7 Reasons Why Effective Compliance Management Is Only Possible with a Graph-Based Solution Part212/20/2018 We're a pretty young company, 10 months old. We are a compliance company with a strong focus on anti-money laundering. So far within the first 10 months we are representing 500 shops mainly in continental Europe. We're targeting further 250 by the end of the year and for Q1 next year we're looking at 1000 point of sales we try to represent. What are we doing? We're a one stop shop for everything compliance related https://www.casinoslots.co.nz/jackpotcity-casino-review.
We do the risk analysis for the company. We establish an entire compliance organization into the company so we outsource and re-implement. We put standards and policies out. We provide AML officer so it's pretty legal work as well. The problem is with the volume we are working in. And with the regulations our customers are facing is no way we can do this without technology. So we have some tiny technology solutions we are providing such as a whistle-blowing system. An e-learning platform. But as well we're working with graph technology. One of the key challenges here is besides the transaction monitoring if you look at the gambling industry even in our country on a game day we have hundreds of thousands on transactions every day. Then you need to have a proper case management. Something that Structr Neo4j is building up for us as well. And finally you have the so-called know your customer process. Most of you might know about it. Who's the business partner I'm working with actually? What do I know about him? Who's the ultimate beneficial owner behind it. We take this one out now for a second and my colleague Julian will be talking about this. How we are approaching this with graph technology. - Hi. My names Christian. Well let's imagine you're the regional manager of Los Pollos Hermanos and the nice gentleman Mr. Walter White comes to you and says hey I wanna be part of your franchise. What do you do? You haven't watched Breaking Bad. You only receive an application a couple documents and you have publicly available information. Now for those of you who listened to the keynote this morning I think Emil quoted the professor Farlow said if I wanna tell you who's a smoker, I don't want the information about this person I want the information about his friends. And that's what we also do at KERBOEROS. And we call it our Know-Your-Customer-Graph. Or know your franchisee graph. And if you look at, okay laser pointer. Oh yes it works. Look at the graph of Walter White we can see a couple of things. He used to be a teacher. He used to be a shareholder, Gray Matter Technologies. High-tech business I think was stem cell research. But nowadays he's mostly involved with two businesses. A car wash and also a fumigation service Vamonos Pest. Now the car was he runs jointly with is wife. That looks normal. But if you look at the fumigation service you see well there are two of his business associates they're a little bit shady. You have Mr. Pinkman who has a known criminal record. Drug dealing and you have Mr. Ehrmantraut, who was a former police officer who left the force on allegations of corruption. So there's media reports. So just by looking at this and no further information you can tell, well maybe I don't wanna, not go into business with Walter White immediately. Well we know that we don't. But a potential regional sales manager would not but we would know how to trigger an enhanced diligence for sure. Now let's take a look how this works in a more real life example. What do we do is usually we, we build a graph. We take information that we receive as part of the due diligence process. We also use publicly available information. So we got ID's, we got registry records. Contracts like rental contracts. We take all this data. We analyze it, you know we look at who are the actors, the companies, the people, locations. And then we kinda collect that data and enrich it with media reports. Some open source intelligence. And we build a graph. This hypothetical example you have at the top here our franchisee that will be our client and you have here the alpha LLC as a potential franchise partner. Now in a traditional know your customer process what do you need to look at? You need to identify and verify your partner. You need to identify the ultimate beneficial owner, the so-called UBO. And you need to identify the sources of wealth. Where is the money coming from? And do some background checks like PEP checks and sanction checks and everything. Now in our example, what we could do is you know get a business record for the LLC it would see it has two shareholders. Some majority shareholder. A minority shareholder. The minority shareholder has below 25% so that's the limit under which I do not have to identify the person necessarily. Or not fully and I have majority shareholder Mr. John Doe up here who is also CEO. So I check them I verify I get his ID. And we can assume he has some legitimate sources of wealth and we're fine we're done. That's more or less the traditional process. Now why do we build the graph? Because of the interesting part right here. Let's say this is a restaurant chain. Here are three locations. And we look at the locations, look at the contracts and we see oh well. All locations lead to the same landlord. Down here which is not unusual. That's a common occurrence. What is not so common that's what you only see in the graph is that actually this landlord shares the company's seat with the minority shareholder and if we look further we also see that the landlord's owned by a company who in turns owned by the owner of the minority shareholder. So what we kind of see here is a very traditional way of doing some layering of the money. So you have your restaurants there the money comes in. The illegal proceeds are getting mixed up. And instead of having high revenues for the restaurants, for the company what he do is you kind of siphon all the profits, or at least part of it it must still seem legitimate through overpriced rents out of the companies. Now overpriced rents we are in New York I come from Berlin and either city I think overpriced rents are the norm. So that really isn't that suspicious. And then you transfer the money via those companies to the ultimate actual beneficial owner who is not John Doe. But actually Jane Doe. So first this process just gives us a fuller picture. Because when we analyze it right we just don't look at the potential partner itself. What we also look at is a graph. We see are there any interesting personnel in the second or third row. Or are there businesses that are unrelated. But also are very cash intensive. So by that we can say okay the risk for this potential partner is higher, lower, and we have a fuller picture. A fuller picture is not necessarily only important for the Know-Your-Customer process but also for the risk management itself. Because we wanna have that network. We wanna see that network because the risks that are within that network they don't necessarily stay with one node. Like, oh. Uh. Yup. So if Mrs. Jane Doe is for instance a known drug dealer we wanna know that. Because that risk can spread easily to actually the person that we are in direct contact with as a franchise organization. And we want to prevent that we are being named in the same breath as that person. Especially Christian talked a little bit about reputational risks, reputational risks they don't adhere to the same standards as legal proceedings. As prosecutions. Well because they are a reputational and we all know media tickets get out quickly and the headline is a lot juicier when I write let's say it's a restaurant chain that like Burger King helps the money laundering then I wanna say well Jane Doe was convicted of money laundering. So what you wanna do is with especially with AML you wanna look at the network. You wanna see where are my risks and how can I manage them risks in themselves are not bad, but you have to deal with them. Another thing we do a little bit differently is we don't draw only pretty lines but for us it's very important that we document those lines. So most of our relationships our edges between our nodes they are actually hyper-relationships where there is some kind of proof in the middle. Usually it's a document where I say okay I have contract, or I have an ID, or I have a record. That actually verifies this edge, this relationship. Because for us it's important if law enforcement comes or if an audit comes. We need to prove that the graph we built and the risks we found or did not find was all that we could do at that particular moment. Especially since most investigations or even audits they come years after the fact. So whenever something today happens I need to document today how a network looked. How a shareholder structure looked because it will be investigated a year or two or five years from now. And at that time I need to show okay at this point in time I knew what we were actually dealing with. Or did not. So what were our challenges? I think some of them are pretty universal.
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AuthorCharls Roberts is author of this blog, avid traveler and travel blogger. He writes articles for Eleventy traveler blog. He is also a Problem solver. Social media fanatic. Webaholic. Bacon specialist. Writer. Lifelong analyst. Incurable reader. ArchivesCategories |