Human trafficking exploits and enslaves a stunning 40.3 million people, and earns traffickers $150 billion yearly. Can data help fight back? Enigma is developing a platform to help financial institutions block the traffickers' transactions.
The following transcript has been lightly edited and condensed for ease of reading.
Dan Costa: So I want to do two things, first I want to talk a little bit about Enigma, your business processes, where you get your information and how you use it, and then we’ll talk about some of the applications. There’s obviously a lot of business applications but there are some really important humanitarian applications that you put in to work for as well. And I want to make sure we hit those. To start off, I want to quote a magazine, a Forbes article and the quote is “To date Enigma has synthesized 100,000 data sets in more than 100 countries, organized intelligence on 30 million small businesses and accumulated a 140 billion points of data on the U.S. population.” So what are you doing with all that?
Hicham Oudghiri: So lots of good stuff hopefully. I guess, let me kind of step back and give you a big overarching premise for us and I think kind of dirty secret in the data industry which is most of the advanced AI work and all of the buzz around machine learning and what people are doing with big data is really understanding how behave online, right, most of it. And the successes we have heard have been these stories from the Googles, the Facebooks, and the Amazons of the world and essentially using all of this very sophisticated math and this data at scale to get you to click on things on the internet, which has done some amazing stuff like the communication paradigm we have as human beings. But in terms of fundamentally changing how businesses work, be it drug safety or getting access to credit, it’s a whole segment of the population that’s been kind of left by the wayside by the big banks because they just don’t understand them and putting data to work in the real world is quite difficult. So our goal and our mission has always been to collect kind of a new kind of information and model how the real world operates for a variety of use cases that I’m happy to get into but that first big divide is really what we’ve been doing with all of it. Just trying to model how the actual real world works and prove it where ever we can.
Costa: When you started off a lot of people have heard of Enigma Public. You were taking public data sources, government sources, and making that available.
Oudghiri: Yep.
Costa: And then you’ve been adding and sort of layering more and more private data sets.
Oudghiri: Yep.
Costa: Can you explain how that works?
Oudghiri: Totally. So you know in this drive to kind of really understand how things work, we just came to a point where we had understood the symbiosis in between what was available kind of openly and publicly for everyone and what we could get by either partnering people, partnering with people, sometimes we get data back from our clients, by buying data from folks who had spent good and hard time collecting it. You know the fundamental process for us is the same which is, does this data have signal, does it have quality, how is this data collected from a lineage point of view. I mean just because the data said it’s public doesn’t mean that gives us infinite usage. You can’t use property tax assessments in marketing situations. And sometimes those regulations are city by city, right. So for us it wasn’t that much of a shift but in the kind of scale of the business operation and the questions that we’re trying to answer now, we kind of have, we’re agnostic as to where the source of the data comes from. The public data being the foundation for us to kind of resolve entities, i.e. merge very, very disparate datasets together who sometimes don’t speak to each other. Having that backbone reference spine of every business, every person, these sort of things, has definitely kind of gotten us to where we were in this regard.
Costa: Can you give us an example, I know you have a lot of financial clients, can you give us an example of a problem that you solved for a financial institution, a bank or a lender?
Oudghiri: Yeah. We’ve done tons of things. I’ll give you a couple. So we do a lot of compliance work. You know, basically is the person or company that you’re doing business with legitimate? Right, this is a question that is actually quite hard to answer. And if you’re a small business and you’ve tried to open a bank account, you’re kind of sitting there and annoyed and like why can’t I give these people my money, right. And most of the time it’s because the bank’s processes are just really bad in that regards. But the other half of the time, there is some due diligence that needs to be done. And getting that due diligence, like the kind of first 90% of that due diligence automated so that you can let folks investigate the real bad guys is something that we’ve done quite well. We’ve done this for American Express and helped them with their anti-money laundering operations. We’ve done this with folks like BB&T where we help score every client that comes into the bank. So think about credit score and think about scoring someone on basically call it like a shadiness factor as it were.
Costa: Is that an informal metric that you’ve calculated?
Oudghiri: Yes. The shadiness factor. And it takes all kinds of data in and helps the bank basically do business with the right people much, much faster.
Costa: So we’re going to get to the bad guys in just a second. What’s the strangest data set that you have and what makes it useful?
Oudghiri: So I think we were talking about this a little earlier in the back and it’s like the strangest one is really, I haven’t found much of a good use for it but it would definitely be like some of my personal favs are understanding the expense details for various government agencies. Like how much does the NYPD spend on bagels? It’s something that I can answer. There hasn’t been much use but if something unusual and maybe given that we were talking about politics for a while and we use this data set in a very different way but the voter registration data in the United States, which is a public data set. Like everyone who’s registered to vote their address all of this good stuff and it’s quite hard to access and it’s quite hard to structure but it actually gives you the topography of kind of where people live, how densely, how dense are they located to each other, how densely is the population. I think it’s actually a better if not more granular metric than the census, right. It actually gives you like approximate counts, which lets us do all kinds of interesting things. Like we help CPG companies place products like drinks and soups and all kinds of these things based on the profiles of where people actually live and their driving radiuses from businesses and all kinds of things. There is a tremendous amount of waste in that system. That supply chain is not well understood and weirdly voter registration data is like the chicken stock for us in that algorithmic recipe.
Costa: Interesting. I imagine it leaves out all the people that don’t vote. It leaves out all the people that can’t vote.
Oudghiri: Correct.
Costa: How much of a problem has that been?
Oudghiri: Well it’s not a problem because we don’t target person by person. So our use case is always probabilistically what is the shape of this neighborhood look like? So residential neighborhood, how clustered are people to the shopping centers, what’s the average drive time, like all of these things go into calculation. But it’s not like your data set is incomplete so I can’t send these people a piece of marketing or I can’t use it to underwrite them, that’s what we use it for. And yeah, so in that senses is it does the job the pretty well.
Costa: Obviously it’s sort of easy to understand the commercial applications of a lot of this data?
Oudghiri: For sure.
Costa: But you’ve been, you’ve got a running number of humanitarian projects as well.
Oudghiri: Correct.
Costa: Talk a little bit about STAT: Stand Together Against Trafficking and the Polaris Project and what you’re bringing to that effort.
Oudghiri: So this one is one that’s kind of born out of what we’ve seen in the field. So we’ve noticed that much like the rest of the economy and folks really wanting to found businesses and this like revived sense of entrepreneurship that’s also been ported over to the illegitimate part of the economy. So you no longer have like large mafia families controlling most of the crime, or maybe you do, but there’s just a massive proliferation of call them like young founders in the criminal space, right.
Costa: Shady entrepreneurs.
Oudghiri: Shady entrepreneurs. And we’ve noticed in them like a pretty big uptick in human trafficking. Which is like not commonly well understood concept. Like people are trafficked all the time. It could be for farm labor, it could be for you know sex trafficking purposes and basically we started doing this work with the banks and helping them catch these people because they’re one, regulatory obligated to do so and two, you know there are massive liabilities just in terms of fraud and all kind of things that happen when these folks transact in your network. And then we started to see some patterns emerge that would help identify these folks in a more and more and more automated fashion. And we’re always talking to the banks about sharing information. And does anyone here work at a bank? Yeah.
Costa: There’s definitely some banks here.
Oudghiri: Yeah.
Costa: Bank of America was just here.
Oudghiri: Well one thing that’s particularly hard at a bank is sharing information. Now I personally believe that there’s good reasons for that. From a privacy perspective and all kinds of other things. But we were trying to get the banks to initially share, like, “Hey we caught x, y, and z, be on the lookout.” But that turned out to be actually a compliance burden in and of itself. Because if one bank told someone else and then that bank had them in their system, they basically proved that those systems for catching them weren’t efficient enough. So we said okay, you don’t need to share, quite share the target list or quite share the data but what if we sent you, what if we kind of packaged everyone and got everyone to crowd source the queries they used with the external data and the internal data.
Costa: You’re not naming names but you’re saying this is how you find people.
Oudghiri: Exactly.
Costa: We found people this way. You can find people the same way.
Oudghiri: Exactly. So entire industry code. Take say, you know, this is the kind of activity for a nail salon that has resulted in multiple of instances of human trafficking for us. We know that that nail salons or truck insurance companies and kind of help the banks categorize kind of first swath of those. And really relying on Polaris and their expertise and their kind of, their function as an NGO of really raising awareness around this, we set up to build this crowd sourcing tool which a bunch of the banks have jumped on. And it’s in release with a couple of folks right now. It will always be mostly kind of private enclosed within the banks because we don’t want the bad guys to catch on on the tools of the trade. But we’re really excited about it. I think it’s a good step towards sharing information in an industry that’s usually extremely averse to collaborating in this kind of way.
Costa: How many partners have come on? Have you sensed any reluctance? Or are they all like, “Yes, this is exactly what we’ve been waiting for”?
Oudghiri: Actually you know the reluctance when we kind of came up with this paradigm dropped pretty significantly. I think the main stage-gating is the how do we operationalize this in our processes and finding some extremely lightweight ways for them to do so. But we’ve had tons of bank partners notice that, some even want to fund the project now. So it’s kind of like, “Whoa we were just doing this you know out of just kind of seeing this expertise trying to get you all at the same table, if you want to go ahead and run with this.” So there’s a lot of exciting stuff happening in this regards. I think coming up with ways for people to share all kinds of information is kind of what’s necessary for problems like human trafficking where the target is constantly changing. So it’s no one person or no one institution that’s going to have the expertise required to basically follow a new kind of pattern in criminal activity. And listen, a lot of people ask us well why isn’t government doing this, right. Well the reality of the matter is of the data actually sits in the banks, right. Sits in the banks. Sits in the kind of databases that we have and that we productize. Governments are a recipient of this and gets the signal it needs and then sends boots on the ground and you know Olivia Benson from SVU shows up to your client and starts giving you problems but you know there’s a reason why there’s actually a really fruitful and necessary collaboration in between private and public parts of the economy here and it’s the folks that live in that world have often been in between both constantly. There’s like a revolving door between compliance officers and the district attorney’s office and it’s kind of fun to see people motivated by that kind of passion as well.
Costa: So this is going to be up and running, it’s in beta now. It’ll probably launch in a couple of months?
Oudghiri: Yeah. So it’s up and running now. We have like a restricted set of partners that we’re working with mostly because you know we want to get it right and we don’t want to kind of onboard like the entire banking system at once for a pro bono project that, you know, kind of labor of love. But this summer we’ll be going, we’ll be doing a bigger round of it, bigger round of things in these regards. And anyone’s welcome to get in touch with us. You can shoot [email protected] if you’re interested. We have someone, you know, a team of folks dedicated and plenty of collaboration with folks like Polaris. Which is why we decided to partner with folks who had the expertise who could kind of carry this together with us.
Costa: Great. Hicham, thanks for the time. I appreciate it.
Oudghiri: Of course. Of course.
Costa: Round of applause.
Oudghiri: Thank you.
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