If hidden connections are a known issue with identifying and stemming illicit funds for bad actors, why hasn’t this been solved for?

Koert [3:08]

When discovering bad actors, the most simplistic way is just to check if your customer or potential customer is on a list of known bad actors. Obviously, bad actors catch on to that, and they try to have people or companies stand in for them, so that they can still do what they want to do, but they’re no longer discovered. And to make this harder to detect, they try to create multiple degrees of separation between them, and this company or person that is standing in for them. If you think about that problem, it sounds relatively simple, right? Because all you got to do is simply trace back from the person you’re dealing with to the potential bad actor that is behind it. But this is a network problem. And it quickly gets very unwieldy, because before you know it, these steps between the person you’re dealing with or the company you’re dealing with, and the bad actor can easily become 2, 3, 4, 5, 6 or more hops, which becomes almost infeasible for a human being to uncover by looking through this network. And even for a computer, this can become very expensive to do up to the point where it becomes computationally infeasible. And that is the problem we wanted to solve on our platform; to uncover these relationships despite the fact that it’s actually very difficult to do.

Shreya [4:39]

Can you elaborate a bit more there with an example? How complex do these networks get?

Koert [4:47]

The first thing you need to do to get there is realize how quickly you’re building up these levels of indirection or hops as we call them. A very simple example would be somebody who says they want to open a bank account. But it turns out he is married to somebody who is an officer of a company, that is a subsidiary of another company. The owner is a known bad actor. If you take this apart what I just said, I actually described quite a complex multi hop relationship. And so in a network each entity can have, let’s say, 10 relations, that’s not an unreasonable proposal. In reality, it can be more. So if you now need to go out, let’s say 10 hops, and at each hop, you need to traverse these 10 relations, then you’re quickly talking about inspecting billions, or actually 10s of billions of potential entities in this network.

Shreya [5:47]

Fundamentally, what are the challenges that BADaaS solves that other lookup services have not been able to?

Koert [5:57]

I think solving the problem of bad actors trying to hide behind these levels of indirection or these hidden relationships, if you will. To do that, you need to solve two very difficult problems. The first one is you need to solve the problem of finding all these hidden relations or even non hidden relations. So you need to build this enormous collection of relations. These can be known relations, corporate information, corporate structure, and they can be stuff that’s hard to uncover like people living on the same address or people being related. You could take this to an extreme people who are childhood friends, right? The first challenge is building that having all that information in one place. And then the second challenge is, once you have all the information, exploiting it, to find these hidden or indirect relationships between bad actors and people that you might be doing business with,

Shreya [6:54]

To push a bit more into the first piece, building an enormous collection of relations. What data goes inside Bad Actor Discovery?

Chris [7:04]

So we’re using all publicly accessible data from company registry information that illustrates Officer of relationships, beneficial owners, legal officers of these companies, as well as offshore leaks that have been made public and government sanctions lists. All are aggregated together to power BADaaS. So in terms of the datasets, specifically from the offshore leaks, those include Panama Papers, paradise papers, Azerbaijani laundromat, many of these leaks that have been made available through ICIJ and the OCCRP. Those are included in the data.

Shreya [7:43]

And to push into the second challenge. Once we have the public data, what does it mean to exploit or reveal relationships in that data?

Koert [7:57]

Because data is public does not mean the relationships are public, the relationship might be hidden in there, but you still have to bring them out. Even just integrating leaks data like Panama Papers with corporate registry data is is non trivial. The more subtle your relations become, the harder it is to actually uncover them in that data. Even if all the relationships are known, you can hide in plain sight because the enormity of the amount of data you need to investigate as you move through these layers of indirection.

Chris [8:32]

When we talk about the scale of some of these hidden relationships and connections, if we look at company registry information, we’re looking at 400 million different entities 100 million different individuals. Sanctions lists are 10s to hundreds of 1000s. While offshore leaks are also in the 10s to hundreds of 1000s. Where those entities have multiple relationships. When we scale out to the number of relationships possible, we’re talking billion connections between all of these entities.

Koert [9:07]

Given the size of the data, this network becomes infeasible to inspect a single entity, we actually took the extreme goal of always inspecting all entities.

Shreya [9:20]

So we now know how and why a BADaaS was built. So who’s using it?

Chris [9:27]

And so with a click of a button, that says can show an investigator the hidden connections of an entity to a sanction bad actor. And this helps investigators in two ways. First, they can search across many data sets that typically aren’t integrated, so leaks with company ownership and the sanctions list. But more importantly, now the data is together BADaaS can directly show the connections to these sanctioned entities. So for, you know, an advanced investigation, for example, the process to kind of collect this data piece together their relationships. across that data, it may take weeks or even months to identify the expanded network of relationships for a suspected entity. But with BADaaS, those potential connections are shown, and the investigator can use this as an early lead on what relationships to look into and investigate even further.

Shreya [10:21]

Right, and bank investigators aren’t the only ones who need to be cognizant of financial crime. Who are the other types of people who can use BADaaS?

Chris [10:35]

It can be used as a screening tool for new customers and clients specifically, for example, real estate agents, they may want to use badass for a quick scan to search for new clients, which can help prevent money launderers from entering the financial system through real estate purchases. For lawyers, a quick search of these new clients can also prevent criminals who may be seeking the lawyer’s advice for setting up a new LLC or business, which may serve as a front for the money laundering activities. In both cases, scanning for new client names will not only scan for known bad actors who were sanctioned, but basically allow lawyers and real estate agents to give a second thought about who may be connected to these actors. And make sure they’re not getting involved in any possible suspicious activity. When it comes to Wealth Management BADaaS can be a useful tool for customer due diligence can also help search for prospective clients who are not next to bad actors, but closely connected with current wealth clients. For example, if a wealth manager is already working with a customer who has multiple business, those business partners may be good prospects for wealth management business. And they already know the source of wealth.

Shreya [11:43]

Are there any interesting stories you’ve come across in the data?

Chris [11:47]

So in terms of interesting stories, there is one related to Darren de Bono. He’s an international football player who has been linked to a money laundering case that involved fuel smuggling, from Libya, into Italy through Malta. With Bad Actor Discovery, you’re able to quickly visualize the network of these companies, which are now sanctioned. And also see additional actors who played a part in in that role. And across the network of these companies, you can easily see how de Bono was a center of this, as well as auditors that help facilitate this network of companies.

Shreya [12:30]

Why did Tresata choose to make BADaaS free?

Koert [12:35]

We want to showcase our capabilities to the world. And then we want to make this data publicly available for people to to be able to check these things. And, and actually be more effective at uncovering these hidden relationships, it basically becomes a way for us to build a capability that we can leverage to solve many large problems that we believe are all expressed as network problems.

Shreya [13:08]

BADaaS seems quite different from products, Tresata has developed historically, what are the principles underlying these next-gen Tresata products?

Chris [13:20]

First would be five minutes to value. So being able to quickly see with a single click of a button paths to these bad actors, is one of these these principles applied for usability. Because if you do have these paths to the bad actors, next thing you want to know is investigate exactly how are they connected, being able to see the explanation for similar address or Officer of relationships give the user the ability to to explore these gamification really evolves user engagement with the product, the thought behind gamification of these enterprise apps is that engagement with the end users is going to be important to kind of addressing some of these large problems. So there’s definitely a trend in the industry to share more data share more stories, and customers who are engaged with the platform and continuing to contribute to the community. Having kind of a gamified engaging application will help encourage collaboration across the industry.

Shreya [14:27]

Now we always end with what we lovingly call the one mic stand. This is where Koert, Chris, we ask you to make a prediction. What is the one thing that Tresata is paying attention to or is top of mind as you think through where our products will go?

Koert [14:47]

I am first and foremost a software developer for big data processing and big data analytics. So that’s where most of my attention goes. And I’m currently paying particular attention to the convergence of big data processing and cloud, which, for example, in open source, spark is happening through containerization. So basically what’s happening is Spark is moving off these kind of dedicated platforms for data processing, like Spark standalone and Hadoop, and is moving into the more generic container ecosystem like, like Kubernetes, great, lots of implications for anybody who is active in this space.

Chris [15:40]

So I think when it comes to something we’re paying attention to in the enterprise software space, it’s this move to, obviously, consumerized applications. So with consumerization, that is coming back to an application that users are engaged in. So not only kind of solving the big business problem, but also doing it in a way that users can quickly see the value and use it easily and effectively, in a way that they want to continue using the application to address these these big problems out there.

Shreya [16:19]

Koert, Chris, thank you for joining us.

Chris [16:24]

Thanks, Shreya.

Koert [16:25]

Thank you.

Shreya [16:27]

If you want to know more about the power of BADaaS, try it yourself at treseta.com/badaas. That’s tresata.ai forward slash, b-a-d-a-a-s. And if you’re left wondering about anything else related to Tresata Talks, email us at curious@tresata.com, that’s c-u-r-i-o-u-s at treseta.com. And give us a follow on LinkedIn, Twitter, Instagram and Facebook. And feel free to subscribe anywhere you’re listening to us and we’ll talk data to you soon.