart contract overnight and maybe changes the rights I have as a token holder. So, we also make it possible to identify when this is happening. I mean, we're basically checking on a daily basis any kind of updates for the tokens we're monitoring, and then we're able to inform also the investors, asset allocators who subscribe to it about this change.

So, there you can see it's like an assessment on the from the legal perspective, it's from a security perspective, it's from a compliance perspective, and everything is framed around like the interest of institutional investors. And why do I highlight this in specifically? Because if you compare what's important for retail investors and institutional investors, it's a completely different set of metrics. And when we take, for example, what's required within the MiCA regulation in Europe, that the token comes also with a functionality to pause or transfers and all activity, putting on the retail lens, it's like, "Oh my god, this is like sketchy. I don't want to invest into it.

I have basically zero rights. I cannot control it. I want to stay away from it. Putting then the institutional lens on it or looking from the institutional lens on it, this is absolutely required. So, I need to comply with regulatory requirements, but also it's like a safety net in case something is happening.

And there you can see we really need to consider what kind of transparency, what kind of assessment, and what kind of quality standard we are also communicating to the market is enabling institutional investors, institutional asset allocators to get into tokenized assets. And I think looking at the past 3 years, there have been lots of fear in the market, but only because of intransparency and the lack of understanding. I mean, typically you need to consider if you really want to assess the whole scope of what we are doing, really also looking at like the off-chain platform, how KYC is managed, how the interaction with the fund administrator is managed. I mean, there are so many different little parts that are happening behind the scenes what you do not see when you just look in the pocket for where to token, but you would need to calculate with 200 250 hours to invest per token to understand what's happening there. And we take away this burden from institutional investors to enable them to have like faster decisions in place and act on the market.

And one more thing to this, we do also harmonize this data sets. So, it's possible to compare an issuer who's coming from a jurisdiction like Switzerland with an issuer coming from a jurisdiction like Singapore. Or we make it possible to compare tokenized asset which is like being emitted uh in the asset class of like tokenized equities with something in the realm of like tokenized real estate. So, because of the structured approach we have in place, it became easier to compare between jurisdictions, asset classes, environments, networks. And I think this makes it really easier for investors to deploy their capital in the market and also track what's happening with this investment, how is it developing over time.

>> A lot to digest here. I think one of the one interesting note I just want to I want to jump to that you shared is how the risk perspective of of an invest of an institutional investor is way different than a retail investor. I really like this example how like upgrading or pausing a smart contract actually is an is you know, enticing to a to an institutional investor but not a retail investor. So um helps to clarify how this is targeted. Uh the framework is directly targeted for institutional investors.

And I think that how comprehensive that answer that you just provided was really gave insight into what the product is. As a as a layman, when you look at a risk rating and you look at it's you know, triple A or you know, you look at the rating itself, it's kind of hard to understand really how what this is indicating. A risk rating >> Yeah, let me add to this. This is very good that you bring it up because maybe we also have in the audience people coming more from the TradFi space. So the typical rating scores reflecting the risk of a total default.

That's different for us because we are identifying the risk of can I still redeem the token against the underlying asset. And I think that's something which is also a little bit a mindset shift because when I'm talking about assessing a tokenized asset, I may fear that on the token level something is happening and that's why the asset has a low rating and it's not a good investment. But in the moment that actually I need to pay attention to that the issuer is still existing, the company is still operating, that I can redeem this token is shifting this token related security assessment and rating perspective more in a direction of like a counterparty risk because the token and the underlying asset like a tokenized ETF, the ETF can be as good as is. The token can be as good as possible. But if the issuer is going out of business, what am I doing with the token?

And that's I think a really big thing to understand when looking at ratings in on-chain markets, that the narrative is not you're okay, I'm looking at credit risk and the probability of a default. We have completely different risks present when we interact in DeFi markets. >> Hey, and this is precisely one of the caveats I've heard the arguments against some of the RWAs that are being issued and brought on chain tokenized assets, you know, I can purchase an equity or a share of something, but it's held in like a BVI or Cayman Islands entity. >> Yeah. >> If it goes under, what's the redeemability of it?

So, I I completely agree with this with the thesis that redeemability of these asset-backed um uh tokens is a is super is a super underserved and essential component of >> Yeah. >> you know, of the risk profile of them. And so, yeah, super clear then. You know, it it's So, let's say for example then, I'd love to look at target consumers, right? You really identified institutions.

Um and so, is it typically an institution that'll come to Particular and say, you know, we're interested in potentially um allocating capital into this asset, can you provide a risk rating on this asset? Or is it more so the asset issuer themselves that say, you know, we want to be institution-facing, uh can you help us get a rating on this so we can provide this to our, you know, prospective institutional partners? >> Both and also one additional dimension, but maybe let's start with the token issuer. So, that's also like in the traditional model, the issuers approaching us to obtain a rating and then go out in the market with it. Um I think here the big benefit is what I mentioned earlier, we have more like this cooperative approach with them.

So, we also give those insights in order to increase the quality and reduce what we just mitigate possible risks that could come up within the technical infrastructure whatever aspect. Um so, therefore this is like of course a really important um client audience for us. We are working directly with token issuers. So far we have looked at over 200 tokenized asset insurances. So we also have a very good understanding about tokenization platforms out there, off-chain, on-chain procedures, the mix and also how they bring the product to the market, how it's generally structured.

So I think also looking at our platform being present there is also a little bit like a gateway to become visible for token issuers, for possible asset allocators. And now looking at like this target audience of asset allocators, and this can be like a deployment protocol. I mean we are seeing lots of things happening now in the Solana ecosystem and also others where capital is actively deployed into tokenized assets. Then we have like institutional investors coming from the traditional space looking to diversify into tokenized assets, on-chain assets in general. We have also on-chain native investors in place who look to deploy capital into it.

We have like bots coming up more and more. I mean tokenized assets are being like also accepted as collateral now within lending bots. And in general lots of DeFi bots start to establish a strategies where they have a mix of crypto tokens and tokenized assets where they invest into it. So therefore you can map out the need to receive risk data for those tokenized assets is increasing. And therefore everything of that, let's say we are putting like the title asset allocators above that.

It can have different ways. On the one side they of course want to monitor and do due diligence before. So first due diligence on the tokens, then create a basket and then monitor how they are developing. Uh but also when you look at like lending protocols and general DeFi protocols, they also utilize it to calculate the interest rate, loan to value ratio. So they need to have some kind of risk data in place in order to build rules and automation top of it to operate the actual business model.

I think this is highly interesting because this brings us also to like a third party on the market. These are AI agents. So we are looking very actively towards like the agent economy because you can see now they are more like aspects Russian dolls in the room talking about like agentic portfolio management agentic transactions and I think it will happen sooner than we may anticipate but also those agents those agentic flows need some data where they can act on and I think the risk data set we are providing it's not only the risk score itself it's also coming with sub scores it's coming with identifier information for example in the issue level it's the LEI on the token level the DTI digital token identifier on the underlying asset level from a traditional sector you know like the ISIN or like a CUSIP and therefore we map it out like with risk data identification data we provide like red flags and alerts as well so we add a bunch of metadata to the risk score in order to also provide context and I think this will be also a big enabler for the agentic economy and was also one of the reasons why we started to bring all of this risk data on chain within our particular digital asset risk passport to make it consumable in this way because when you now see those three different target audiences it's also like mixed in terms of how they consume the data in the traditional way risk ratings are consumed within like a PDF or like on the website or even by email so there are several ways of course everything off chain and not so highly dynamic typically with a delay of weeks until the rating app action happened and the risk score has been like evaluated again based on some parameters have been changed. So we do it completely dynamically that's also why we build up this framework in a programmatic way so we can recalculate the rating score every time one of those rating relevant metrics is changing or market event is happening then it can be consumed either due to the through the platform which is then more like the web interface for let's say traditional asset allocators but also then directly on chain by DeFi protocols or agents. So therefore it's a quite diverse set of target groups we are working with, both TradFi and DeFi, both off-chain and on-chain.

And of course, we are very much aiming to work with all of them and increase the velocity of how fast we can deliver this kind of risk data, how fast we can update it to give all of those participants in the market the best possible option to build on top of it. And one thing we are also doing and then maybe I do a pause. Uh one thing we are also on one side is also like pre-allocation assessments. So, then issues come to us before they launch the product to the market to benefit from the insights we can give them based on the assessment. And also we have structured in the past this kind of like RFP like request for proposal programs where we then transfer our rating framework into a specific use case of the asset allocator and then build up a full like interface platform for asset issuers to directly put in like all the data and basically apply for this program.

So, there you can see that the risk framework is one big part and the output of it in a way of like an eligibility program, in a way of like the finished risk rating, a rating report or on-chain risk signals is pretty multi-dimensional. And therefore, I think that's the interesting part. In the traditional financial world, it was always like let's say one-dimensional. We work in a very multi-dimensional way. Which is by the way also an interesting aspect on why we did choose a DIA as an oracle provider and infrastructure, but maybe we talk about that later.

>> Yeah, no. So, I I want to I'm at the risk of probably making you repeat some of the things you just said. I think it's important to isolate and go deeper into the particular digital asset risk passport. Okay, the passport is the idea of these ratings and data points being brought on chain for on-chain use cases, uh as you mentioned. Okay, and uh you jumped into a bunch of things here.

I mean, like I now maybe I can get you to like do a little bit of like a forward-looking estimation, right? If I may. I'm curious. Do you find What do you feel is the bigger growth vertical then for particular when you look at total addressable market? Do you find that it's these off-chain risk ratings that are primarily targeted towards institutions?

Or rather than through the digital asset risk passport, bringing them on-chain for AI agents to consume and, you know, your average retail investor to utilize as it um implicates, let's say like loan-to-value ratios, uh I I and actually, can you also share go through again kind of what this the digital asset risk passport would implicate when utilized on-chain? Um so again, kind of a kind of maybe let me recap. Um Which ones that Which ones are your total addressable market for you guys? And what is you recap again, like what is the use case on on-chain for the risk passport? >> Yeah, I mean, it's a very wide field and looking at the traditional rating space, let's say it in simple terms, it doesn't wait work in 24/7 active on-chain markets.

It's highly dynamic, so therefore updating a rating cannot take days or weeks. It needs to happen basically almost in real time. So therefore, in my point of view, the future can only be in on-chain markets in on-chain ratings. Because otherwise, it's not possible to interact with that market and to build on top of it. So therefore, we can also look at the business model.

I think that's even more interesting question. So so far, basically the issue is approaching uh the rating company, the rating provider, there's like a one-off transaction, then I'm holding like the rating, and then I'm going out with it. The problem is now, overnight, we touch base like on examples like contract updates, token holder concentration, and so on. So one day later, the whole risk profile has changed and I'm still going out with that rating from yesterday. And we did rethink this completely.

So, in the end, we see like the whole DeFi actors, DeFi protocols, more consuming risk data in the moment they need it. So, in the moment a redemption is happening, in the moment a new collateral subscription is happening, in the moment a recalculation of the rating score, recalculation of like the interest rate is happening. Also, like an exchange was currently monitoring the assets they allowed to be traded on the platform. So, we see it more as a continuous consumption of risk data, containing the risk score, but also the metadata around it we mentioned. So, therefore, it's completely new way of how risk data is being structured and consumed and a new business model on top of it.

And looking at like DeFi protocols in the agenda of economy as such, the risk passport is a dynamic vehicle on chain, which you can stuff with a lot of different information. So, we are of course, I think the main provider of information for this dynamic vehicle on chain with all of our risk data, but also in the future, we can see or what's already starting to happen that for example, fund administrator is directly ingestion ingesting the daily calculated NAV into the designated field in the risk passport without going two ways with the issue and off-chain ways and so on and so on. So, it's a direct way also to ingest this information into this risk passport. This can be also like a proof of reserve attestation. This can be information about the regulatory requirements when they are changing.

So, there's so many different ways of how external actors can also contribute to this risk data set. So, we designed it in a way that it's fully extendable over time. So, any information that's coming in is not overriding the existing information for this certain metric, it's just it. So, also we are building a full on-chain audit trail, meaning that in the end, you can see, for example, the rating score itself, you can see on-chain the full history of ratings that have been awarded and calculated for this token. And then also like always logged as an event with a reason, you know?

And therefore it becomes very transparent how risk is developing on-chain, and also it enables asset allocators, asset managers, and future AI agents to run their own analysis and maybe also prediction based on it because the full set of historical risk data available natively on-chain. And in the simple sense, the risk passport is like an NFT with a bunch of metadata that can be extended over time and coming with an access and control layer, so that everyone can write data, not everyone can read and update data. So, we can also control which party has which rights, who can interact with the risk passport. And I think one really important thing to mention is as well, each tokenized asset gets connected to one risk passport. And when I say one risk passport, it still means this risk passport can live on several chains simultaneously with metadata being synchronized between all of them.

So, when I'm like a DeFi protocol on Solana, and I accept a tokenized asset that's active on Solana and Ethereum, I can also query the risk passport on Solana and get also the risk data set for the issuance on Ethereum. Because very often the characteristics are completely different. So, therefore as an asset allocator and not only evaluating the risk isolated for the chain where I'm acting in, I evaluate the risk of this tokenized asset, doesn't matter which chains it is active. And I think this is also like a mindset shift to really think about tokenized assets or token in general is living, is interacting in different networks, different blockchain networks to understand that each of this network is coming with its own risk profile, that's something to be considered. And we see the risk passport as the yeah, let's say highly dynamic very flexible data object vehicle, however you want to call it, that is storing all of this information and can be used as the basis to then build my own rules on top of that.

>> So, can we take this then into the context of some of the partnerships and ecosystems that you guys have already expanded into? I think that's the next logical step here to kind of see the whole process. I'm super curious. I think the digital the risk passport is super exciting. I agree with your thoughts entirely that in order for this to be effective, it needs to be real-time so that any resulting actions that come out of it or automated actions that come out of it can be instantaneous when information changes.

So, super interesting point about how it um becomes more real-time accurate once on chain. And also which just opens the floodgates for more utility. Um And so, how is this going so far? You mentioned Solana. I know you guys were have done some interesting pilot programs.

I don't know which which would be most interesting to highlight, but if you could highlight some of the use like partnerships so far that would maybe be the best example of bringing this use case to life. I think that'd be super neat. >> Yeah, I mean there's a lot going on behind the scenes. You can imagine integrating something like this is not happening overnight especially if our protocols having our own risk teams. >> I'll I'll caveat and I'll say of those which you might be able to share or high-level examples, right?

Because I know there's also a lot of this is probably NDA'd and in the works currently. So, of course with that >> Yeah. I mean you would you can maybe guess we're basically everywhere working with people who are into tokenized assets and protocols. There are a bunch of them uh although this is like not uh something which is pretty much marketed because what we are doing right now is also simulating a lot of different scenarios to understand how is a lending protocol actually acting if the rating score is dropping not only by one notch but maybe by three notches. So, what is happening then?

How can this kind of dynamic be managed? So, therefore we're working with a lot of those protocols together. We're handling tokenized assets and also, what might be surprising but it's really cool to see the development, there are more and more protocol protocols who do Well, active right now like in the normal crypto space and they want to expand into tokenized assets but could not do it so far because they didn't understand they have not been able to quantify the risk that's attached to it. And I think speaking more like um on like a meta level, um one of the biggest parts we're looking at or the two biggest parts we're looking at right now on the on uh on the one side the on-chain allocation programs that are happening on the market. I mean, we have seen it uh with a couple of them with the Spark Run Free in the future.

We had Kiln on Solana a couple of months ago. So, there's more and more happening right now. So, native on-chain allocation is one of the biggest factors we're driving forward, utilizing the framework, applying those principles what we have learned over time, and give a chance to them to evaluate which tokens to deploy into it. And I think the biggest part we're working on right now, and I think that's also most likely to have like couple of announcements coming up and on the next uh few weeks and months, are like lending protocols who do accept tokenized assets as collateral. Because this is like a very delicate topic.

In some cases, we can see it already. I mean, vaults is also a topic. But then looking like at a lending vault, you might accept a tokenized asset or token as collateral which is representing a basket of underlying assets. Or you even accept as collateral a vault token which is representing a share in this basket, whereas in this basket some of those tokens are also represented by several underlying assets. So, we have this kind of multi-layer structure in place and I think this is really cool and interesting to see how it's developing, but also some kind of dangerous.

And I mean, we also have like this definitely something to highlight. We have like a good partnership also going on like the Centrifuge. I mean, they're really successful. It's really cool to see tokenization platform, but also coming out with lots of like yeah, novel and really cool use cases like the DTE RWA like a wrapper, which enables also for retail users to get access to this kind of institutional grade investment products. So, you can see there's a lot happening and the risk passport acts in this way as like a bridge, as like a gateway to access all of that.

And of course, we're looking more like programmatic use case scenarios where the lending protocol and I mean, yeah, I think we're looking mainly like at the Ethereum and Solana. So, I think these are like the most active ecosystems we're currently in this matter where the lending protocol is consuming constantly risk data in order to always be able when a threshold they can define by themselves enables them to then recalculate the interest rate and maybe increase it or also increase the liquidation bonus. So, it's also way to create this kind of safeguards for protocols because it's so hard to estimate the activity in a tokenized asset market. And what other thing we're very actively working on in terms of like use cases, integrations and it's in general agentic finance, the agentic economy because we already also started to deploy our own autonomous rating agent on chain. So, it comes directly with an on-chain identity, is currently active like on three chains and can then actively collect information also and stuff it directly into the risk passport.

So, there's several parties interacting with it and I mean, we have done lots of rating projects. Not all of them are public unfortunately, but the last one also we brought public is like for Kyros at the Horizon token. It's launched on the Canton network like on a private network, which is also very interesting use case. We have worked a lot again like with I mean Centrifuge as like tokenization platform, but with the issue and more general senders, and we have worked with likely Wellington Management. So there are many clients we're currently actively using already the particular rating and the more these kind of tokenized assets are getting utilized in different protocols, the more interesting the risk passport as this kind of like risk vehicle becomes.

So therefore, I mean it's a wide field. We can talk about it for hours because there's so much happening on the market, but looking at like also maybe some numbers. So we have so far conducted 200 over 200 tokenized asset assessments also to get a better understanding of the market. I would say maybe yeah, less than 10% of them are public, but that's normal. I mean that's also like the normal traditional finance market.

Not all of them are public, but we're currently looking at the shift in the sole rating market, whereas not anymore the token issuer is mainly the driving party, but it's then the protocol who wants to deploy capital, wants to allocate capital, wants to monitor the risk profile of tokens they invested in. And therefore it's also a shift of the whole business model and I think that's the exciting phase we are right now in because it's not only about technical integration, it's also about rethinking how is this being monetized, how is this being integrated into all of those programmatic automated flows within DeFi protocols. So it's exciting. We're seeing a lot and I hope I mean we have lots of conferences coming up with the agenda of finance conferences, with the board summit, with the year summit. There's so much happening, so we're also looking forward to do some announcements around the events, of course.

>> Nice. Yeah, super excited to dive deeper into the uh the ratings agent that you highlighted a bit. Seems like a really cool way of scaling. And one of the things I I guess one of the preconceived notions I had coming into this chat is wondering how this scales because, you know, assigning so many such comprehensive analysis and ratings uh for assets seems to be a very um tedious long pro meticulous process and it is. Uh but with the framework that you guys have put in place, it allows you guys to scale.

And as you mentioned already, hundreds of assets that have already received ratings from Particular. So, super exciting to unblock the sca you know, the scaling um troubles that one might have in in such a um you know, a com complex uh space. Um and also excited to see how the ratings agent can support on this. I want to be cautious of time here. So, like limit my curiosity to a few last questions.

Um I something I got to say, which is I always I think we also had a chat about this um maybe a couple conferences ago. I'm always curious the overlap between ratings and insurance. This is one topic that you haven't touched on yet. I think all of the events over the last year, you know, the the bad events, right? The October November 2025, the more recent Cal DAO incident.

I think they beg for two things, which is uh they at least two things. Um ratings and insurance. And I think that there's a strong overlap potentially between the two, right? Insurance protocols can definitely leverage Particular to help underwrite risk and provide coverage on different assets. Is this something Is this a vertical that you guys have explored?

Um curious I mean, again, I don't want to force something, but I'm just I wanted to open >> It's a natural development. >> Yeah, a natural next step. It's so curious yeah, how you guys are looking at that as an opportunity. >> Yeah, I mean you just mapped it out pretty good already. I mean of course we are also in contact with like insurance providers on chain native on chain and also off chain and everything what they need is of course data, risk data.

And I think therefore it's so interesting when you look at like on chain insurance protocols who also work in a highly dynamic way, for them there's always like a big delay when they need to consume information off chain, always delays on all ends. So also by bringing this risk data on chain and update it highly dynamically almost like in real time with small delays, also enables them to interact with it and then calculate risk premiums in also dynamic way. And there we can see comparing trade file DeFi, everything is becoming from being static into becoming highly dynamic, which is very difficult, but I think the potential for like insurance protocols is quite high and for insurance protocols for an insurance use case in general, becomes even more important to not only look at like on chain activities. And that's I think the good thing that we combine like those on chain off chain data points to also understand where's like a possible risk on the off chain side, on the issuer side, on the legal side, on off chain procedures like KYC and so on. So we're exploring some of those use cases.

I mean insurance is still very much oriented I think towards like the retail sector, but we can also see that there's increasing interest from like institutional side, more like from the professional side, but I think it's also highly risky. I mean looking at particular what you can do with risk data, insurance prediction, there's so much possible. We don't touch it by ourselves, but we give the right input to get started on this. And we were trying it out, that's also why I mentioned that we're doing simulations right now. How does a lending protocol operate?

At which point would they liquidate a position and which kind of insurance is possible to implement there? So of course we also think about which possible use cases we can fulfill with what we are doing. Although we are not offering this kind of service by our self. >> You are You also open up now the gates on predictions as well. Like prediction >> It's Yeah, also something I mean, if you see how the market is developing and the potential on the market, tokenization as such is I think still perceived in a too limited way.

There's so much more possible what can be tokenized. And I think that's the cool thing. Framework we are having in place is completely asset class agnostic. So, it doesn't matter what you're tokenizing. Of course, it matters then later on when you dive deep on the underlying asset, but we are only scratching the surface of what's coming in terms of tokenization, but also what comes in terms of on-chain DeFi risk.

>> Yeah. 100% agree. I mean, it's probably arguably the largest total addressable market within the industry is assets that will be tokenized and brought on chain. Um And there's a >> expanding. I mean, if you have seen like the update on like the BCG study market study, I think the potential they are seeing now is now has tripled basically since they did the last study like 1 year ago.

And you can see this is not coming from out of nowhere. This is really also what we are seeing that token issuers are not anymore talking about one token asset issuance. It's a real It's a full program. It's a full suite of tokens and products. So, we are reaching that point in the market where we slowly getting this kind of like from an investor perspective you say like a hockey stick, but I just want to say in a visual way, "Hey, now the acceleration is happening." And I think that's so interesting to be active here at this position at this moment in time in DeFi markets.

>> Super exciting. This was um a great uh background and I think comprehensive overview and in particular for anybody listening. So, um in sake of time, I to say thank you for joining and spending your time on the podcast today and going really deep into all of the complexities um and the use cases for Particular. I think it's super exciting. I completely agree with the vision that you guys have.

Also excited about the collaborations that we have ongoing with respect to the digital asset risk passport. So even a little bit slightly bit more bullish now after going deep with you. So thank you for that. I want to maybe we can close out I want to just make sure that we didn't miss anything essential that you also wanted to highlight or share. In other words, otherwise, you know, what would be the best resource or access point for an institution and you know, an asset issuer or somebody who wants to go maybe get in touch or learn more about Particular.

>> I mean, first of all, thanks a lot for the invitation and for having me. I think also to stay up-to-date with what we're doing in the market on our website particular.io, it's possible to download also the past public released rating reports to get an idea how deep we're going or how extensive it is. Next to this, of course, on X, my handle is just Gaston Merzmann and then underline or like Particular underline IO as our company account, so there we provide frequently updates. But I think one thing I also wanted to mention, I mean we are at the DIA data podcast, so therefore I think it's also good maybe to close out this talk with why did we actually choose to work with DIA. So I want to give some highlight to this as well and we mentioned so much about risk scores, about subscores, about lots of other metrics, so you can see it's like multi-dimensional.

And I think one constraint we've seen in the market typically within like the Oracle feed, it's like always like one data point that's being transmitted, so it's one feed, one data point. And we believe this is not enough, it's not versatile enough for risk data. So we have like a multi-dimensional data feed in place. I think this is highly interesting because by directly supplying also subscores, each DeFi protocol, each receiver, each consumer can directly do their own rules, their own automation based on their own risk profile. For some, maybe the underlying asset part is more important.

They have more weight on like this sub score. For others, it might be the counterparty risk. And therefore, we deliver it all in multi-dimensions, and then the receiver can act on it in a very flexible way. So, I think this is like highly beneficial and based on how we are seeing risk in DeFi, this can't be one-dimensional. So, therefore, for us, yeah, it exactly solves uh solves like the purpose we had in mind.

So, yeah. Always happy to talk about our cooperation. >> Uh thank you for that. And I think it's a super exciting just example of the Oracle use case itself. What particularly is doing is combining and bringing the off-chain intelligence side of of risk to on-chain as well.

And this is like what the, you know, the dream use case for an Oracle is to is, you know, has always been to be the the bridge of access between web two information and web three information. So, I think it's really good example use case of a strong collaboration in bringing more utility on-chain. So, super excited about it as well. Um yeah. >> Cool.

>> Excellent. So, with that, Carson, I think we wrap up here. Thank you so much for joining the podcast today. Um yeah, excited to watch this back again, actually, and go a little bit deeper. And uh hopefully we catch up soon at one of the conferences.

>> Likewise. Thank you very much for having me. It was definitely fun to be here, and yeah, always happy to continue the conversation. >> With pleasure. Yeah, cheers.

And thank you everybody for tuning in today. >> All right. Bye-bye, everyone.