August 25, 2025 • 68 mins
Built primarily for institutional-grade clients, Pareto delivers customizable on-chain credit markets designed to expand DeFi liquidity and TradFi tokenization through structured yield strategies tailored to diverse risk profiles. Pareto allows its users to construct individualized credit lines in specific risk-ajusted tranches, with custom: interest rates, lockup periods, withdrawal cycles, reserve ratios, etc. In addition, Pareto’s USP is an yield-bearing synthetic stablecoin, fully backed by major stablecoins, that can be deployed into a diversified portfolio of liquid, short- and long-term credit, thus increasing capital efficiency.
Topics covered in this episode:
- Matteo’s background
- Idle Finance yield optimization
- Pivoting to Pareto
- Institutional borrowers in early DeFi
- Competitive advantage of Pareto
- Outsourcing underwriting
- Managing defaults
- Customized lending
- KYC requirements
- Timeline terms ‘marketplace’
- USP, Pareto’s synthetic yield-bearing dollar
- Legal framework & credit allocators
- USP yield, liquidity & integrations
- ‘Opaque’ credit vs. DeFi
- Pareto smart contracts and redeems
- Success in on-chain credit markets
Episode links:
Sponsors:
- Gnosis: Gnosis builds decentralized infrastructure for the Ethereum ecosystem, since 2015. This year marks the launch of Gnosis Pay— the world's first Decentralized Payment Network. Get started today at - gnosis.io
- Chorus One: one of the largest node operators worldwide, trusted by 175,000+ accounts across more than 60 networks, Chorus One combines institutional-grade security with the highest yields at - chorus.one
This episode is hosted by Friederike Ernst.
Mark as Played
Transcript
Episode Transcript
Available transcripts are automatically generated. Complete accuracy is not guaranteed.
(00:00):
Arbitrage opportunities are now like always available and so the
funds that we were using to deploy into the arbitrage bots
were idle for some time. So we're like, OK, we need to
find a way to make that more efficient.
In 2022, we realized that most of the customer base for idle
finance was traditional funds are locating across different
(00:21):
strategies. And one of the major feedback
that we received at that time was that you'll go to the
optimization is great, but we need to take risk and risk
diversification. So at that time we decided to
introduce yield the tranches credit lines can be really
customized from underwriting terms, covenants to compliance
(00:42):
requirements. This is something that like for
example, Maple or Centrifuge is a bit limited.
I would say the credit lines that we have are creation based.
So we're not the only one that are underwriting these kind of
credit lines, but we opened up this process also to 3rd party
curators. Welcome to Epicentre, the show
(01:05):
which talks about the technologies, projects and
people driving decentralization and the blockchain revolution.
I'm Frederica ANZ and today I'm speaking with Matteo Pandolfi,
who is the Co founder of Pareto,a credit coordination protocol
that evolved out of Idledale in and is building on chain markets
for private credit. Before I talk with Matteo, let
(01:25):
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Matthew, it's super nice to haveyou on.
Really excited to be here. And yeah, thanks to the entire
(03:32):
Epicentre team for hosting this session.
To all of you for tuning in as well.
Cool, Matteo, tell us a little bit about yourself before we
dive into Pareto. What's your professional and
academic background? Yeah.
So before founding Pareto and aswe are going to go through in
(03:52):
more into details, Idle DAO backin the past, So my background is
in finance. So my academic career was pretty
much in finance and entrepreneurship.
So it's kind of like a good mix between that that brought me
into D5 this kind of like, you know, innovative market, but
still really related to finance before actually starting with
(04:15):
Pareto. And and I do doubt I was working
on financial risk analysis. So I was consulting with major
financial institution in Italy like it is a Sao Paulo for
example on their credit desk. So essentially do helping on the
underwriting process and due diligence process in order to
create these kind of like corporate bonds instrument for
(04:38):
these financial institutions. What drew you into the
blockchain space in in the firstplace?
That's a that's a really good question because I've been
brought into the D phase space by my other Co founder, our CTO
and who made me find out find out about Ethereum at first.
(05:02):
At that time, I'm actually, I was actually still a finance
student and we were exploring Ethereum.
We, he had background in computer science and with my
background in finance, we were thinking how we could build
something on Ethereum and the first instance of something that
(05:22):
would be old on chain was actually touching the arbitrage
and arbitrage bots. So you know what we know right
now as Med at that time was still called PGA price Gas
Auction. You know, so we started building
(05:43):
these kind of bots that we're looking on at discrepancies
between Chexys and Daxys and arbitrage in price across these
assets. And and yeah, so that that was
kind of like the first instance and the first way where I
realized that we could really create something that was
(06:05):
completely programmable and automatic and was really like
changing the way that I was usedto see finance.
Because, you know, from the academic, but even from the
professional background, I always swing like finance being
quite siloed. So all these kind of closed
system that we're all like communicating internally and not
(06:28):
to each other. And with the theorem, that was
like the first time that it was my ha ha moment is like, OK,
that that's really composable. And that's something that we can
build automation that that runs on chain and runs 24/7.
So that was the first time I really touched with my hands
(06:49):
Ethereum and, and the power of smart concerts.
And then, you know, I, I just got into the rabbit hole of
that. So we started exploring landing
protocols. That time there wasn't even a
other, it was only compound and the YDX.
And so then everything evolved into what brought us into idle
(07:11):
finance. And then those are way too later
on. When did you decide to kind of
discontinue the arbitrage part? That's a good point because idle
finance came out from a need that we had for the arbitrage
bots because actually, you know,arbitrage opportunities are now
(07:33):
like always available. And so the founds that we were
using to deploy into the arbitrage bots were idle for
some time. So we were like, OK, we need to
find a way to make that more efficient.
And so I would say that we proposed idle finance as a way
to optimize the yield on idle phones in 2019 on an Akatoner on
(08:00):
Git coin. So I think that was in 2019 when
we decided to focus completely on an idle finance and put aside
the arbiter spots. So I would say 2019, we started
the arbitrage bots back in 20/17/2018.
So, yeah, we ran that for a couple of years and then decided
(08:20):
to focus completely on IT finance.
And what's the rationale for kind of moving over purely a
return on investment kind of play or what, what were the
drivers here? Scalability, one of the major
drivers were scalability becausewith the arbitrage bots, we saw
(08:41):
that by increasing the AUMDTVL of the arbitrage bots, we were
actually reducing the margin of the funds for the arbitrage
opportunity. So we, we were really focused on
building something on Ethereum that could be scalable,
scalable, you know. So when we started implementing
(09:03):
the very first version of idle finance into our arbitrage bots,
we realized that we could manage1,000,000 to 100 million and the
result was pretty much the same in terms of return of the
capital because the kind of optimization could be applied
with more capital compared to arbitrage bots.
So building something that was able to be scalable and to be
(09:29):
offered also to a more broader audience, I would say because
you know, arbitrage in general is quite a niche product or in
general it's a niche concept that not not a lot of users
grasp or understand in general. Instead like automating the
yield generation optimization, yield optimization, yield
(09:52):
generation across different protocols was really something
that was clicking with Defy users at the time.
So I would say, yeah, scalability and also being able
to offer this kind of product toa broader audience and let let
them understand the product to abroader audience was the main
driver of us focusing completelyon idle finance.
(10:15):
There were quite a few projects that kind of centred around that
right, kind of, I mean, in the very beginning it was things
like Insidap that kind of let let you kind of switch funds
between make and ABBA memories serves correctly and then kind
of more sophisticated things like come came along.
(10:40):
So how? How did Idle Dao kind of fit
into that landscape? So I think we had the lucky and
the opportunity. We have been lucky and we had
the opportunity also to meet like instead of team or even
Andre Cronzer from why you're even before like them building
(11:01):
out their products. I would say instead up is being
really great that they're they have been really great builders
and but never really focused on yield optimization and
automation of that yield optimization.
They were more focused on providing great tools for
borrowers that were using like for example, compound Ave. or
(11:25):
even Maker DAO. And I would say that instead of
falls more into the into into a different category compared to
idle finance, why you're earningidol finance are pretty much
similar. Actually, I remember a lot of
conversation in Telegram, we found that crunch at the
beginning on how to solve some like initial issues of yield
(11:47):
optimization. Like there was these ping pong
effect that we found out at the time that was affecting yield
aggregators, which means that essentially when when you move a
big chunk of the funds into our specific lending protocols, you
also influence the interest rateon that specific protocol.
(12:08):
So if you don't have control on the funds and a control on the
influence that you have on the interest rate, you might end up
into a ping pong effect, which means that you move all the
funds from protocol A to protocol B because protocol B
has the higher interest rate. But when you do that, you're
lowering the interest rate of protocol B and might become
(12:32):
lower than the protocol A. So the EEL aggregator sees that
the higher interest rate is again on protocol A and so you
move back the funds from protocol A.
So you see that you create thesekind of ping pong fat between by
moving and rebalancing funds. So we have been like talking a
(12:52):
lot and understanding what was the right, you know algorithm
and optimization mechanism to take all these.
Also, we have hundreds of cards at the beginning and then I
think why aren't did a great jobon, I would say the positioning
and narrative built around the yield aggregation.
(13:14):
At the time we were quiet, you know, for starting idle finance.
As I mentioned, we started with a GIT coin Akaton.
So we won that git coin Akaton and then we got accepted into a
an accelerator in New York with consensus.
And so we kind of took a more professional approach, I would
(13:36):
say or institutional approach tothe product.
Instead, I think we're at very early phase of wire and they
took more addition approach, which turned out to be the right
narrative. So I, I, I, I would like to say
that that they did, they did a great job on that side of
things. And of course then the dev team
(13:56):
and the community that formed around Wyern was really great.
So Congrats of course on Wyern and Andre Cronzo on
bootstrapping these at the beginning.
And I would say that we, we kindof at the end of the day, D5 was
a small market at the beginning.So the way that we were looking
at competitors, the beginning was, was more like competition
(14:21):
rather than pure competition. And so turned out that like one
of the major partners for idle finance goes wire in itself like
with some of the strategies and the goals that wire had at the
beginning like for USBCUSDT and I were based on, I do finance
(14:43):
yield optimization for a big chunk of the funds that they
were managing. So it turned out to be kind of
like a more a partnership ratherthan a competition with, with
wire and finance, for example. Of course, over time Defy market
evolved, become more sectorialized, the more you
know, professionalized and of course competition came out.
(15:07):
But I would say that early timesof Defy, I really enjoyed the
early time times in Defy becauseit was all about like
cooperating together and trying to build like, you know, all the
legal pieces that you could put together in the fight was really
about this. Santa Ben was really about
these, these philosophical you know, work together to grow the
(15:32):
market instead of competing, competing together to shrink the
market to each other. What ultimately kind of drove
the pivot from from Ida to Preto?
I would say that we've focused alot in for on on yield
optimization from since 2019 to I would say 2022.
(15:58):
In 2022, we realized that most of the customer base for idle
finance was institutional funds.So institutional funds are
locating across different strategies and one of the major
feedback that we received there at that time was that yield to
the optimization is great, but we need to take care of risk and
(16:20):
risk diversification. We need to mitigate risk for the
liquidity providers. So that time we decided to
introduce an additional product line and then an additional
primitive to the product suite which was yield the tranches.
So essentially A primitive that could where we could build the
senior and unit tranches, seniormedicine unit tranches on top of
(16:43):
potentially any defy yield source.
And, and so this this was a response to these kind of
feedback that we received and. And senior and junior trenches
in this context kind of being different rates of return for
different levels of risk, right?Or do you mean something else
(17:04):
by? That, no, no, that's correct.
That's correct. Essentially senior tranche is
the most protected vehicle investment world that is
protected by the junior tranche.So whatever there is a default
because of a knock or a loss of funds by the borrowers or these
kind of loss of funds, juniors are first, first loss absorption
(17:28):
layer. So they absorb the the loss on
behalf of the senior because of this risk that they bear they
receive also not here healed compared to compared to the
senior trenches. So we we introduced this
primitive, we started building around different kind of yield
sources and defy over collateralized landings taking
(17:48):
in order to understand or so where was the product market fit
for this kind of product line. And we realized that most of the
product market feed was on institutional credit.
At that time we were working with protocols like you know,
Maple or Clearpool that we're doing these kind of first
instances of institutional credit on chain.
(18:12):
And we saw that like equity providers and lenders were
really appreciating senior and unit tranches for institutional
credit lines because different credit funds, different hedge
funds or families offices had different views on these
specific institutional borrowers.
They were lending, they were lending too.
And so using senior unit branches allowed them to to
(18:37):
customize the risk reward profile of their entire credit
exposure of their entire credit portfolio.
So we realized that most of the product market fit was there,
but we received those a lot of feedback from both borrowers and
lenders that the credit infrastructure was not ready for
institutional needs. It was really kind of like mini
(19:00):
king compound and have a interest rate model which was a
pain for the borrower because itwas a single borrower that
wanted to with a wire valuable interest rate that operationally
speaking was terrible because they were looking more at the
interest rate that they were paying compared to doing their
(19:21):
own business. And in general the terms and the
underwriting process for this kind of institutional credit
lines of on chain at that time wasn't really customizable and
modular I would say. And so we realized most of the
problem market fit was there. There was room for improvements
(19:42):
on the credit space. So we, we said, OK, we need to,
we have a clear opportunity overthere and we wanted to build and
improve that space, that part ofthe D5, that D5 sector, let's
say. So that's pretty much how we
transition to, to Pareto. We started working a lot with
(20:03):
institutional credit lines and then we realized that we wanted
to improve the credit lines themselves, and that's how
essentially Pareto came out. Tell me more about the
institutional borrowers. So kind of what kind of
institutions where they that they were willing to kind of
borrow in this very competitive market on chain where kind of
(20:27):
you, you can go to your local bank and probably get a more
competitive loan? That's a good point because of
course they could access, you know could originate on credit
lines from a traditional bank. But the process of origination
takes a lot of time. Especially the kind of borrowers
(20:49):
that we're working with right now are pretty much, you know,
institutional funds that run delta neutral strategies or
market makers or prime brokerages.
And the process of originating aloan and managing that that loan
in a in a traditional way usually takes a lot of time and
(21:10):
resources from their team. And usually the teams are not
like really huge in. And so the, the overhead is
really tangible for the final borrower.
We, we recently actually run a use case with one of our major
borrowers which is Falcon X, oneof the leading market and prime
(21:31):
brokerages. And what came out of the use
case is that before using on chain credit lines.
So relying on traditional creditlines took around 80 hours per
month from their team to make sure that in order to originate
(21:53):
all the documentation was there and making sure that all the
lenders were aware of updated terms and also doing manual
reconciliation. What came out is that now with
the on chain credit line, they have to dedicate 2 hours per
month the same team. So operationally speaking, the
improvement for the borrower is really tangible.
(22:15):
There's a streamlining process that is happening on the
operational side of things. You know, on chain credit lines
are pretty much self operating compared to a traditional credit
line. So that's that's I would say the
the main advantage of operating announcing credit line.
(22:38):
And secondarily, I would say also being able to have a credit
position that is composable is abig advantage for, for this kind
of borrowers and a big advantage, I would say also for
the lenders at the end of the day, because, you know, being
able to use a credit position asa collateral in have a more for
(23:03):
Euler, for example, or create an, an interest rate swap
instrument using Pendo for the same credit position.
It's really powerful for the lenders.
So lenders have a better, let's say UX when lending to the this
kind of institution and interns borrowers are able to originate
(23:24):
more liquidity with these kind of credit lines and streamlining
the process of managing these kind of credit lines.
Cool. There's a couple of projects
kind of doing very similar things in this space, so kind of
the likes of Maple and Centrifuge and so on.
So how is your approach different?
(23:44):
Or is it different from your competitors?
So it, it is different. I think Centrifuge they started
like really early. So they did a great job at you
know, pioneering on that, on this kind of area.
And also Maple they they kind ofpivoted I remember back in maybe
(24:06):
it was 2020-2021 and then they focused completely on chain
credit. So both are doing great jobs on
expanding this kind of space. The main difference that we
wanted to implement since the beginning of Orator was to make
sure that we have a highly customizable protocol.
(24:28):
So credit lines can be really customized from underwriting
terms, covenants to compliance requirements.
And that's this is something that like for example Maple or
Centrifuge is a bit limited I would say.
But also and more more importantly, the credit lines
that we have are creation based.So we're not the only one that
(24:53):
are underwriting these kind of credit lines, but we opened up
these process also to 3rd party curators.
It's kind of if you look at whathappened with MARFO and other
over collateralized lending protocol in the past where they
introduced these kind of curation role that's essentially
the same that we're doing with arate to compare to other on
(25:16):
chain creditor credit products. This allows us to have again
more scalability for this kind of products because we're not
the only one underwriting new borrowers, but we have other
creators that can provide this kind of underwriting
capabilities. And also in our longer term
vision, the the curation based model allows us to work also
(25:41):
with, you know, kind of like traditional financial
institution. Instead we see more like
products like Maple or Centrifuge kind of a in house
underwriting company. So that, that, that, that's,
that's pretty much like the maindifference that we have.
It's an open curation based model versus all in hours
(26:02):
underwriting process. So underwriting is actually
really skilled task, right? So kind of like what it kind of
takes is kind of you need to understand the books of this
company, you need to understand the business prospects, you need
to kind of you need to calculatethe default risk and the
(26:24):
associated interest you need to charge in order to kind of break
even. Tell me how this process is
handed within Pareto and kind oflike how how you outsource this
to others. So with Pareto that that's a
really good point in the sense that on the writing is it
(26:47):
requires a lot of skills and also a lot of domain expertise.
I would say that's why it was westarted with you know,
institutional digital native funds.
So we're which are most familiarwith what we have been doing in
the past. So we completely understand the
kind of deployment and strategies that they're going to
(27:07):
apply to the credit lines that they're opening.
And of course, like the first step, it's pretty much like as
you mentioned, scan and review of the credit worthiness of the
borrower, which is given by analyzing the books and balance
sheets of these kind of borrowers, making sure there is
(27:29):
enough equity, for example, to be able to cover for any losses
to be. And I would say that like
there's been some experiment in underwriting in the past.
I'm not talking about Pareto, but in the past there's been
some experimenting underwriting small companies, for example,
which turned out to be a bit toomuch naive.
(27:52):
And, and so I would say that like in order to bootstrap and
grow this kind of credit space, we need to start with more solid
corporation and institution at the beginning just to make sure
that if I still get a good reputation on on credit side,
then it really depends on the kind of deployment.
For example, we work a lot with Maven 11 credit as a curator for
(28:18):
five connects. And what they do is pretty much
on the underwriting side of things for the credit line that
we have. There's been a lot of work
around the covenants that are around the credit line.
So the borrower has a certain kind of parameters and action
that they can do. And at the moment Maven 11 is
(28:39):
monitoring that. And we're doing the same for
other borrowers like Fasanara orBoston Trading, for example.
So it takes an initial screen ofthe wealthiness and and creative
worthiness of the borrowers and then it takes also monitoring
tools and monitoring capabilities for making sure
(28:59):
that the healthiness of the borrower is still there.
It's a bit manual at the moment,but our like goal in the next, I
would say year is to how to makethat process.
And actually just to introduce abit a new concept here.
But what we see as a really interesting implementation into
(29:22):
this kind of credit line is 0 knowledge technology.
Because what we what we realizedis that most of the information
and data that we need to monitorand review to make sure that
there is a good credit warnings for a borrower are sensible data
for the borrower. The borrower doesn't feel really
(29:43):
comfortable in making these kindof information completely
public, both for compliance reason, but also for like just
to be ahead of other competitorsand not to fall behind other
competitors. And what we saw is that like ZK
technology really fits well in these kind of area on monitoring
(30:04):
credit warnings and covenants for certain credit lines.
So I'll give you a really, really simple example of that.
Let's say that a credit line foran institutional borrower has
covenant, a covenant that says that the borrower can only
deploy funds into Tier 1 exchanges and no more than 25%
(30:25):
of the entire loan amount can bedeployed into a single exchange.
So the borrower doesn't want to expose all these kind of data
for all the reason that they have.
And by using ZK technology, moreprecisely ZKTLS, we essentially
encrypt these kind of data and what the lenders receive is a
(30:50):
proof of the fact that the covenant is respected and so
also that the the creditworthiness of the borrower
is still there. And, and in this way, we also to
make the process from a more traditional monthly reporting or
like a certificate of collateralon a specific custodian that is
(31:11):
issued. Then you need to completely rely
on a single data source. 2 The idea is to have the borrower
being able to plug in multiple data sources from centralized
exchanges, custodian accounts, bank accounts, even encrypt
these kind of data. So all the sensible information
are, are protected and it's privacy preserving for you by
(31:35):
final borrower. But the lenders on the other
side are able to get a proof of the fact that the covenants are
respected, that the borrower credit warning is is still good.
Is there some sort of second opinion for the underwriting?
So kind of do you have differentcompanies kind of look at the
same loans or kind of is, is the, is the underwriter in some
(31:59):
way incentivized or penalized ifthey kind of make a wrong
assessment? I mean, wrong assessment goes a
long way here, right? Because kind of like everyone
acknowledges from the get go that there is some level of risk
and kind of in order to kind of say whether something was
calculated correctly or not, kind of you would actually have
to run this experiment many times and kind of like you're
only running it once. So kind of, so kind of even if
(32:22):
kind of like there's only a 2% chance of default and that is
acknowledged kind of like those 2% can materialize, right.
So kind of how, how do you do quality assurance on the
underwriters? That's a good question because
we have been thinking about thisbecause of course like
underwriting process is not likemathematics something that
(32:45):
mathematically you can, you can always like be right.
So of course, overall, we, we could expect the faults in the
as, as, as we scale up these kind of credit protocols.
So we can now like say, no, OK, there won't be any defaults.
So we need to be all around mitigating rules for this kind
of default risk. And one way at the moment,
(33:07):
essentially each credit line canbe curated by a single curator.
So there's A1 entity that takes care of of on the writing and
monitoring the advance of a power.
But the guardrail and the essentially protection that we
embedded into it is that the curator is also a legal
(33:28):
representative for the lenders. So it doesn't take care only on
the under of the underwriting process and monitoring process
for the borrower. But in a case of a default, the
creator in this case is becomes the lender represent legal
representative towards the borrower.
So the incentive of course for the curator is to make sure that
(33:52):
they are doing the right calculation in order to get the
default risk and also to make sure that they didn't.
They don't underestimate this kind of default risk because
then they are responsible for any legal representation for the
lenders, which is actually another difference from other
(34:13):
protocols. Other protocols essentially, if
you if you are a lender and it happens to be the full, it's up
to you to go to court to, to file the bankruptcy, for
example, documents in order to get back usually multiple years
and proceed from the liquidationof the borrowers.
(34:35):
Instead in the setup that we have is the curator that is also
taking care of this process. So of course, because of that,
the curator is more incentivizedto do a proper due diligence and
proper monitoring of the borrower, even if it means to be
more conservative than than the usual process.
(34:58):
But that that's the initial incentive.
Of course, this is not the finallike set up, I would say over
time, for example. Yeah, having multiple creators
with kind of different roles, like one on the writing initial
and doing the due diligence, another one doing monitoring,
just to have like multiple parties that provides an
(35:22):
understanding of the borrowers makes completely sense.
But yeah, at the current time, that would be the incentive for
the curator to provide a proper due diligence and provide a
proper underwriting process. What happens if the underwriter
just doesn't do it then So kind of like say the the borrower
defaulted and kind of like thereare 20 lenders that kind of now
(35:45):
I as an underwriter meant to represent it.
I just don't do it. And I kind of just kind of step
out of my business and let it default start up a new
underwriting business and kind of start from square one until
because kind of defaults are kind of, if you, if you played
in any way kind of correctly, defaults are rare, right?
(36:07):
Kind of like default should be rare.
So if if I kind of if I can run my business until the 1st
default unfettered is isn't, am I not incentivized to do that?
Yeah, I see. I see what you're saying.
But actually there would be liabilities for the underwriter
as well. So of course, I, I see what
(36:30):
you're saying that you just likeclose out the underwriting
business on, on that entity and you open up a new one, but you
completely destroy the reputation of your underwriting
process. So these are known entities, you
know who they are. And kind of like, there's kind
of like a soft reputation, even if it's not on chain, kind of
you, you know? Yeah.
(36:50):
I would say, yeah, yeah, Institutional likings are on
chain. Credit is big given we work with
a spectrum of collateral becausewe could, we can enable
borrowers putting collateral of course, but we can work with our
full spectrum of collateral fromkinda standard collateralization
(37:12):
ratio to even 0 collateral. I think it's all about
reputation like all these kind of lending is also reputation
based lending. So I see a lot of also
borrowers, institutional borrowers at the moment that are
looking at this kind of under collateralized credit lines as a
way to to build a credit score that is like it's, it's on
(37:37):
chain. And all the interest payments
that they make, all the repayments of the notional that
they make is essentially something that builds a credit
score that is on chain and it's immutable on chain.
So I would say that one like a abig part of the of managing this
kind of credit lines is also making sure that the borrower
(38:00):
but also the underwriters has this kind of reputation based
way of working on the credit lines.
OK, so I think we kind of cover the how and the the why and kind
of the what. Maybe let's let's look at the
how. So kind of like if, if you if,
if you could walk me through themechanics kind of like from the
(38:24):
lender's deposit to kind of how the credit gets allocated and
how the yield is paid out and how often kind of these loans
are novated and so on. I think kind of that that that
would that would go a long way. No, Yeah, absolutely.
So what the process of lending out to to a specific credit line
(38:47):
we are from a UX perspective, I would say we made sure to make
it as see as much similar to thenormal defy UX as possible.
So initial like, you know, liquid providers could feel it,
they were still like in Defy. Of course there are some
compliance requirements that we need to respect depending on the
jurisdiction of the borrower. So all the credit lines at the
(39:10):
moment have a KYC process that you need to go through as a
lender before being able to deposit.
And so after that, like you can,of course, you can access all
the data around the borrower or all the data around the
performance of the vault and make sure that disrespect the
(39:30):
risk reward profile that you want to have for your, for your
credit exposure. The kind of like the duration,
the and the interest rate modelsof the credit lines really
depends on the borrowers. Because as I mentioned before,
we build the protocol around customizability and modularity
(39:54):
of the credit lines. So the borrower can really
decide how the interest rate model behaves and what is the
duration or the covenants of theof the credit line.
So just to give you some example, just to let also the
audience here understand, betterunderstand how the current
(40:15):
credit lines works, we do have usually like weekly to monthly
duration of these kind of creditlines.
At the end of which the borrowerneeds to pay the interest rate,
the lenders can request withdrawals and, and borrowers
have like different duration from weekly to monthly to
(40:37):
satisfy this kind of withdrawal request.
And, and so the interest rate model, for example, it's decided
be with the underwriter, but also the borrower.
So for example, Fazanara, which is one of the borrowers that we
have wanted to have flexible on chain credit line that we done
(41:00):
interest rate that was based on an external benchmark.
So Fazanara Digital is currentlyrunning a deal delta neutral
strategy doing you know a funding rate arbitration
strategy. And so they wanted to have the
on chain credit line attached tothe performance of these kind of
funding rate strategy. So we took a a benchmark which
(41:23):
is the open interest rate at theBTC funding rate times 1.3,
which turned out to be 88% correlated to 1000 hour
performance. And so we integrated this kind
of interest rate model into the into the credit line.
So that's how we can like the customizability is really, it's
(41:49):
something that can be used within credit lines and and
really changes the way the credit lines work.
For example, instead with FalconX is pretty much traditional.
It's a fixed rate facility with 30 days callback period.
So it mimics a bit more the the traditional kind of credit lines
that they already had, but with the advantage of now for
(42:12):
example, file connects credit line is being used as a
collateral in Morpho. So lenders can also, you know,
perform looping strategies on Morpho or even just you don't
use it as a collateral to borrowout to USDC and deploy USDC
somewhere else. So you bring also more capital
efficiency. So the process of deploying
(42:32):
finance is pretty similar to Defy standard.
I would say UX apart from the KYC process different story also
for USP and SUSP which is the yield being stable kind that we
we put on top of that. But I'm going to stop here.
Not sure if you have other question on the on the credit
license have. Yes, you said there is compiled
(42:55):
3 KYC, is that just for the lender or also for the just for
the borrower or also for the lender?
Also for the lender, yeah. And what are the requirements on
the lender here? It depends on the borrower in
the sense that different jurisdictions are required,
different level of of KYC. For example with Falcon X it's
(43:16):
you as based and we do let's saytraditional KYC process.
So getting all the documentation, source of founds,
corporate documentation if it's an entity that is deploying
found into Falcon X. And so I would say it's more
traditional kind of KYC process.We introduced those in
(43:38):
partnership with our KYC provider, which is curing the
proof of KYC way for some of thesome of the borrowers are fine
with it. And essentially what we do is
letting the user logging into our application via Coinbase,
Binance, OK X accounts and theseallows us to henerate the KYC
(44:04):
level that they have on those centralized exchanges.
So user doesn't have to redo again all the KYC process with
these kind of KYC process that we have.
But they just need to log in with Coinbase, Binance or KX or
even we do have Revolute for example, as a possibility to log
(44:26):
in with and we hina read and we get a sort of like proof of KYC
and that's for some jurisdictionenough for the borrower to be
respecting the compliance requirement that they have.
So those are the true current ways that we can allow lenders
to KYC and depends on the on thecredit lines and the
(44:47):
jurisdiction of the borrower. And are the timelines for
borrowers and lenders typically different?
So kind of like my naive assumption would be that as a
borrower kind of I prefer longertimelines, whereas as a lender
kind of I want the, I want to beable to kind of recall my loan
(45:09):
kind of like from week to week. Do do you see this in practice?
Yeah, we definitely do see that in in practice, I would say it's
pretty much a process of findingkind of a credit market
equilibrium in the sense that wedo receive requests from
borrowers to have longer duration of these kind of loans
(45:32):
and on the other side lenders ask for shorter durations in
order to be able to recall funds.
What we saw is that using a marketplace approach to that, so
the borrower can propose different duration and let the
lenders feel the the credit linethat they prefer to have makes
(45:54):
sense. Of course, like the the behavior
that we see is that usually borrowers prefer to have longer
duration, but for longer duration they know that they
have to increase the interest rate.
So it's kind of like having the borrowers bidding and providing
these these these different terms to the lenders.
(46:16):
And then the lenders find an equilibrium and find a sort of
like natural deployment into thevaults that they respect, that
they respect the risk reward profile and also duration that
they want. So there is kind of a process of
finding the equilibrium at the beginning where the borrower
proposed different duration and different interest rate models
(46:39):
and the lenders pick and choose the one that they prefer.
And yeah, having these kind of like marketplace approach allows
these allows us to have like multiple proposals from a
borrower, lenders decide which one is better for them and
naturally than the borrower's keep the one with more liquidity
into that it that is being allocated into it.
(47:02):
OK. You also kind of briefly touched
upon this earlier, but kind of there's also this synthetic
dollar product that you have. Walk us through that.
Yeah. So we decided to introduce these
kind of syntactic yield bearing asset into built on top of the
(47:23):
credit lines because well on on one side we we wanted to create
a sort of like index of this kind of credit lines.
What we observed is that some institutional allocators wants
to control their credit exposure.
So they want to create their owncredit allocation.
(47:45):
And so they pick and choose different credit lines with
different allocations and that's, that's, that's their own
decision. But some other like
institutional allocators actually wants to have exposure
to pretty much a diversified basket of credit lines and they
don't want to really like trigger and control all and, and
(48:05):
the, and the monitor all the exposure that they have.
They're just fine with all the borrowers and they want to have
exposure to all the borrowers. And so on one side, USP and
SUSP, which is the name of the synthetic dollar and yield
bearing synthetic dollar that wehave allows to have a basket of
asset to access a basket of credit lines instead of having
(48:29):
to pick and choose all the credit lines.
And, and on the other side, actually this creates a way for
less sophisticated investors to access these kind of credit
exposure. You can see that as a sort of
like, you know, ETFs compared topick and choose stocks and build
(48:49):
your own stocks portfolio. And so the way that that USP and
SUSP works is that you deposit USBC or in the future USDT and
USDS for example. And these table currents get
allocated across a basket of credit lines, which currently we
proposed an initial allocation mechanism which is kind of hit
(49:13):
everything. Also the expertise that would
build with idle finance and the yield aggregation with idle
finance. But in this way, in in this
instance, it takes the borrower credit warning us the duration
of the loan, the interest rate model.
It creates A diversified allocation across this kind of
borrowers and also always keeps a reserve that is highly liquid
(49:37):
to media to being able to meet withdrawal requests from the
from the stable coin. And so this gives access also to
a less sophisticated, but also to, I would say a permission
less kind of audience because having these kind of synthetic
dollar and yield bearing synthetic dollar allows us to
(50:00):
apply the, let's call it circle model.
You know, if you want to have USDC and mint or redeem USDC,
you need to be KYC. Do you need to go through circle
on boarding process? And then you're able to mean
time redeem USDC against dollars.
But you can actually acquire or sell USDC on secondary markets
(50:20):
on set on Nexus, for example. And these this is something that
we can we can apply as well to USB and susp.
So permission it's user that doesn't want to go through KYC,
they can acquire USB in unit swap curve balancer, for
example, stick it and then eventually sell it back into
(50:42):
unit swap balancer or curve. So these allows also to broaden
the audience and the access to this kind of crediting next also
to a permission kind of audienceand not only to a permission
kind of audience. But I, I have several follow-ups
here. So the first one being kind of
like a mechanical 1. So seeing that I can kind of
(51:07):
deposit into this pool anytime, kind of how do you make sure
that the size of the pool is commensurate with kind of the
borrow volume that you see? You're saying that since you can
deposit into USB whenever you want, how do we match that with
the borrowing request on the other side?
(51:28):
That's a really good question. On the current scale of USB and
SUSB, that's not being an issue.So we have more, we have more
borrow demand. Exactly.
We do have more borrowing demandat the moment, but it's an
aspect that we have been thinking about.
And I think that what fits well is that we can also integrate,
(51:48):
you know, blue chip defy yield sources as a way to park this
kind of capital if there is not enough borrowing demand, like
for example, deploying into SUSDS.
So the old DSR from Maker DAO orinto Ave.
(52:10):
USDC Ethereum pool, which are kind of, you know, blue cheap
to. The idle DAO exactly.
OK. Second question, kind of
previously kind of in terms of legal defensibility.
So kind of previously kind of I can see how you say, OK, we're
just we're we're just kind of the matchmaker here kind of like
(52:33):
this is a 1 to one relationship.But kind of like with USP,
arguably you're offering kind ofan investment product, right?
Are you worried about that? So how the legal clarity around
that still to be shaped, I wouldsay, but yeah, we are beat like
we're we're we're thinking around that land like we don't
(52:56):
want to completely being like investment advisors I would say
on this kind of thing because ofcourse, yeah, like we deal
aggregators if there is a central team, a central entity
that just proposed the allocation.
So you end up to be an investment advisor.
(53:17):
And that's kind of borrowing a concept from what we build also
with adult finance, which is that we the allocation mechanism
for the USBC or USDT or USDS that we that are back in USP,
it's open. So essentially Pareto, we don't
(53:39):
have the A token live right now.It's going to be, it's going to
be released because the governance process of a rate to
Dow in this case would be able to also elect credit allocator
managers. So we're not going to be the
only one proposing a location, but there would be up in, in our
(54:00):
idealistically speaking, a network.
Marketplace again for different investment strategies.
Exactly, exactly that checks out.
OK, So USP currently maybe I, I checked this morning and didn't
seem terribly liquid, right. So kind of what, what's, what's
going on there? So kind of like what's the
(54:20):
what's the rate of return you'recurrently offering and why is it
not attractive enough for users?I would say so we currently have
around 4 million, four, 5 million into into USPI.
Think it's still taking a bit oftime to bootstrap the liquidity
over there because what we are missing in our opinion is the
(54:45):
additional use cases we integrations with like over
collateralized protocols or pandals.
So I would say that like currently, the interest rate on
SUSP is around 1112%. And, and it's really like
depending on, on the borrowers that we have.
Currently, we do have like 3 borrowers integrated within
(55:08):
SUSP. And we need to expand that in
order to make sure that we can one side to diversify the
exposure more for this kind of index, but also being able to
allocate to higher yields in order to become more attractive
in general for the market. I would say that the major
problem that we found out is to,you know, bootstrap the
(55:30):
integration. So, so being able to use SUSP as
a collateral in more for Oiler or other for example, create a
more liquid fixed rate facility.So, for example, in Pando, being
able to to have these kind of fixed rate.
And of course, I mean, we're we're still early in the
(55:51):
development of SUSP and I think we can make more, you know, we
can, we can push it more on the visibility side of things wiki,
which is something that we haven't done a lot yet.
But because we wanted to have all the infrastructure ready and
tested out and bottle tested, I would say.
So I would say it's not really about the interest rate because
(56:14):
if you look at I would say Athena for example, what is
right now Athena should be around like 5-6 percent.
If I remember correctly. They are overly allocated also
to like USDS. So it should be quite similar to
the SSR rate. So it should be around that.
So we're kind of like we're giving double of the of the
(56:36):
interest rate of that. So I don't see the interest rate
as a major problem. I see more like being able to
integrate the USP and SUSP across other protocols, which is
something that we're doing. We started with Euler a couple
of weeks ago and bootstrapping that we're going to push with
more for. Getting things listed as
collateral is is it's it's a pain.
(56:58):
Yeah, yeah, it is. But I mean it, it makes sense.
It should be kind of like a. It should be a pain but.
Exactly, exactly. Otherwise, it's it's hiring the
the the risk for defines I don'tknow and but also like pushing
on visibility and education is something that we're going to do
in the next month, which would be helpful to understand what is
(57:20):
USP, what is SUSP, what where the yield is coming from.
And these we expect will bring also more liquidity and more
scale up scale to the to USP andSUSP.
OK. Credit in some sense is more
opaque than most D5 activity, right?
(57:41):
Because kind of like it, it heavily depends on off chain
books that you don't necessarilyhave access to as as a user.
How do you make this compatible?Or do you make this compatible
with the real time transparency that people expect from D5?
That's a that's a great point. And so the currently it's
(58:10):
difficult to make these kind of process really like comparable
to the real time automation and monitoring of D5.
But we need to go there. I mean, that's like we we need
to improve from, you know, traditional rule on that often
rely on, you know, quarterly monthly reports to verify bar
(58:32):
World Health and and you know, this creates also a risk lag
because, you know, you might find out that find out too late
that a covenant was breached, for example.
So yeah, like ZK, TLS, implementing ZKTLS would be the
first step towards that, towardssome more automated monitoring
(58:54):
process. And also being able to have a
diversified access to different data sources from the borrower
allows us to like verify from multiple data sources that the
borrower ELF is there compared to just a single like data
(59:15):
source or a single auditor that makes the the auditing process
on the borrower. So I would say that like one of
the current implementation that we have in mind to to solve
these is to create a sort of like this kind of ZKTLS model
where the borrower can attach multiple data sources with
(59:36):
different even like frequency ofdata.
But like to try to be as close to the real time monitoring
process that we have in D5 and and so like monitoring and
automating this process for the borrower so they don't have to
provide manual reports and monthly or quarterly reports.
(01:00:00):
It's kind of a step towards going close to the real time
monitoring that we have in default.
Yeah. So my worries primarily about
malicious behaviour. So I do agree that kind of all
kinds of unfortunate business consequences and so on.
(01:00:20):
I think kind of like this is well quote by this, I'm not a
finance person, but kind of I know that kind of like if I
needed to kind of make books look good, depending on kind of
like I, I would book things different ways or kind of like I
would delay the booking or I would kind of I think there's
different ways of kind of fudging the books, right.
(01:00:42):
So that kind of a crisis is not immediately apparent.
Do you have any rails against truly malicious borrower
behavior? Yeah, I would say not relying
solely on proprietary data sources.
So I would say like getting financial statements, bank
(01:01:05):
record, the trading activities in general to form this kind of
borrower reputation shouldn't besomething that a borrower itself
is providing, but it's somethingthat we fetch from that, that
the sources that are third parties.
And for example, it's a read only API from a checks account.
(01:01:26):
So the borrower itself cannot really like manipulate this kind
of data. It would be the checks that
should be manipulate. Of course there's an assumption.
But they can open a second account, right?
So kind of like I can have one clean finance account and open a
second account in the same name,right?
Yeah. But the when you're opening this
(01:01:46):
kind of credit line, you're attaching like the either like,
like all the funds that you are borrowing out for the from the
credit line should be verified that goes to that specific
checks account and all the activity around that checks
account is what we are monitoring.
(01:02:07):
So the collateral that you're using the the kind of trading
activity that you're doing from that account is what matter for
the lender. So I would say these kind of
like credit line should be getting a mix between other
accounts, but should be related to a specific checks account
(01:02:30):
from the borrower side. And in general like the way that
we are also structuring from AI would say legal perspective the
the credit lines is that they are SPV based.
So they're also bankruptcy remote from the let's say main
company as well. OK, so you're.
Trying to ring fence as much as possible.
(01:02:51):
Exactly. Exactly.
So if the Pareto interface and kind of like the centralised
entities behind it kind of disappeared this afternoon,
could I still unwind my loan or redeem my USP just by
interacting with smart contracts?
Or do I kind of rely on you guysto kind of provide this service
(01:03:13):
to me? No, it would be possible.
The way that we made the protocol itself, especially on
USB and SUSP side is that there can be a way for the Dow itself
to change, for example, the meanadmin controls for the
allocation mechanism. For example, that also allows to
(01:03:36):
let's say we call back all the borrowers, all the funds from
the borrowers and we pay all theUSB token holders.
So if we call it, we have been call it in this kind of test
with our legal adviser the Bahamas test.
So if all the team decide to go to Bahamas and just don't build
(01:03:58):
the protocol anymore, we need tohave a way to let the community
and the token holders to be ableto change the parameters and to
change the admin controls, recall all the, borrow all all
the loans and repay back the andrepay and repay back USP and
SUSP. So yeah, we have been thinking
about that and since the beginning, the protocol is made
(01:04:20):
to be open in a way that it doesn't only rely on the core
team. Maybe as as a closing question,
if you look at the on chain private credit market and Pareto
in particular, what does successfor you guys look like in the
(01:04:40):
next two to three years? Nice, Great question.
In the sense that of course the main metric that we're using in
D5 is TBL. So that's kind of a North star
for all the D5 projects and that's what we need to what what
we're focused on growing over time and that comes of course
(01:05:03):
from creating a more and more diversified marketing
marketplace. So what I see as a growth driver
and potential expansion for operator in the next 2-3 years I
would say is to gradually introduce known crypto related
(01:05:23):
borrowers. We want to have a way to give a
whales to lenders and equity providers in general to not rely
only on borrowers that are related to the crypto market
because then depends on depending on the market cycle,
you're completely subject to themarket cycle.
And so the first driver would beto expand into more corporate
(01:05:47):
bonds, corporate loans, kind of credit lines, So introducing
kind of like fintech companies or cross-border payment
facilities, credit card companies, accounts receivable
companies, still related to finance and fintech, but that
would be the first expansion. And so creating this kind of
(01:06:09):
more and more diversified marketplace that would allow us
to grow USP and SUSP diversification power and allow
us to work with the larger basket of borrowers, borrowers
capabilities and loans in general to grow, to grow also
USP and SUSP. So I would say 2-3 years we
(01:06:30):
onboard new kind of corporate borrowers, we onboard new
curators that in our vision are also like traditional
institution that bring their ownunderwriting process and due
diligence process on chain without having to build
everything from scratch. And of course that would be like
meeting ATVL that goes above 1 billion to like numbers that
(01:06:53):
we're more like, you know, credit market is a 15 trillion
market all around the world. So we do have a lot of room of
expansion here in DFA. So like our first like milestone
is 1 billion of TDL, but then weneed to think bigger with like
this kind of number and we need to grow from there.
Cool, Mateo, where do we send people to find to find out more
(01:07:18):
about Pareto and kind of potentially borrow or lend some
money with you guys? I would say let's definitely
direct users and the audience that is interested to learn more
about Pareto to Pareto dot credit.
That would be the main point of access where you can access the
dashboard so you can see all theballs.
(01:07:39):
Read about the borrowers that wehave, read about the strategies
that they're running and understand more also about USP
and SUSP. And then I would say if they
want to also keep up with the announcement that we make, which
lately are a lot actually to follow us on Twitter at Pareto
dot Credit. You can find all the links at
(01:08:00):
Pareto dot Credit for Twitter aswell to look at all the
announcement that we're making and also to read the blog post
and the articles that we made with our partners.
Perfect. Thank you so much for coming on
Mateo. It's been a pleasure chatting
with you. Thank you Likewise.
Arbitrage opportunities are now like always available and so the
funds that we were using to deploy into the arbitrage bots
were idle for some time. So we're like, OK, we need to
find a way to make that more efficient.
In 2022, we realized that most of the customer base for idle
finance was traditional funds are locating across different
(00:21):
strategies. And one of the major feedback
that we received at that time was that you'll go to the
optimization is great, but we need to take risk and risk
diversification. So at that time we decided to
introduce yield the tranches credit lines can be really
customized from underwriting terms, covenants to compliance
(00:42):
requirements. This is something that like for
example, Maple or Centrifuge is a bit limited.
I would say the credit lines that we have are creation based.
So we're not the only one that are underwriting these kind of
credit lines, but we opened up this process also to 3rd party
curators. Welcome to Epicentre, the show
(01:05):
which talks about the technologies, projects and
people driving decentralization and the blockchain revolution.
I'm Frederica ANZ and today I'm speaking with Matteo Pandolfi,
who is the Co founder of Pareto,a credit coordination protocol
that evolved out of Idledale in and is building on chain markets
for private credit. Before I talk with Matteo, let
(01:25):
me tell you about our sponsors this week.
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Matthew, it's super nice to haveyou on.
Really excited to be here. And yeah, thanks to the entire
(03:32):
Epicentre team for hosting this session.
To all of you for tuning in as well.
Cool, Matteo, tell us a little bit about yourself before we
dive into Pareto. What's your professional and
academic background? Yeah.
So before founding Pareto and aswe are going to go through in
(03:52):
more into details, Idle DAO backin the past, So my background is
in finance. So my academic career was pretty
much in finance and entrepreneurship.
So it's kind of like a good mix between that that brought me
into D5 this kind of like, you know, innovative market, but
still really related to finance before actually starting with
(04:15):
Pareto. And and I do doubt I was working
on financial risk analysis. So I was consulting with major
financial institution in Italy like it is a Sao Paulo for
example on their credit desk. So essentially do helping on the
underwriting process and due diligence process in order to
create these kind of like corporate bonds instrument for
(04:38):
these financial institutions. What drew you into the
blockchain space in in the firstplace?
That's a that's a really good question because I've been
brought into the D phase space by my other Co founder, our CTO
and who made me find out find out about Ethereum at first.
(05:02):
At that time, I'm actually, I was actually still a finance
student and we were exploring Ethereum.
We, he had background in computer science and with my
background in finance, we were thinking how we could build
something on Ethereum and the first instance of something that
(05:22):
would be old on chain was actually touching the arbitrage
and arbitrage bots. So you know what we know right
now as Med at that time was still called PGA price Gas
Auction. You know, so we started building
(05:43):
these kind of bots that we're looking on at discrepancies
between Chexys and Daxys and arbitrage in price across these
assets. And and yeah, so that that was
kind of like the first instance and the first way where I
realized that we could really create something that was
(06:05):
completely programmable and automatic and was really like
changing the way that I was usedto see finance.
Because, you know, from the academic, but even from the
professional background, I always swing like finance being
quite siloed. So all these kind of closed
system that we're all like communicating internally and not
(06:28):
to each other. And with the theorem, that was
like the first time that it was my ha ha moment is like, OK,
that that's really composable. And that's something that we can
build automation that that runs on chain and runs 24/7.
So that was the first time I really touched with my hands
(06:49):
Ethereum and, and the power of smart concerts.
And then, you know, I, I just got into the rabbit hole of
that. So we started exploring landing
protocols. That time there wasn't even a
other, it was only compound and the YDX.
And so then everything evolved into what brought us into idle
(07:11):
finance. And then those are way too later
on. When did you decide to kind of
discontinue the arbitrage part? That's a good point because idle
finance came out from a need that we had for the arbitrage
bots because actually, you know,arbitrage opportunities are now
(07:33):
like always available. And so the founds that we were
using to deploy into the arbitrage bots were idle for
some time. So we were like, OK, we need to
find a way to make that more efficient.
And so I would say that we proposed idle finance as a way
to optimize the yield on idle phones in 2019 on an Akatoner on
(08:00):
Git coin. So I think that was in 2019 when
we decided to focus completely on an idle finance and put aside
the arbiter spots. So I would say 2019, we started
the arbitrage bots back in 20/17/2018.
So, yeah, we ran that for a couple of years and then decided
(08:20):
to focus completely on IT finance.
And what's the rationale for kind of moving over purely a
return on investment kind of play or what, what were the
drivers here? Scalability, one of the major
drivers were scalability becausewith the arbitrage bots, we saw
(08:41):
that by increasing the AUMDTVL of the arbitrage bots, we were
actually reducing the margin of the funds for the arbitrage
opportunity. So we, we were really focused on
building something on Ethereum that could be scalable,
scalable, you know. So when we started implementing
(09:03):
the very first version of idle finance into our arbitrage bots,
we realized that we could manage1,000,000 to 100 million and the
result was pretty much the same in terms of return of the
capital because the kind of optimization could be applied
with more capital compared to arbitrage bots.
So building something that was able to be scalable and to be
(09:29):
offered also to a more broader audience, I would say because
you know, arbitrage in general is quite a niche product or in
general it's a niche concept that not not a lot of users
grasp or understand in general. Instead like automating the
yield generation optimization, yield optimization, yield
(09:52):
generation across different protocols was really something
that was clicking with Defy users at the time.
So I would say, yeah, scalability and also being able
to offer this kind of product toa broader audience and let let
them understand the product to abroader audience was the main
driver of us focusing completelyon idle finance.
(10:15):
There were quite a few projects that kind of centred around that
right, kind of, I mean, in the very beginning it was things
like Insidap that kind of let let you kind of switch funds
between make and ABBA memories serves correctly and then kind
of more sophisticated things like come came along.
(10:40):
So how? How did Idle Dao kind of fit
into that landscape? So I think we had the lucky and
the opportunity. We have been lucky and we had
the opportunity also to meet like instead of team or even
Andre Cronzer from why you're even before like them building
(11:01):
out their products. I would say instead up is being
really great that they're they have been really great builders
and but never really focused on yield optimization and
automation of that yield optimization.
They were more focused on providing great tools for
borrowers that were using like for example, compound Ave. or
(11:25):
even Maker DAO. And I would say that instead of
falls more into the into into a different category compared to
idle finance, why you're earningidol finance are pretty much
similar. Actually, I remember a lot of
conversation in Telegram, we found that crunch at the
beginning on how to solve some like initial issues of yield
(11:47):
optimization. Like there was these ping pong
effect that we found out at the time that was affecting yield
aggregators, which means that essentially when when you move a
big chunk of the funds into our specific lending protocols, you
also influence the interest rateon that specific protocol.
(12:08):
So if you don't have control on the funds and a control on the
influence that you have on the interest rate, you might end up
into a ping pong effect, which means that you move all the
funds from protocol A to protocol B because protocol B
has the higher interest rate. But when you do that, you're
lowering the interest rate of protocol B and might become
(12:32):
lower than the protocol A. So the EEL aggregator sees that
the higher interest rate is again on protocol A and so you
move back the funds from protocol A.
So you see that you create thesekind of ping pong fat between by
moving and rebalancing funds. So we have been like talking a
(12:52):
lot and understanding what was the right, you know algorithm
and optimization mechanism to take all these.
Also, we have hundreds of cards at the beginning and then I
think why aren't did a great jobon, I would say the positioning
and narrative built around the yield aggregation.
(13:14):
At the time we were quiet, you know, for starting idle finance.
As I mentioned, we started with a GIT coin Akaton.
So we won that git coin Akaton and then we got accepted into a
an accelerator in New York with consensus.
And so we kind of took a more professional approach, I would
(13:36):
say or institutional approach tothe product.
Instead, I think we're at very early phase of wire and they
took more addition approach, which turned out to be the right
narrative. So I, I, I, I would like to say
that that they did, they did a great job on that side of
things. And of course then the dev team
(13:56):
and the community that formed around Wyern was really great.
So Congrats of course on Wyern and Andre Cronzo on
bootstrapping these at the beginning.
And I would say that we, we kindof at the end of the day, D5 was
a small market at the beginning.So the way that we were looking
at competitors, the beginning was, was more like competition
(14:21):
rather than pure competition. And so turned out that like one
of the major partners for idle finance goes wire in itself like
with some of the strategies and the goals that wire had at the
beginning like for USBCUSDT and I were based on, I do finance
(14:43):
yield optimization for a big chunk of the funds that they
were managing. So it turned out to be kind of
like a more a partnership ratherthan a competition with, with
wire and finance, for example. Of course, over time Defy market
evolved, become more sectorialized, the more you
know, professionalized and of course competition came out.
(15:07):
But I would say that early timesof Defy, I really enjoyed the
early time times in Defy becauseit was all about like
cooperating together and trying to build like, you know, all the
legal pieces that you could put together in the fight was really
about this. Santa Ben was really about
these, these philosophical you know, work together to grow the
(15:32):
market instead of competing, competing together to shrink the
market to each other. What ultimately kind of drove
the pivot from from Ida to Preto?
I would say that we've focused alot in for on on yield
optimization from since 2019 to I would say 2022.
(15:58):
In 2022, we realized that most of the customer base for idle
finance was institutional funds.So institutional funds are
locating across different strategies and one of the major
feedback that we received there at that time was that yield to
the optimization is great, but we need to take care of risk and
(16:20):
risk diversification. We need to mitigate risk for the
liquidity providers. So that time we decided to
introduce an additional product line and then an additional
primitive to the product suite which was yield the tranches.
So essentially A primitive that could where we could build the
senior and unit tranches, seniormedicine unit tranches on top of
(16:43):
potentially any defy yield source.
And, and so this this was a response to these kind of
feedback that we received and. And senior and junior trenches
in this context kind of being different rates of return for
different levels of risk, right?Or do you mean something else
(17:04):
by? That, no, no, that's correct.
That's correct. Essentially senior tranche is
the most protected vehicle investment world that is
protected by the junior tranche.So whatever there is a default
because of a knock or a loss of funds by the borrowers or these
kind of loss of funds, juniors are first, first loss absorption
(17:28):
layer. So they absorb the the loss on
behalf of the senior because of this risk that they bear they
receive also not here healed compared to compared to the
senior trenches. So we we introduced this
primitive, we started building around different kind of yield
sources and defy over collateralized landings taking
(17:48):
in order to understand or so where was the product market fit
for this kind of product line. And we realized that most of the
product market feed was on institutional credit.
At that time we were working with protocols like you know,
Maple or Clearpool that we're doing these kind of first
instances of institutional credit on chain.
(18:12):
And we saw that like equity providers and lenders were
really appreciating senior and unit tranches for institutional
credit lines because different credit funds, different hedge
funds or families offices had different views on these
specific institutional borrowers.
They were lending, they were lending too.
And so using senior unit branches allowed them to to
(18:37):
customize the risk reward profile of their entire credit
exposure of their entire credit portfolio.
So we realized that most of the product market fit was there,
but we received those a lot of feedback from both borrowers and
lenders that the credit infrastructure was not ready for
institutional needs. It was really kind of like mini
(19:00):
king compound and have a interest rate model which was a
pain for the borrower because itwas a single borrower that
wanted to with a wire valuable interest rate that operationally
speaking was terrible because they were looking more at the
interest rate that they were paying compared to doing their
(19:21):
own business. And in general the terms and the
underwriting process for this kind of institutional credit
lines of on chain at that time wasn't really customizable and
modular I would say. And so we realized most of the
problem market fit was there. There was room for improvements
(19:42):
on the credit space. So we, we said, OK, we need to,
we have a clear opportunity overthere and we wanted to build and
improve that space, that part ofthe D5, that D5 sector, let's
say. So that's pretty much how we
transition to, to Pareto. We started working a lot with
(20:03):
institutional credit lines and then we realized that we wanted
to improve the credit lines themselves, and that's how
essentially Pareto came out. Tell me more about the
institutional borrowers. So kind of what kind of
institutions where they that they were willing to kind of
borrow in this very competitive market on chain where kind of
(20:27):
you, you can go to your local bank and probably get a more
competitive loan? That's a good point because of
course they could access, you know could originate on credit
lines from a traditional bank. But the process of origination
takes a lot of time. Especially the kind of borrowers
(20:49):
that we're working with right now are pretty much, you know,
institutional funds that run delta neutral strategies or
market makers or prime brokerages.
And the process of originating aloan and managing that that loan
in a in a traditional way usually takes a lot of time and
(21:10):
resources from their team. And usually the teams are not
like really huge in. And so the, the overhead is
really tangible for the final borrower.
We, we recently actually run a use case with one of our major
borrowers which is Falcon X, oneof the leading market and prime
(21:31):
brokerages. And what came out of the use
case is that before using on chain credit lines.
So relying on traditional creditlines took around 80 hours per
month from their team to make sure that in order to originate
(21:53):
all the documentation was there and making sure that all the
lenders were aware of updated terms and also doing manual
reconciliation. What came out is that now with
the on chain credit line, they have to dedicate 2 hours per
month the same team. So operationally speaking, the
improvement for the borrower is really tangible.
(22:15):
There's a streamlining process that is happening on the
operational side of things. You know, on chain credit lines
are pretty much self operating compared to a traditional credit
line. So that's that's I would say the
the main advantage of operating announcing credit line.
(22:38):
And secondarily, I would say also being able to have a credit
position that is composable is abig advantage for, for this kind
of borrowers and a big advantage, I would say also for
the lenders at the end of the day, because, you know, being
able to use a credit position asa collateral in have a more for
(23:03):
Euler, for example, or create an, an interest rate swap
instrument using Pendo for the same credit position.
It's really powerful for the lenders.
So lenders have a better, let's say UX when lending to the this
kind of institution and interns borrowers are able to originate
(23:24):
more liquidity with these kind of credit lines and streamlining
the process of managing these kind of credit lines.
Cool. There's a couple of projects
kind of doing very similar things in this space, so kind of
the likes of Maple and Centrifuge and so on.
So how is your approach different?
(23:44):
Or is it different from your competitors?
So it, it is different. I think Centrifuge they started
like really early. So they did a great job at you
know, pioneering on that, on this kind of area.
And also Maple they they kind ofpivoted I remember back in maybe
(24:06):
it was 2020-2021 and then they focused completely on chain
credit. So both are doing great jobs on
expanding this kind of space. The main difference that we
wanted to implement since the beginning of Orator was to make
sure that we have a highly customizable protocol.
(24:28):
So credit lines can be really customized from underwriting
terms, covenants to compliance requirements.
And that's this is something that like for example Maple or
Centrifuge is a bit limited I would say.
But also and more more importantly, the credit lines
that we have are creation based.So we're not the only one that
(24:53):
are underwriting these kind of credit lines, but we opened up
these process also to 3rd party curators.
It's kind of if you look at whathappened with MARFO and other
over collateralized lending protocol in the past where they
introduced these kind of curation role that's essentially
the same that we're doing with arate to compare to other on
(25:16):
chain creditor credit products. This allows us to have again
more scalability for this kind of products because we're not
the only one underwriting new borrowers, but we have other
creators that can provide this kind of underwriting
capabilities. And also in our longer term
vision, the the curation based model allows us to work also
(25:41):
with, you know, kind of like traditional financial
institution. Instead we see more like
products like Maple or Centrifuge kind of a in house
underwriting company. So that, that, that, that's,
that's pretty much like the maindifference that we have.
It's an open curation based model versus all in hours
(26:02):
underwriting process. So underwriting is actually
really skilled task, right? So kind of like what it kind of
takes is kind of you need to understand the books of this
company, you need to understand the business prospects, you need
to kind of you need to calculatethe default risk and the
(26:24):
associated interest you need to charge in order to kind of break
even. Tell me how this process is
handed within Pareto and kind oflike how how you outsource this
to others. So with Pareto that that's a
really good point in the sense that on the writing is it
(26:47):
requires a lot of skills and also a lot of domain expertise.
I would say that's why it was westarted with you know,
institutional digital native funds.
So we're which are most familiarwith what we have been doing in
the past. So we completely understand the
kind of deployment and strategies that they're going to
(27:07):
apply to the credit lines that they're opening.
And of course, like the first step, it's pretty much like as
you mentioned, scan and review of the credit worthiness of the
borrower, which is given by analyzing the books and balance
sheets of these kind of borrowers, making sure there is
(27:29):
enough equity, for example, to be able to cover for any losses
to be. And I would say that like
there's been some experiment in underwriting in the past.
I'm not talking about Pareto, but in the past there's been
some experimenting underwriting small companies, for example,
which turned out to be a bit toomuch naive.
(27:52):
And, and so I would say that like in order to bootstrap and
grow this kind of credit space, we need to start with more solid
corporation and institution at the beginning just to make sure
that if I still get a good reputation on on credit side,
then it really depends on the kind of deployment.
For example, we work a lot with Maven 11 credit as a curator for
(28:18):
five connects. And what they do is pretty much
on the underwriting side of things for the credit line that
we have. There's been a lot of work
around the covenants that are around the credit line.
So the borrower has a certain kind of parameters and action
that they can do. And at the moment Maven 11 is
(28:39):
monitoring that. And we're doing the same for
other borrowers like Fasanara orBoston Trading, for example.
So it takes an initial screen ofthe wealthiness and and creative
worthiness of the borrowers and then it takes also monitoring
tools and monitoring capabilities for making sure
(28:59):
that the healthiness of the borrower is still there.
It's a bit manual at the moment,but our like goal in the next, I
would say year is to how to makethat process.
And actually just to introduce abit a new concept here.
But what we see as a really interesting implementation into
(29:22):
this kind of credit line is 0 knowledge technology.
Because what we what we realizedis that most of the information
and data that we need to monitorand review to make sure that
there is a good credit warnings for a borrower are sensible data
for the borrower. The borrower doesn't feel really
(29:43):
comfortable in making these kindof information completely
public, both for compliance reason, but also for like just
to be ahead of other competitorsand not to fall behind other
competitors. And what we saw is that like ZK
technology really fits well in these kind of area on monitoring
(30:04):
credit warnings and covenants for certain credit lines.
So I'll give you a really, really simple example of that.
Let's say that a credit line foran institutional borrower has
covenant, a covenant that says that the borrower can only
deploy funds into Tier 1 exchanges and no more than 25%
(30:25):
of the entire loan amount can bedeployed into a single exchange.
So the borrower doesn't want to expose all these kind of data
for all the reason that they have.
And by using ZK technology, moreprecisely ZKTLS, we essentially
encrypt these kind of data and what the lenders receive is a
(30:50):
proof of the fact that the covenant is respected and so
also that the the creditworthiness of the borrower
is still there. And, and in this way, we also to
make the process from a more traditional monthly reporting or
like a certificate of collateralon a specific custodian that is
(31:11):
issued. Then you need to completely rely
on a single data source. 2 The idea is to have the borrower
being able to plug in multiple data sources from centralized
exchanges, custodian accounts, bank accounts, even encrypt
these kind of data. So all the sensible information
are, are protected and it's privacy preserving for you by
(31:35):
final borrower. But the lenders on the other
side are able to get a proof of the fact that the covenants are
respected, that the borrower credit warning is is still good.
Is there some sort of second opinion for the underwriting?
So kind of do you have differentcompanies kind of look at the
same loans or kind of is, is the, is the underwriter in some
(31:59):
way incentivized or penalized ifthey kind of make a wrong
assessment? I mean, wrong assessment goes a
long way here, right? Because kind of like everyone
acknowledges from the get go that there is some level of risk
and kind of in order to kind of say whether something was
calculated correctly or not, kind of you would actually have
to run this experiment many times and kind of like you're
only running it once. So kind of, so kind of even if
(32:22):
kind of like there's only a 2% chance of default and that is
acknowledged kind of like those 2% can materialize, right.
So kind of how, how do you do quality assurance on the
underwriters? That's a good question because
we have been thinking about thisbecause of course like
underwriting process is not likemathematics something that
(32:45):
mathematically you can, you can always like be right.
So of course, overall, we, we could expect the faults in the
as, as, as we scale up these kind of credit protocols.
So we can now like say, no, OK, there won't be any defaults.
So we need to be all around mitigating rules for this kind
of default risk. And one way at the moment,
(33:07):
essentially each credit line canbe curated by a single curator.
So there's A1 entity that takes care of of on the writing and
monitoring the advance of a power.
But the guardrail and the essentially protection that we
embedded into it is that the curator is also a legal
(33:28):
representative for the lenders. So it doesn't take care only on
the under of the underwriting process and monitoring process
for the borrower. But in a case of a default, the
creator in this case is becomes the lender represent legal
representative towards the borrower.
So the incentive of course for the curator is to make sure that
(33:52):
they are doing the right calculation in order to get the
default risk and also to make sure that they didn't.
They don't underestimate this kind of default risk because
then they are responsible for any legal representation for the
lenders, which is actually another difference from other
(34:13):
protocols. Other protocols essentially, if
you if you are a lender and it happens to be the full, it's up
to you to go to court to, to file the bankruptcy, for
example, documents in order to get back usually multiple years
and proceed from the liquidationof the borrowers.
(34:35):
Instead in the setup that we have is the curator that is also
taking care of this process. So of course, because of that,
the curator is more incentivizedto do a proper due diligence and
proper monitoring of the borrower, even if it means to be
more conservative than than the usual process.
(34:58):
But that that's the initial incentive.
Of course, this is not the finallike set up, I would say over
time, for example. Yeah, having multiple creators
with kind of different roles, like one on the writing initial
and doing the due diligence, another one doing monitoring,
just to have like multiple parties that provides an
(35:22):
understanding of the borrowers makes completely sense.
But yeah, at the current time, that would be the incentive for
the curator to provide a proper due diligence and provide a
proper underwriting process. What happens if the underwriter
just doesn't do it then So kind of like say the the borrower
defaulted and kind of like thereare 20 lenders that kind of now
(35:45):
I as an underwriter meant to represent it.
I just don't do it. And I kind of just kind of step
out of my business and let it default start up a new
underwriting business and kind of start from square one until
because kind of defaults are kind of, if you, if you played
in any way kind of correctly, defaults are rare, right?
(36:07):
Kind of like default should be rare.
So if if I kind of if I can run my business until the 1st
default unfettered is isn't, am I not incentivized to do that?
Yeah, I see. I see what you're saying.
But actually there would be liabilities for the underwriter
as well. So of course, I, I see what
(36:30):
you're saying that you just likeclose out the underwriting
business on, on that entity and you open up a new one, but you
completely destroy the reputation of your underwriting
process. So these are known entities, you
know who they are. And kind of like, there's kind
of like a soft reputation, even if it's not on chain, kind of
you, you know? Yeah.
(36:50):
I would say, yeah, yeah, Institutional likings are on
chain. Credit is big given we work with
a spectrum of collateral becausewe could, we can enable
borrowers putting collateral of course, but we can work with our
full spectrum of collateral fromkinda standard collateralization
(37:12):
ratio to even 0 collateral. I think it's all about
reputation like all these kind of lending is also reputation
based lending. So I see a lot of also
borrowers, institutional borrowers at the moment that are
looking at this kind of under collateralized credit lines as a
way to to build a credit score that is like it's, it's on
(37:37):
chain. And all the interest payments
that they make, all the repayments of the notional that
they make is essentially something that builds a credit
score that is on chain and it's immutable on chain.
So I would say that one like a abig part of the of managing this
kind of credit lines is also making sure that the borrower
(38:00):
but also the underwriters has this kind of reputation based
way of working on the credit lines.
OK, so I think we kind of cover the how and the the why and kind
of the what. Maybe let's let's look at the
how. So kind of like if, if you if,
if you could walk me through themechanics kind of like from the
(38:24):
lender's deposit to kind of how the credit gets allocated and
how the yield is paid out and how often kind of these loans
are novated and so on. I think kind of that that that
would that would go a long way. No, Yeah, absolutely.
So what the process of lending out to to a specific credit line
(38:47):
we are from a UX perspective, I would say we made sure to make
it as see as much similar to thenormal defy UX as possible.
So initial like, you know, liquid providers could feel it,
they were still like in Defy. Of course there are some
compliance requirements that we need to respect depending on the
jurisdiction of the borrower. So all the credit lines at the
(39:10):
moment have a KYC process that you need to go through as a
lender before being able to deposit.
And so after that, like you can,of course, you can access all
the data around the borrower or all the data around the
performance of the vault and make sure that disrespect the
(39:30):
risk reward profile that you want to have for your, for your
credit exposure. The kind of like the duration,
the and the interest rate modelsof the credit lines really
depends on the borrowers. Because as I mentioned before,
we build the protocol around customizability and modularity
(39:54):
of the credit lines. So the borrower can really
decide how the interest rate model behaves and what is the
duration or the covenants of theof the credit line.
So just to give you some example, just to let also the
audience here understand, betterunderstand how the current
(40:15):
credit lines works, we do have usually like weekly to monthly
duration of these kind of creditlines.
At the end of which the borrowerneeds to pay the interest rate,
the lenders can request withdrawals and, and borrowers
have like different duration from weekly to monthly to
(40:37):
satisfy this kind of withdrawal request.
And, and so the interest rate model, for example, it's decided
be with the underwriter, but also the borrower.
So for example, Fazanara, which is one of the borrowers that we
have wanted to have flexible on chain credit line that we done
(41:00):
interest rate that was based on an external benchmark.
So Fazanara Digital is currentlyrunning a deal delta neutral
strategy doing you know a funding rate arbitration
strategy. And so they wanted to have the
on chain credit line attached tothe performance of these kind of
funding rate strategy. So we took a a benchmark which
(41:23):
is the open interest rate at theBTC funding rate times 1.3,
which turned out to be 88% correlated to 1000 hour
performance. And so we integrated this kind
of interest rate model into the into the credit line.
So that's how we can like the customizability is really, it's
(41:49):
something that can be used within credit lines and and
really changes the way the credit lines work.
For example, instead with FalconX is pretty much traditional.
It's a fixed rate facility with 30 days callback period.
So it mimics a bit more the the traditional kind of credit lines
that they already had, but with the advantage of now for
(42:12):
example, file connects credit line is being used as a
collateral in Morpho. So lenders can also, you know,
perform looping strategies on Morpho or even just you don't
use it as a collateral to borrowout to USDC and deploy USDC
somewhere else. So you bring also more capital
efficiency. So the process of deploying
(42:32):
finance is pretty similar to Defy standard.
I would say UX apart from the KYC process different story also
for USP and SUSP which is the yield being stable kind that we
we put on top of that. But I'm going to stop here.
Not sure if you have other question on the on the credit
license have. Yes, you said there is compiled
(42:55):
3 KYC, is that just for the lender or also for the just for
the borrower or also for the lender?
Also for the lender, yeah. And what are the requirements on
the lender here? It depends on the borrower in
the sense that different jurisdictions are required,
different level of of KYC. For example with Falcon X it's
(43:16):
you as based and we do let's saytraditional KYC process.
So getting all the documentation, source of founds,
corporate documentation if it's an entity that is deploying
found into Falcon X. And so I would say it's more
traditional kind of KYC process.We introduced those in
(43:38):
partnership with our KYC provider, which is curing the
proof of KYC way for some of thesome of the borrowers are fine
with it. And essentially what we do is
letting the user logging into our application via Coinbase,
Binance, OK X accounts and theseallows us to henerate the KYC
(44:04):
level that they have on those centralized exchanges.
So user doesn't have to redo again all the KYC process with
these kind of KYC process that we have.
But they just need to log in with Coinbase, Binance or KX or
even we do have Revolute for example, as a possibility to log
(44:26):
in with and we hina read and we get a sort of like proof of KYC
and that's for some jurisdictionenough for the borrower to be
respecting the compliance requirement that they have.
So those are the true current ways that we can allow lenders
to KYC and depends on the on thecredit lines and the
(44:47):
jurisdiction of the borrower. And are the timelines for
borrowers and lenders typically different?
So kind of like my naive assumption would be that as a
borrower kind of I prefer longertimelines, whereas as a lender
kind of I want the, I want to beable to kind of recall my loan
(45:09):
kind of like from week to week. Do do you see this in practice?
Yeah, we definitely do see that in in practice, I would say it's
pretty much a process of findingkind of a credit market
equilibrium in the sense that wedo receive requests from
borrowers to have longer duration of these kind of loans
(45:32):
and on the other side lenders ask for shorter durations in
order to be able to recall funds.
What we saw is that using a marketplace approach to that, so
the borrower can propose different duration and let the
lenders feel the the credit linethat they prefer to have makes
(45:54):
sense. Of course, like the the behavior
that we see is that usually borrowers prefer to have longer
duration, but for longer duration they know that they
have to increase the interest rate.
So it's kind of like having the borrowers bidding and providing
these these these different terms to the lenders.
(46:16):
And then the lenders find an equilibrium and find a sort of
like natural deployment into thevaults that they respect, that
they respect the risk reward profile and also duration that
they want. So there is kind of a process of
finding the equilibrium at the beginning where the borrower
proposed different duration and different interest rate models
(46:39):
and the lenders pick and choose the one that they prefer.
And yeah, having these kind of like marketplace approach allows
these allows us to have like multiple proposals from a
borrower, lenders decide which one is better for them and
naturally than the borrower's keep the one with more liquidity
into that it that is being allocated into it.
(47:02):
OK. You also kind of briefly touched
upon this earlier, but kind of there's also this synthetic
dollar product that you have. Walk us through that.
Yeah. So we decided to introduce these
kind of syntactic yield bearing asset into built on top of the
(47:23):
credit lines because well on on one side we we wanted to create
a sort of like index of this kind of credit lines.
What we observed is that some institutional allocators wants
to control their credit exposure.
So they want to create their owncredit allocation.
(47:45):
And so they pick and choose different credit lines with
different allocations and that's, that's, that's their own
decision. But some other like
institutional allocators actually wants to have exposure
to pretty much a diversified basket of credit lines and they
don't want to really like trigger and control all and, and
(48:05):
the, and the monitor all the exposure that they have.
They're just fine with all the borrowers and they want to have
exposure to all the borrowers. And so on one side, USP and
SUSP, which is the name of the synthetic dollar and yield
bearing synthetic dollar that wehave allows to have a basket of
asset to access a basket of credit lines instead of having
(48:29):
to pick and choose all the credit lines.
And, and on the other side, actually this creates a way for
less sophisticated investors to access these kind of credit
exposure. You can see that as a sort of
like, you know, ETFs compared topick and choose stocks and build
(48:49):
your own stocks portfolio. And so the way that that USP and
SUSP works is that you deposit USBC or in the future USDT and
USDS for example. And these table currents get
allocated across a basket of credit lines, which currently we
proposed an initial allocation mechanism which is kind of hit
(49:13):
everything. Also the expertise that would
build with idle finance and the yield aggregation with idle
finance. But in this way, in in this
instance, it takes the borrower credit warning us the duration
of the loan, the interest rate model.
It creates A diversified allocation across this kind of
borrowers and also always keeps a reserve that is highly liquid
(49:37):
to media to being able to meet withdrawal requests from the
from the stable coin. And so this gives access also to
a less sophisticated, but also to, I would say a permission
less kind of audience because having these kind of synthetic
dollar and yield bearing synthetic dollar allows us to
(50:00):
apply the, let's call it circle model.
You know, if you want to have USDC and mint or redeem USDC,
you need to be KYC. Do you need to go through circle
on boarding process? And then you're able to mean
time redeem USDC against dollars.
But you can actually acquire or sell USDC on secondary markets
(50:20):
on set on Nexus, for example. And these this is something that
we can we can apply as well to USB and susp.
So permission it's user that doesn't want to go through KYC,
they can acquire USB in unit swap curve balancer, for
example, stick it and then eventually sell it back into
(50:42):
unit swap balancer or curve. So these allows also to broaden
the audience and the access to this kind of crediting next also
to a permission kind of audienceand not only to a permission
kind of audience. But I, I have several follow-ups
here. So the first one being kind of
like a mechanical 1. So seeing that I can kind of
(51:07):
deposit into this pool anytime, kind of how do you make sure
that the size of the pool is commensurate with kind of the
borrow volume that you see? You're saying that since you can
deposit into USB whenever you want, how do we match that with
the borrowing request on the other side?
(51:28):
That's a really good question. On the current scale of USB and
SUSB, that's not being an issue.So we have more, we have more
borrow demand. Exactly.
We do have more borrowing demandat the moment, but it's an
aspect that we have been thinking about.
And I think that what fits well is that we can also integrate,
(51:48):
you know, blue chip defy yield sources as a way to park this
kind of capital if there is not enough borrowing demand, like
for example, deploying into SUSDS.
So the old DSR from Maker DAO orinto Ave.
(52:10):
USDC Ethereum pool, which are kind of, you know, blue cheap
to. The idle DAO exactly.
OK. Second question, kind of
previously kind of in terms of legal defensibility.
So kind of previously kind of I can see how you say, OK, we're
just we're we're just kind of the matchmaker here kind of like
(52:33):
this is a 1 to one relationship.But kind of like with USP,
arguably you're offering kind ofan investment product, right?
Are you worried about that? So how the legal clarity around
that still to be shaped, I wouldsay, but yeah, we are beat like
we're we're we're thinking around that land like we don't
(52:56):
want to completely being like investment advisors I would say
on this kind of thing because ofcourse, yeah, like we deal
aggregators if there is a central team, a central entity
that just proposed the allocation.
So you end up to be an investment advisor.
(53:17):
And that's kind of borrowing a concept from what we build also
with adult finance, which is that we the allocation mechanism
for the USBC or USDT or USDS that we that are back in USP,
it's open. So essentially Pareto, we don't
(53:39):
have the A token live right now.It's going to be, it's going to
be released because the governance process of a rate to
Dow in this case would be able to also elect credit allocator
managers. So we're not going to be the
only one proposing a location, but there would be up in, in our
(54:00):
idealistically speaking, a network.
Marketplace again for different investment strategies.
Exactly, exactly that checks out.
OK, So USP currently maybe I, I checked this morning and didn't
seem terribly liquid, right. So kind of what, what's, what's
going on there? So kind of like what's the
(54:20):
what's the rate of return you'recurrently offering and why is it
not attractive enough for users?I would say so we currently have
around 4 million, four, 5 million into into USPI.
Think it's still taking a bit oftime to bootstrap the liquidity
over there because what we are missing in our opinion is the
(54:45):
additional use cases we integrations with like over
collateralized protocols or pandals.
So I would say that like currently, the interest rate on
SUSP is around 1112%. And, and it's really like
depending on, on the borrowers that we have.
Currently, we do have like 3 borrowers integrated within
(55:08):
SUSP. And we need to expand that in
order to make sure that we can one side to diversify the
exposure more for this kind of index, but also being able to
allocate to higher yields in order to become more attractive
in general for the market. I would say that the major
problem that we found out is to,you know, bootstrap the
(55:30):
integration. So, so being able to use SUSP as
a collateral in more for Oiler or other for example, create a
more liquid fixed rate facility.So, for example, in Pando, being
able to to have these kind of fixed rate.
And of course, I mean, we're we're still early in the
(55:51):
development of SUSP and I think we can make more, you know, we
can, we can push it more on the visibility side of things wiki,
which is something that we haven't done a lot yet.
But because we wanted to have all the infrastructure ready and
tested out and bottle tested, I would say.
So I would say it's not really about the interest rate because
(56:14):
if you look at I would say Athena for example, what is
right now Athena should be around like 5-6 percent.
If I remember correctly. They are overly allocated also
to like USDS. So it should be quite similar to
the SSR rate. So it should be around that.
So we're kind of like we're giving double of the of the
(56:36):
interest rate of that. So I don't see the interest rate
as a major problem. I see more like being able to
integrate the USP and SUSP across other protocols, which is
something that we're doing. We started with Euler a couple
of weeks ago and bootstrapping that we're going to push with
more for. Getting things listed as
collateral is is it's it's a pain.
(56:58):
Yeah, yeah, it is. But I mean it, it makes sense.
It should be kind of like a. It should be a pain but.
Exactly, exactly. Otherwise, it's it's hiring the
the the risk for defines I don'tknow and but also like pushing
on visibility and education is something that we're going to do
in the next month, which would be helpful to understand what is
(57:20):
USP, what is SUSP, what where the yield is coming from.
And these we expect will bring also more liquidity and more
scale up scale to the to USP andSUSP.
OK. Credit in some sense is more
opaque than most D5 activity, right?
(57:41):
Because kind of like it, it heavily depends on off chain
books that you don't necessarilyhave access to as as a user.
How do you make this compatible?Or do you make this compatible
with the real time transparency that people expect from D5?
That's a that's a great point. And so the currently it's
(58:10):
difficult to make these kind of process really like comparable
to the real time automation and monitoring of D5.
But we need to go there. I mean, that's like we we need
to improve from, you know, traditional rule on that often
rely on, you know, quarterly monthly reports to verify bar
(58:32):
World Health and and you know, this creates also a risk lag
because, you know, you might find out that find out too late
that a covenant was breached, for example.
So yeah, like ZK, TLS, implementing ZKTLS would be the
first step towards that, towardssome more automated monitoring
(58:54):
process. And also being able to have a
diversified access to different data sources from the borrower
allows us to like verify from multiple data sources that the
borrower ELF is there compared to just a single like data
(59:15):
source or a single auditor that makes the the auditing process
on the borrower. So I would say that like one of
the current implementation that we have in mind to to solve
these is to create a sort of like this kind of ZKTLS model
where the borrower can attach multiple data sources with
(59:36):
different even like frequency ofdata.
But like to try to be as close to the real time monitoring
process that we have in D5 and and so like monitoring and
automating this process for the borrower so they don't have to
provide manual reports and monthly or quarterly reports.
(01:00:00):
It's kind of a step towards going close to the real time
monitoring that we have in default.
Yeah. So my worries primarily about
malicious behaviour. So I do agree that kind of all
kinds of unfortunate business consequences and so on.
(01:00:20):
I think kind of like this is well quote by this, I'm not a
finance person, but kind of I know that kind of like if I
needed to kind of make books look good, depending on kind of
like I, I would book things different ways or kind of like I
would delay the booking or I would kind of I think there's
different ways of kind of fudging the books, right.
(01:00:42):
So that kind of a crisis is not immediately apparent.
Do you have any rails against truly malicious borrower
behavior? Yeah, I would say not relying
solely on proprietary data sources.
So I would say like getting financial statements, bank
(01:01:05):
record, the trading activities in general to form this kind of
borrower reputation shouldn't besomething that a borrower itself
is providing, but it's somethingthat we fetch from that, that
the sources that are third parties.
And for example, it's a read only API from a checks account.
(01:01:26):
So the borrower itself cannot really like manipulate this kind
of data. It would be the checks that
should be manipulate. Of course there's an assumption.
But they can open a second account, right?
So kind of like I can have one clean finance account and open a
second account in the same name,right?
Yeah. But the when you're opening this
(01:01:46):
kind of credit line, you're attaching like the either like,
like all the funds that you are borrowing out for the from the
credit line should be verified that goes to that specific
checks account and all the activity around that checks
account is what we are monitoring.
(01:02:07):
So the collateral that you're using the the kind of trading
activity that you're doing from that account is what matter for
the lender. So I would say these kind of
like credit line should be getting a mix between other
accounts, but should be related to a specific checks account
(01:02:30):
from the borrower side. And in general like the way that
we are also structuring from AI would say legal perspective the
the credit lines is that they are SPV based.
So they're also bankruptcy remote from the let's say main
company as well. OK, so you're.
Trying to ring fence as much as possible.
(01:02:51):
Exactly. Exactly.
So if the Pareto interface and kind of like the centralised
entities behind it kind of disappeared this afternoon,
could I still unwind my loan or redeem my USP just by
interacting with smart contracts?
Or do I kind of rely on you guysto kind of provide this service
(01:03:13):
to me? No, it would be possible.
The way that we made the protocol itself, especially on
USB and SUSP side is that there can be a way for the Dow itself
to change, for example, the meanadmin controls for the
allocation mechanism. For example, that also allows to
(01:03:36):
let's say we call back all the borrowers, all the funds from
the borrowers and we pay all theUSB token holders.
So if we call it, we have been call it in this kind of test
with our legal adviser the Bahamas test.
So if all the team decide to go to Bahamas and just don't build
(01:03:58):
the protocol anymore, we need tohave a way to let the community
and the token holders to be ableto change the parameters and to
change the admin controls, recall all the, borrow all all
the loans and repay back the andrepay and repay back USP and
SUSP. So yeah, we have been thinking
about that and since the beginning, the protocol is made
(01:04:20):
to be open in a way that it doesn't only rely on the core
team. Maybe as as a closing question,
if you look at the on chain private credit market and Pareto
in particular, what does successfor you guys look like in the
(01:04:40):
next two to three years? Nice, Great question.
In the sense that of course the main metric that we're using in
D5 is TBL. So that's kind of a North star
for all the D5 projects and that's what we need to what what
we're focused on growing over time and that comes of course
(01:05:03):
from creating a more and more diversified marketing
marketplace. So what I see as a growth driver
and potential expansion for operator in the next 2-3 years I
would say is to gradually introduce known crypto related
(01:05:23):
borrowers. We want to have a way to give a
whales to lenders and equity providers in general to not rely
only on borrowers that are related to the crypto market
because then depends on depending on the market cycle,
you're completely subject to themarket cycle.
And so the first driver would beto expand into more corporate
(01:05:47):
bonds, corporate loans, kind of credit lines, So introducing
kind of like fintech companies or cross-border payment
facilities, credit card companies, accounts receivable
companies, still related to finance and fintech, but that
would be the first expansion. And so creating this kind of
(01:06:09):
more and more diversified marketplace that would allow us
to grow USP and SUSP diversification power and allow
us to work with the larger basket of borrowers, borrowers
capabilities and loans in general to grow, to grow also
USP and SUSP. So I would say 2-3 years we
(01:06:30):
onboard new kind of corporate borrowers, we onboard new
curators that in our vision are also like traditional
institution that bring their ownunderwriting process and due
diligence process on chain without having to build
everything from scratch. And of course that would be like
meeting ATVL that goes above 1 billion to like numbers that
(01:06:53):
we're more like, you know, credit market is a 15 trillion
market all around the world. So we do have a lot of room of
expansion here in DFA. So like our first like milestone
is 1 billion of TDL, but then weneed to think bigger with like
this kind of number and we need to grow from there.
Cool, Mateo, where do we send people to find to find out more
(01:07:18):
about Pareto and kind of potentially borrow or lend some
money with you guys? I would say let's definitely
direct users and the audience that is interested to learn more
about Pareto to Pareto dot credit.
That would be the main point of access where you can access the
dashboard so you can see all theballs.
(01:07:39):
Read about the borrowers that wehave, read about the strategies
that they're running and understand more also about USP
and SUSP. And then I would say if they
want to also keep up with the announcement that we make, which
lately are a lot actually to follow us on Twitter at Pareto
dot Credit. You can find all the links at
(01:08:00):
Pareto dot Credit for Twitter aswell to look at all the
announcement that we're making and also to read the blog post
and the articles that we made with our partners.
Perfect. Thank you so much for coming on
Mateo. It's been a pleasure chatting
with you. Thank you Likewise.
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