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Welcome to Breaking Banks, the number one global fintech radio show and podcast. I’m Brett King. And I’m Jason Henricks.

Every week since 2013, we explore the personalities, startups, innovators, and industry players driving disruption in financial services. From incumbents to unicorns and from cutting edge technology to the people using it to help create a more innovative, inclusive, and healthy financial future. I’m J.P. Nichols, and this is Breaking Banks.

This week I headed to Guatemala for the MonitorPlus user conference. And it’s my first time in Guatemala, but it’s not my first time with the MonitorPlus team. We’ve done a couple of events with them now.

Last one in Bogotá, I think. And so we have Giovanni Castellano, who’s joining us. You’re the VP of Sales and Marketing for MonitorPlus.

But tell me a little bit about Guatemala, first of all, for those that aren’t familiar with it. Well, Guatemala is actually the first country in Central America. We are descendants from the Mayan civilization.

There’s a lot of Mayan ruins around here, I noticed that. A lot of them. Yeah, Mayans.

I mean, You were telling me that when the Spanish came to Central America, this is where they came, first of all. Yeah. Actually, they arrived.

I mean, the first important one was in Mexico with the Aztecs. And they followed the south pattern, trying to find out what else they found, and they found Guatemala. I mean, Guatemala with Mayans.

Actually, the Mayan culture is right in the south of Mexico, and Guatemala is considered the heart of the Mayan civilization. That’s a very pre-Hispanic civilization. So we’re in Antigua? Antigua.

Antigua was founded by the Spanish. That was back in the 1500s, I think, 1500s or 1600s. So this is a very unique- Yeah, we’re surrounded by three different volcanoes.

One of them’s still active. One is still active. Yeah.

Which is- One is, we call it the water volcano. Yeah. Yeah.

The other is the fire volcano. And the other one is Acatenango, I think. Acatenango.

Yeah. Those are the three volcanoes. We have 37 volcanoes in Guatemala.

And tell me why Monitor Plus, as an organization, has been based in Guatemala. That’s a good question. Our CEO, our founder, Jorge Domingo, he’s Guatemalan.

He is ex-IBM. He was IBM until 1991. IBM decided to sell all the operations in Central America.

So at that time, he took the decision. That was a management buyout of the IBM business. Was it? Yeah.

No, the thing is, IBM was, in 91, they were about to go bankrupt. Remember IBM at that time? IBM was really doing bad. Because they were mainly a hardware company.

And everything was moving towards business. I mean, services. Software, yeah, yeah, yeah.

And software. So they decided to sell the Central American operation, including the Dominican Republic. And they sold the operation to local investors.

And they kept stock participation in the company. So that’s the only alliance in the world where IBM is partnered. And GBM, which is the name of the new company that was founded back in 91, is the exclusive IBM distributor for the region.

A little bit of trivia for you. My first internship I did at IBM. Really? In Australia.

It was funny because I was a coder back in the day. I taught myself coding at school. Okay.

In fact, I used to do this little thing. When we get coding assignments, I would get other students to pay me to write their code. Because I would try and see how many different ways I could code the answer to the assignment.

Anyway. That was wonderful. By the way, I taught computer science.

That’s right. Back in the 90s, I taught computer science. And so I taught students how to code.

And it was beautiful. I mean, it was a very creative thing, I think. They hate it, but I mean, it was part of it.

So, yeah. So, this company is still the IBM exclusive distributor for the region. And Jorge, when that kind of a spinoff happened, he decided to become independent.

So, he invested in another small firm in Guatemala that they were kind of struggling and trying to take off at that time. It was a software company, but they were doing all these kind of things. But how did you guys come to specialize in fraud software? This is where the answer comes.

Actually, what happened is in 98, I think, most of the institutions that were concerned about the Y2K effect. So, Jorge decided to take a group of developers within the company and to develop this, what he called the X project. And basically, it was a real-time engine, basically.

A rule-based engine. So, they took three years to develop that product. They launched it in Guatemala because it was developed in Guatemala.

That was 98, 99. Right. Yeah.

So, they launched it and, of course, the first client. So, was it an expert system? It was an expert system. It was a rule-based system, 100%.

So, for those unfamiliar, this is early artificial intelligence, right? The rules-based engine stuff, the expert systems were our early attempts at coding. Exactly. Coding artificial intelligence.

And we found out that mainly auditors, they were interested in the product, trying to find out unusual activities within the institutions. So, we started selling the engine. That was back in 2000, just like an open-rule engine.

But two years later, we figured out that the best thing to do was to create models around that engine. So, we decided to do a model for fraud prevention for credit cards, for branch, for internal fraud. So, we started developing our own models powered by the engine.

And then in 2007, we incorporated, 2006, I think, we incorporated neural networks. And we did it because the franchises, MasterCard and Visa, they were requiring that those rule engines, they were also powered by artificial intelligence. Okay.

So, were you supplying fraud management stuff for MasterCard and Visa? Well, not necessarily for them, but since banks, they were… In partnership with them. In partnership, yeah. For the banks, right.

Yeah. So, that was… But in parallel, we also found out another opportunity was money laundering. Right.

So, banks that are regulated, they require a tool, a system to do a continuously monitoring about unusual activities. So, that was another niche market, really important. I mean, let’s dive into AML stuff and some of the specifics.

Because when we look at money laundering globally, we’re pretty ineffective at preventing money laundering. The latest statistics are maybe 1% to 2% of money laundering we stop. Why is this such a hard problem to resolve? Well, I think the first one is because it’s something that is imposed by the regulator.

And when you have some regulation, you try to comply. That’s it. Pretty much comply.

But you’re not digging into the real problem. So, that’s one of the problems. So, we have like FATF and the special rules, the 40 rules and the other ones.

But when we look at the way the industry as a whole has tackled it for terrorist financing and criminal money laundering, it’s this game of whack-a-mole. We’re trying to identify suspicious transactions. But we have so many false positives that happen through that system today.

But I think of the future of money laundering and fraud management more like the way we think about cyber security today. Systems that will track and identify suspicious behavior and stop those suspicious actors. From the technology and operational perspective, the problem with fraud versus AML is when you’re doing the fraud monitoring, you get a feedback.

All the time. If you raise an alarm or alert that is a potential threat, someone is going to say yes or no. All the time.

The problem with AML is you don’t have to do that. Until it becomes a crime, you don’t have to. Still, you don’t have the feedback from the regulator.

Because what you have to do is you have to document the whole thing. Yes. You have to build a case for criminal justice.

You don’t know if that was real money laundering or a false positive. Exactly. That’s part of the problem, why you have so much false positives in the AML space.

In the fraud space, it’s different. That’s why machine learning is becoming so popular in the fraud space. I shared a statistic with the group today.

During the presentation, I talked about the difference between the Chinese mobile wallet systems and internet credit card usage in the United States. It probably will surprise many people listening to the show right now that the fraud rates on Chinese mobile wallets are a fraction of credit card fraud that we see in North America. And generally, in Western Europe and so forth.

One ten thousandth. The official numbers is 0.0006 basis points of fraud for Alipay versus 11.2 basis points of fraud for Alipay. It sounds like in a utopia.

Part of the reason I hypothesize that Alipay is so much better at handling fraud is a couple of different reasons. One is it’s a very modern tech stack. So they have fraud monitoring systems built into the tech stack natively with some machine learning and AI for sure.

And secondly, that the Chinese financial system is now based on facial recognition and biometrics, not based on old forms of identity, which can be easily frauded. So your social security number in the US, your driver’s license, all of that. We know that all of that stuff can be copied.

It’s no longer securable in the sense of sort of long term fraud protection. So when you’re dealing with the fraud stuff, you can use machine learning. You can track more and more fraud.

But isn’t it fundamentally about fixing identity and fixing sort of the core technology? Kind of. Partially, yes. You fix that, you solve part of the puzzle.

But you still have one problem, especially that depends on the side of the world, on the perspective of societies and everything. Because if you go to Europe and you try to talk about internal fraud in Europe, you’re not going to get any kind of attention. Because cultural things.

Internally within organizations, like in banks. But we know that the biggest frauds that have happened historically are employee based fraud. Yeah.

But in developing countries like most in Latin America, in Africa, you really got an issue there. So what I’m saying is fraud moves in different ways. So when you’re thinking about identity and biometrics and stuff like that, that helps a lot from the consumer perspective.

But still, that’s why the multilayer approach for fraud prevention is the best one. Because you’re coming in different layers. The recommended are five.

The first one is related to the device. That’s where you can have facial recognition and stuff like that. So the device is one.

Then the navigation, what also is called behavioral biometrics. Or heuristics, we might call it. And then it’s related to your account.

Account-centric fraud. And then in the four layers, you have a multi-product and multi-channel approach. What is called also omni-channel.

So you’re going different layers. But that’s most of the time related to the consumer, from the consumer perspective, from the cardholder and things like that. But you’re always going to have the internal threat all the time.

And when you have the criminal rings, organizations with people within organizations and banks, you’ve got a problem. Right, the organized fraud. Or even when we’ve seen big frauds, sometimes big frauds have happened just because someone who has control over trading platforms and things like that, they run away with it because they’re trying to cover losses or whatever.

FTX is a good example of that, I think, to some extent. Although you could point to fiduciary management as a problem. You wouldn’t believe the amount of fraud because of internal threat.

You only need an insider to know about an account being dormant for a while. And that’s a usual operation. I mean, it’s just type in an account number and see how long that account is being dormant.

So let’s take a step back. Tell me about the advancements that we’ve already made in fraud management that have been effective. Well, definitely fraud management, from a technology perspective, there are two big buckets.

The first one is rule-based. That’s where the expert judgment takes place. And that’s still valid.

And it’s going to continue to be valid. Because humans that are into the fraud prevention business, they have this sense of what to place in terms of a rule because they really know what’s going on. So they can create scenarios that are not easily doable with machine learning.

Because one of the problems with machine learning, which is the second bucket, is that you need a lot of information. A lot of data. A lot of data.

Like all machine learning AI we’re learning, it’s about training those engines properly. So you still need good fraud managers, people that know those rules, to be able to choose what data to filter. And what is becoming a really powerful combination is what they call an ensemble of technologies, which is rule-based systems plus machine learning.

That combination is becoming really powerful because you can put the expert criteria into a rule, but you have a powerful algorithm that is gathering a lot of information, learning on the process, and it’s very accurate. So when you go in to do an implementation at a major bank, for example, I would imagine part of the issue is getting access to the right data because you have all these disparate legacy systems in a bank. That’s the biggest challenge always.

So do you guys have to invest in building out middleware and figuring out how to pull the data before you can execute? Yes, but it still is really hard because of legacy systems. They have disparate system platforms, and you want some information. You always have pieces of information.

And that’s the problem. And the problem the banks are facing is IT people, of course, top priority is business. It’s not fraud prevention.

It’s not regulations. So what they’re doing is they’re developing code for the business to make money, and that’s the major challenge that the fraud units have. They don’t have time.

They’re always waiting. Well, that’s an argument for automation. I guess you could say.

So where does this go over the next 10 or 20 years? Well, I think machine learning is taking off. The challenge is data. That’s one.

I mean, you really need data, a lot of data, and good quality data. The other one is orchestration. You need to orchestrate different systems because the problem is with those layers that I mentioned, the five layers, you’re going to find many vendors.

An average bank, you might find easily from eight to 10 different vendors. And most of the time, the problem is they don’t talk to each other. So they have different scores.

There is a vendor that is analyzing the device and says, okay, this device looks risky, and he’s going to give you a score. So another vendor might be looking at the transaction and say, well, this amount is unusual for this customer. But they don’t talk to each other, and that’s the biggest challenge.

So orchestration is the real future of fraud prevention if you put all those systems together. No, I mean, I can tell you from the early days of Mubin, we had a lot of Eastern European identity theft, trying to open accounts to do ACH fraud in the US. And in the end, we were able to stop about 90% of that activity just by learning the browsers that the fraudsters were using, like a very simple technique.

Exactly. We just got a new customer from Argentina. It’s a digital bank only.

And when they launched their app, they were expecting, I don’t know, 10,000 new accounts. They got 70,000. But 40%, 50% of those accounts were fake.

Yeah, we had similar numbers. And that’s a common problem. That’s when you need a multilayer system otherwise you’re just protecting the first layer.

And that’s the problem. You need to correlate those. And the challenge, again, is you have different vendors and you have different business units trying to, I mean, trying to avoid fraud, but they don’t talk to each other.

Different priorities as well. Okay, fair enough. All right, let me bring in Jose Ruiz, right? Yeah.

Jose, you’re the product manager at Monitor Plus. I want to dive in a little bit into the problem of AML. We were talking about this offline a little bit, money laundering.

Why is it that we’re so ineffective at AML today? Okay, so the first thing is I want to consider that we question where we get that conclusion. Because we may see the regulators publish that they received in a year 700,000 reports or something like that. They only processed about 1,000.

So the thing is we don’t really have any visibility on to what those reports were, how many of them are repeats, how many institutions provided the same type of reports about the same type of accounts or people. And the other part is that it’s mostly only the financial space and some other large asset or investment-related industries that are being regulated. And those are the only ones that not only have to comply, but also provide the information and the efforts to actually fight against money laundering.

And finally, there’s also the fact that this tends to be a local effort in terms of countries working on their own. There’s very little collaborative information sharing. But the AML crimes tend to be multinational.

They tend to be suspicious actors offshore. So if you’re going to solve this problem, particularly from an automation perspective, identifying bad actors, you need data sharing, cross-border data sharing. But that’s a tough ask.

I can tell you, like six years ago, I met with FinCEN in the US. The regulator that looks after financial crime. And they said, yes, we absolutely agree, but we don’t know when that’s going to be politically viable.

That was essentially the answer they gave. They said, yes, to fix AML, we’re going to have to have broad international data sharing agreements. That is not politically tenable right now for America.

There’s that. In terms of collaboration internationally, that political will is usually lacking. But there’s also the fact that, for instance, regulators who could locally be a great source of collaboration usually enforce, but whenever, specifically in the case of FinCEN, for example, they have certain lax criteria when it comes to current Anti-Money Laundering Act 2020 requirements that they set.

One of the worst kept secrets in financial services is the fact that New York, London, Dubai, all the banking sector in those major cities makes a lot of money out of money laundering. I don’t know if you guys remember Charlie Shrem, who was the founder of BitInstant, the first Bitcoin exchange in the States. I had Charlie Shrem on the show many times, and I had him on the show the day before he went into prison for two years for money laundering crime, which was retrospectively applied because they changed the rules about Bitcoin becoming a money laundering vehicle and so forth.

They applied that retrospectively and put him in jail. The day he was going into jail, HSBC had a $1.7 billion fine for Mexico money laundering crimes. And tourism financing.

And you look at that situation and you say, well, first of all, why didn’t anyone from HSBC go to jail? I mean, this is a big, big, big money laundering, way bigger than the million dollars that BitInstant traded on Bitcoin on the Silk Road for drugs. Right. But I mean, this is sort of at the heart of the problem.

The banks make a lot of money out of this as it is today. That’s why a lot of it gets a blind eye turned to it. But when do you think that’s going to change? Or is it going to change? Well, it’s a good question because the political landscape doesn’t seem to be going in a way that we’re going to actually collaborate.

But I think that at least locally we can because there are some efforts from regulators to instill a collaboration information sharing. There’s some trend to start local registries, national registries of beneficiary ownership. But there’s always the fact that it all comes back down to the original data.

Is the data up to date? Is it reliable? Is it complete? All of that. Yeah. No, I agree.

In terms of MonitorPlus’ business overall, what impact is artificial intelligence having now to this business overall? Well, it’s a mix because our system actually provides a hybrid between the expert models that apply the expert knowing how the rule-based models and all that. And those expert systems are based on compliance process and policy in many cases. So they have some structural element to them.

Yeah. And as well, the AI aspect of it, which allows the continuous learning and the improved detection models. But it always relies on an input and it always relies on the expert.

So you might know that the greatest performance from AI derives from a black box. But the problem is that from the regulation side, there’s always a need for some explainability. And you can’t simply have a black box from that side.

And also, if you have a black box, there’s a lot of biases that could come into play and a lot of errors that could be taken into account that the model could be considering and creating alerts for that you need to fix. So you always, at least at the point that we’re in, you still need a balance between human and machine. Okay.

All right. Well, that’s good to know. Tell me about the conference and what’s been happening in Guatemala this week.

Great. So we just started today. It’s been going great.

We have about 240 people from around Latin America that have visited us. These are very committed professionals. And we’ve had conferences today regarding artificial intelligence, money laundering, fraud prevention, some banking innovation, which as well might include artificial intelligence and how it can be used to not only boost the business, but also benefit the customers and provide real value.

So where can people find out more about Monata Plus? They can visit our site at plusti.com. And what’s the TI? For people who aren’t familiar with that. So the original name for the company was Plus Technologies and Innovations. But for marketing purposes, it has been shortened to Plus TI.

Great. Well, for both of you, thanks for joining us on Breaking Banks today and all the best for the rest of the conference. Thank you very much.

This show is brought to you by Alloy Labs. As much as we love talking on the show, we believe that action is more valuable than talk. Alloy Labs is the industry leader in helping fearless bankers drive exponential growth through collaboration, exclusive partnerships, and powerful network effects that give them an unfair advantage.

Learn more at alloylabs.com. Alloy Labs. Banking Unbound. This is Roberto Capodici, your host.

Today we have an exceptional guest, Mr. Jahed Moman from Barcelona, Spain. And what we’re talking today about, yeah, again, we’re talking about carbon, carbon credits, and all these things that are green, right? It is green in part, green, green, like the walls behind our guest, Jahed. But no, today we are talking about the color blue because, and it’s going to be a beautiful discovery for many people, there is more absorption of carbon that is captured by things in the sea compared to those captured in the forest.

And this is really a revelation for me. I haven’t thought about it. You think that we breathe thanks to the tree that produces, you know, like oxygen for us, but there are algaes and there are a lot of plants in the sea that do a much better work.

So those get monetized as well in terms of the work they do and the space that, you know, they take. And we’re going to discuss this. So welcome to Breaking Back Europe, Jahed.

You can give us a little bit of an introduction about yourself and how you find yourself into the blue carbon world. Definitely. Thank you for having me.

So I run a venture fund called Cerulean Ventures, which is just a combination of blue and green. Actually, if you end up going to the website, that’s what the color means. And we basically are interested in creating planetary scale regeneration and climate impact through nature markets and energy markets.

And so we came to this space, basically my partner and I have been in this space for about seven years. Collectively, we’ve been looking at, we both started in tech and open source software and blockchain. And we very much saw an opportunity here, you know, on the fintech side, because like you said, there is a lot of interest in carbon and carbon credits and carbon capture because we’re in a context where, if you haven’t been paying attention, there’s a freaking heat wave in Europe right now.

I’m sweating. I don’t know if you used the video for this thing, but I cannot stop sweating here. It’s like 70% humidity and 30 degrees Celsius.

But anyway, why is blue carbon important for all this stuff, right? Well, basically we, if you look, like you said, 90% of the world’s carbon is actually stored in the ocean. Depending on the US, there’s a number of studies on this, 80 to 90% of it. And it’s stored in a various number of ways.

Actually, it can be stored even by the creatures in it, right? Like whales capture a ton of carbon, actually, when they die and sink to the bottom of the ocean. So that’s actually one way we’ve seen some crazy ideas out there about storing carbon in the oceans. But anyway, we got into this space because we’re looking at how to use FinTech to scale the adoption of blue, green carbon, protect biodiversity, protect topsoils, and a number of other things.

That’s an interesting, there are so many questions that pop to mind because when it comes to a forest, that’s very well assigned. The forest belongs to a government, belongs to a private corporation. So it’s very tangible, the square kilometers and you can make an easy calculation on how this is contributing, not cutting down the matter rather than planting more.

So what are the activities and how you can assess the benefits of blue carbon collection, right? Well, that’s one of the reasons why you’re seeing blue carbon projects are still less common than land-based ones. It’s because this measurement, reporting, and verification, which is known as MRV in the field, is very challenging with blue carbon projects, right? There are dozens and dozens and dozens of companies, standards, scientific papers published on arboreal MRV, measuring forest carbon capture, right? And then measuring that in an ongoing way, reporting and verifying it. There are standards that have been built around additionality, around, you know, and I can define some of these terms if people aren’t familiar with them, but basically that stuff is way more advanced.

We know far less about ocean carbon, but we do know that in total there’s more of it stored there, right? So like coastal ecosystems, tidal marshes, mangroves, seagrasses, they can capture and store carbon at a rate up to six times higher than mature tropical forests, right? And there’s published work on that. The problem is that when you begin to insert financial dynamics into it, you need to be able to tell a customer who’s paid for an outcome, how much of it can I claim this year? How much of it can I retire, right? If you’re familiar with how carbon credits work, right? And so, you know, people have their net zero commitments or they’re in the compliance carbon markets and they have to actually emit less. And so they’re looking for ways to emit more if they’re in the compliance markets.

And then if they do that, they have to find a way to actually offset that in a trusted way because if you’re in the regulated market, you can’t mess around with that stuff. Right. Just for everybody else that is listening or watching us now and are a little bit confused with the whole thing, you know, the impact of pollution rather than cleaning the air that is being measured and collected.

So there are places and countries and operations that have a positive impact and those that have a negative impact. So those that have a negative impact, they need to pay on the positive side in order to balance out their damage. Right.

So this is pretty much in very short. And now I wonder with this blue carbon, people can say, hey, I’m polluting but the sea is taking care of counterbalancing, right? So who has the right to claim this aspect? Well, you asked an interesting question. The rights go to a lot of things.

Right. It’s easy. I was at a conference recently actually in Lisbon, the Economist Impact Summit on the World Ocean Summit.

Right. And there was there was a person that actually I’m not going to name too many names. I don’t remember what the rules were on that, but I’ll just say that it’s one of the world’s largest banks and one of the world’s largest banks and insurers, chief sustainability officers.

And one of their key points was that the reason why they haven’t made an ocean investment yet is because the provenance of ownership on land is very simple. They own the asset. They own the thing that lies underneath it.

They can claim that with the ocean. The oceans are what are under what’s known as commons based governance. Right.

So they we have three types broadly. Right. There’s there’s a global commons based governance.

There’s private. There’s private property rights. There’s public property rights.

Public is the government. Private is private individual or corporation who buys it. Commons is actually what the UN and other global international NGOs and bodies that say, you know, going out to this point from your coast is where you’re, you know, if you’re America or Canada, what have you, your coastline ends here.

The rest of it is commonly governed. Exactly. And so that in this context, the Economist Impact Summit was really being positioned as something that was a problem.

Whereas, you know, I asked the question in being the kind of kind of person I am. I was like, well, are you arguing that we should have private ownership of all the oceans? As you were talking, I was thinking to set up a website to make a small grid around all the ocean and start setting the NFT for each square kilometre of ocean so people can contribute on a piece of it. In fact, that’s probably you should counterweight for everybody in equal parts of land.

I don’t know. That’s an interesting aspect. If you’re a fisher, I would trust that.

If you’re in fishery, if you’re in aquaculture, these things, if you’re just some person off the Internet buying stuff, I don’t really think it’s going to do anything. In fact, it was ironic I didn’t realize. Oh, it’s funny you mentioned that though, because I see people like this idea all the time and I think it will happen.

I think it could be good. It’s just about how well done it is. Right.

Because I don’t think we should be on the other side of this where, you know, the oceans are owned by Nestle, Bechtel and like a couple other folks who because at that point, if you haven’t built a system that also holds them accountable for the environmental costs that they freely impose on everybody, then you’re just going to have the private right to pollute, which is what you have on the land. Right. Because in fact, at the beginning, if we’re talking about lakes that are very huge lakes, that’s easier to measure and to assess.

But the oceans are something, something more difficult. For sure. So, yeah.

In fact, the all the questions that can come to mind are really putting next to each other. Green carbon credit, blue carbon credits in terms of how they exchange. It’s funny because in the crypto world, whatever was a token were called color coins.

So. Oh, yeah. That was old school.

Yeah. I’m wondering if it is this carbon credit we are entering the face of the color coins as well in terms of how many other measurement that probably the stratosphere in a certain point is contributing in a certain way. So you’re going to have that one as well.

I can definitely fill in a little bit of details on that. So like this right now, one of the main problems with the carbon markets is that they the voluntary carbon markets is that it’s somewhat difficult to get ongoing quality data about the quality of ongoing quality data about the efficacy of the carbon credit. So, like, basically, I buy some credits in year three or four of a project.

I need to you know, I don’t really it’s hard for me to get feedback on like did this did this carbon that was up in the air actually end up somewhere. Right. And so the problem with VCM is that a lot of people are trying to treat.

We think, you know, we think I’m now speaking like for our fund, right, is that people think that carbon is can be made very fungible. We think it can be, but there’s going to be some extent to which it’s always semi fungible. It’s not carbon here.

We’re already talking about green carbon, green and blue carbon. That’s a great example. But there are other methodologies like biochar, direct air capture.

There’s all these other various things where an enhanced rock weathering, which you haven’t even touched on. And they have different yield curves. And what I mean by that is like, if you’re coming from crypto, right, like, what is the yield curve look like for Uniswap or even something better? Sushi swap.

Like, what did you get from fees when you started staking your sushi swap versus Uniswap? They’re totally different because one pays fees and the other one doesn’t. And so if I look at something like a forest, when you plant a forest, I don’t know if you guys use video. Maybe you can see my finger tracing an S in the air.

And you plant a forest for the first few years. It kind of just trots along. It doesn’t really capture much.

Then when it starts to mature, it goes hugely up like this. And then when it matures fully, it goes back to just kind of being a little bit flat and increasing. However, when you look at something like biochar, it’s a linear curve.

It just keeps going up like this. So we we trade these often. It’s hard for us in the markets to say, like, what’s the worth of this biochar project at year five versus this other this nature based tree forest project at year seven? And that’s a really hard thing, a really hard trade to make.

But we should be able to do it. Right. And now you’re talking about blue carbon.

I don’t know. I have to go and read some of the science. I’m sure someone listening like that guy’s an idiot doesn’t know it.

Like basically blue carbon kelp forests and all this stuff. We have no idea. Like what what this looks like when you want to marketize it, put it out there on the market.

Right. It’s not stopping people from doing it, which I don’t think it should. But the point with this is that when you compare blue and green carbon, you really have to look at how what rate are they capturing it? If they can if they can capture it at a six times higher rate than tropical forest, I figure I talked about earlier in the pod.

That’s fantastic. It’s great news. Great news for all of us who are sweating right now.

Right. But we also need to know how much like how quickly that is and how fast we can scale it. And FinTech, block chain, normal, normal.

These are the questions we need to be able to answer so that we can put more money in these systems and get more money out of them on the back end with the climate impact. Do you think also now because 2023 is the year of artificial intelligence, right? Or human dumpings, if you want, on the other side, their model, their model of artificial intelligence that can help to measure and understand the distribution of this? Because I would think that an algorithm that has maybe a certain set of inputs, satellite pictures rather than others, probably it will be the most fair methodology to to value things. Well, yes and no, because it’s actually really difficult to do so.

And I think that the generative approaches that are super sexy and getting all the attention are going to be pretty difficult to apply here. There’s a different school, a different tract within AI called causal AI who that those forms and methodologies of AI are really trying to understand what causes what. And so there’s a solution concept in this different field, game theory, called the Shapley value.

And this is a really interesting concept when it’s applied to forests, because basically the Shapley value is essentially if you’re dealing with a cooperative game and there’s a surplus generated by a bunch of players in the game, the Shapley value lets you kind of say who did what. It lets you kind of give you one, you know, how important is each player to this game? And what payoff can they reasonably expect, right? And so any AI approach that can sort of map a Shapley value as a contributor in an ecosystem game is really the way you want to be doing this. Because then you can, because if you think about a nature-based solution, let’s talk about green credits, right? There’s a lot of players involved but the ones who really matter are who planted the trees? Where did they plant them? When did they plant them? When did they measure them? Did they take any of their actions to support them? And then you can actually say great, what was their contribution to this, right? And then if you think about that, I just talked about the people doing the project, what if I also want to include, what if rainfall is a variable in the game? What if biodiversity, soil health, and soil quality are… Without counting the extension of the space that you have to control because the forest is smaller compared to the ocean, right? Exactly.

The forest one is already super complicated but you can see that it’s not as simple as feeding a model a bunch of data and giving it a token and saying what comes next, right? That’s a totally different thing. But I don’t doubt that we can do this. There are already people working on this and we’re working with some of them to figure this out.

So my intention to take all my houseplants and throw them in my swimming pool and try to get some money is not to be taken into consideration I think at this point. Yeah, but if you get some kelp powder, maybe you can do that. If you get some kelp going, that might be helpful.

It is something that, to be honest, I am like a computer scientist a cryptographer I do have a large interest and I’m a blockchain person I am entering this world of the impact on the planet and the monetization of this impact since recently, so I am quite illiterate in these things and this conversation helped me to understand and learn as much as I hope all the people listening to us are having this opportunity as well. What is your vision for a more technological and wise, in terms of a human take on life and future, where things can be put to play in the proper way? Well, I think we’re never going to be I hope someone, there’s a betting market or prediction market on this so people can bet on this, but I’ll probably be wrong, but I think that we’re never going to be able to fully understand every single thing that’s going on in nature in a reductionist way that we kind of, you know, we’ll be able to work backwards from all the variables and say, this is how it works but I don’t really think that’s where the innovation needs to be where I, this is a Vintech podcast I actually think what we need to be able to do is if we look at stuff like blue carbon and green carbon, at a macro level at an outcome level, we know this stuff works. I know that if I plant a tree, it uses carbon to grow.

I don’t need to know exactly how much at what time. Same thing for blue carbon. I know kelp forests do things, I know in a whale if I protect whales longer, they take in more carbon, they die, they go to the bottom of the ocean, they trap carbon.

So what we really need to find is what is the overlap between our financial system and these nature-based solutions and we need to be able to finance them because the problem with this stuff right now is that if I want to do a blue carbon project it’s extremely expensive. At its core, you need forward contracts that pay for development costs right away and then those forward contracts also secure access to future carbon credits at a discount. This is where now we’re talking about stuff like where an NFT could be useful.

You could have a long-term off-take project that’s representation is turned into an NFT that’s tradable and so now you could bring liquidity to a project where there wasn’t any before. I’ll give you a very specific concrete example so people are like, why the hell is this guy talking about oceans and NFTs? If I’m a project developer and maybe I live in India along the coast of the Sundarbans which is actually a place where there are blue carbon projects being developed, I want to develop a mangrove project that requires planting a bunch of mangrove trees, it requires a bunch of stewarding these trees over time, etc. I have to find the labor, I have to find the trees, I have a ton of costs up front and now I want to say to someone, great you know what, mortgages have existed forever, why can’t they just get one? Absolutely they can right? So let’s say a big corporate purchaser like Mastercard or something goes, yeah let’s invest in some, let’s do some mangrove projects right? There but they want to say like, how many credits am I going to get in the future? When you plant this, what am I going to get in year 3, 4, 5? We can answer that question roughly, maybe within a 5 to you’re going to laugh, 5 to 30% error maybe, right? It’s actually wide range, right? Yeah, it’s okay, sure.

We’re doing something, right? So what you do is, you’re the project developer, you go, I’m going to sell you the first 3 years of what we think the project will deliver at a 50% discount. You pay that up front right? Great. And then Mastercard goes, now the CFO of Mastercard, or the CFO’s office goes, yo, what if exactly, what if these people suck at this and I have a bad asset, can I trade it? And like right now, the answer is not really.

But this is where like crypto, NFTs, deep liquid markets are interesting, is that if you have some of these other technologies we’re talking about, like AI, or even just remote monitoring, right? Like other things like that, that can say, hey, what’s the thickness of that tree? What was the thickness of this tree in the last 6 months? Is the tree still there? If you can answer that question, you could have an oracle that accepts that data and updates the value of that credit. Now, not just the credit, the project, right? And now if the project is an NFT and it’s constantly being updated, and you have a pool of forward contracts that everyone of that methodology across that region or across that world participates in, then now you have yourself a forward market, right? And now this enables you to say, to find out things like, who is a good project developer? What are the successful projects that are happening? Who’s being traded the most? Who’s being dumped on? And you then also create some demand, you fulfill the demand for people who are like, I want to actually support these projects and claim environmental impact, right? And that’s where I think the innovation needs to happen and still isn’t necessarily happening as fast as possible. I don’t think it needs to happen at this magical cybernetic nature level where we’re controlling it all like Jurassic Park.

I mean, for sure, what comes to mind at this point is IoT, IoT for measurement, drones, swarm of drones that go and take aerial imagery to be elaborated and things like that. That’s happening too, right? Because, I mean, at the end of the day, you say mangrove, which is a little bit like one foot in the water, one foot out. It’s easy because it’s measurable as much as the green carbon, right? But when it comes to full ocean, full, you know, I live in Bali in Indonesia and sometime going to nearby island, there is a huge growth of algae that they do it simply to make beauty creams or product of other sort.

But you see, you see the extent of this, which probably is micro dot in the ocean, but probably without realizing they’re contributing in their own way to have a mechanics that helps, right? And it’s not so difficult, relatively expensive to do. So, even in the coastal areas, is it mangrove? It is, you know, a byproduct of some other activity. They have in their hand something that can monetize as well, right? If tomorrow they say, look, we’re doing this, we’re doing this for 15 years, we’re going to do it for the next 50, right? For doing our beauty product, we are contributing this in terms of blue carbon and we can also resell this, right, if it is measurable and valid.

Yep, exactly. And I think like that’s the thing, right? This is what’s interesting. This is the stuff.

I’ll tell you, I can’t tell who said this, but like I was extremely excited talking to an institutional, to a financial institution yesterday where these folks were saying they were looking at solutions for their asset managers where their asset managers wanted to know, hey, we’ve been getting some loans out to you know, as if farmers and when I say farmers, farmers are like, oh, you mean like, you know, the one, I’m like, no, I’m talking about like 50,000 hectare gigantic farms, like corporations, right? And like we have been doing our like, you know, biannual check-ins with them and they told us that they were able to sell credits in two areas and they were like super, you know, they were excited about being able to double sell their credits. So then like I was asking, I was like, so what made you, what did you do about that? Did you go and try to find out like where they sold them and like what’s going on? They’re like, yeah, well, what we were really searching for as a solution, as a financial institution was something that prevents double spending and something that gives us the immutability of data and I was like, but it’s funny, I didn’t have to say it. They were already working with a blockchain data provider as a bank and I was like, that’s huge and not enough people are talking about this, right? Because that becomes the real promise of the space is that people are like, what should we be doing about, what should we be doing about blue carbon? What should we be doing about this, that and the other? I’m like, dude, we already have the solution, we just need to adopt this, right? That’s very interesting.

It’s been a pleasure talking to you. We’re at the end of our time, but it is an interesting new planet, surely bigger than all the green carbon, but more difficult to manage and measure and I don’t know, I’m interested to see in the future how these things develop and maybe we have another chat. Oh, let me show a little bit for you.

If you’re interested in this stuff, you should check us out on cerulean underscore XYZ on Twitter. You can find me on Twitter at againstutopia and also we invest in a number of companies who are doing this stuff, so we’re looking really closely at OceanMRV. We’re looking, we already have a bunch of investments in nature-based solutions on LandMRV and so I think if you’re interested in this space, come find us.

Come post, join the rest of us posters, eternal posters you can’t get off Twitter and also on LinkedIn and we are constantly sharing information on nature data, nature markets and all the developing standards in the space, especially on blue carbon. There’s an upcoming in September as we get closer to COP and as we get closer to Climate Week NYC, there’s going to be a new release from the task force on nature-related financial disclosures where they’ll be basically announcing a bunch of new banks and financial institutions who will be adopting standards around blue carbon and around reporting of nature risks that is tied to nature data and I think like we’ve talked about, man, blue carbon, green carbon, whatever it is, it’s really all about how are we measuring it and how are we financing it. And how we don’t double spend it.

Exactly, and people who, you know, whatever your views on blockchain are, I’m kind of a maximalist myself, but you know, there’s a clear use case for it and I don’t need to be saying it anymore. Right, I’m not the one saying it. Unfortunately, there is still a mass that when they hear blockchain, they think bad stuff.

No, no, it’s an instrument, it’s a piece of software, how you use it can be good or bad. And for sure for the carbon credit, it is a perfect marriage. Thank you again for being with us as I had, this is Roberto Gabudieci, this was Breaking Bank Europe episode 184 and see you guys soon.

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