How To Detect Money Laundering – How to Identify Money Laundering with Knowledge Graphs and Reasoning The schemes and plans of criminals are constantly evolving as they strive to stay one step ahead of the public, but the efforts of A law-abiding society makes this even more difficult.
The schemes and plans of criminals are constantly evolving as they seek to stay one step ahead of society, but the efforts of the law-abiding community make this even more difficult. Unscrupulous people have been forced to expand their efforts to avoid capture, spending more time and energy to keep their newfound wealth than to simply steal it in the first place. Faced with this predicament, they often turn to money laundering — a process by which illegal ‘dirty’ money is ‘cleaned’, allowing the owner to spend it freely.
How To Detect Money Laundering
The first, depositing, is the act of depositing ill-gotten gains into the financial system, whether through a bank, store, or any other legitimate business. The last one, mixing, is the last action where the newly refined money is involved in the proper trading — putting the method to the test. While both are interesting subjects in their own right, it is the filling of this robbery sandwich where our interests lie, and we can offer relief.
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Laundering is a true ‘cleansing’ process, a process by which money is moved further and further away from the crime step by step. There are many ways in which this can be achieved, from purchasing tangible items such as gold, gems, or art, to designing services for paperless businesses, such as laundry cars. One of the easiest and most effective is to deposit money.
In this way, dirty money is passed from one criminal to another and so on, until finally its history is confused, and it seems clean, simple law. At first glance, this process may seem simple to identify, but there is a good reason that is plaguing the financial sector — the huge and complex problem of identifying several suspicious transactions among hundreds and thousands of legitimate transactions taking place.
Great machine learning solutions are often thrown at the problem and give good results when faced with complex projects. However, for simple cases like investing, this is overkill and does not guarantee the detection of well-known patterns — which may be missing what is formally visible. Knowledge and reasoning graphs offer a good choice, and the best of the bunch is RDFox with its high performance capabilities.
Let’s say, for example, there is a crime ring that uses mules where some members are known by the authorities to be unethical, but others are not suspected. The challenge is to identify the whole group without dragging out innocent people. To achieve this, we can look at their bank transfers — to reveal the flow of money connecting the offending parties.
Pdf) A New Algorithm For Money Laundering Detection Based On Structural Similarity
Regardless of the method we choose to solve this problem, the first step is to collect the basic elements of the business, namely the date, value, creator, and beneficiary — information that the entity can easily have.
For demonstration purposes, we presented a sample of business data where there are fraudulent chains, and some organizations were known to be suspicious. All names used are random and are not related to anyone with the same name.
Finally, we want to see the flow of money that transfers wealth from one criminal to another — known or otherwise. Of course, it’s rare to see all the money transferred to a single transaction, but as this is a slight change in our approach, and for the sake of clarity, we’ll pretend it’s normal. . Having said that, it is an important part of the real scam so it would be a waste to explain it in detail. The practice of dividing the aggregate is known as ‘structuring’ or ‘smurfing’ and is often used in conjunction with mules. Small amounts are more common than large amounts, so it is not possible for organizations to devote the same resources to tracking them. As a result, it is much easier for criminals to hide several small transactions than one large one.
With our goal in mind, we must explain what links related interactions in such a way that they are malicious. The principal property is indeed a joint member, being the heir of one and the founder of the other. From here we will select some parts with some tolerance, and start creating chains of these complex interactions. It is in these remaining facts and their patience that issues arise, and reasoning.
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It doesn’t take a master criminal expert to make connecting the dots more difficult, so gangs often use different strategies to hide the chains or distort them enough to escape detection. There are a few common pitfalls in the aforementioned business context that we want to be aware of. They highlight the fact that a rational system is best suited to a problem like this, and why a solution without it is difficult and slow. One is that the amount transferred often varies from link to link, and the other is that the order in which transactions occur does not necessarily follow the order of the chain itself. For example, a person can pay before receiving their deposit.
It may seem at first glance that SPARQL’s property paths provide an adequate solution. However, there is an important drawback: we cannot set standards on sales revenue and we have no way to track cash flow as an aggregate. We would end up creating an infinite number of chains, many of which would have innocent people who would exchange money with our known criminal. Because we cannot include other variables in the property paths, there is no way to distinguish between a $10,000 misdemeanor and a $10 birthday present. nephew Despite the differences, this would be added to the chain because of the shady person’s role. This is only reinforced as the chain continues, it begins to link the innocent with the innocent, marking them both as suspicious. Clearly there is a problem here.
Alternatively, we can use a series of INSERT queries that mimic the logic process, each adding links or other data. However, the issue here is one of scale. Even if we compress the maximum length of the chain (which we will), the process can take such action from the iterative process that achieving a result of sufficient length will not be possible. possible. In addition, the queries would have to be executed in the correct order for the chain to be found correctly since the loss of listing methods here would result in the chain breaking up with no way to find it. it fully integrates.
As you can see, recognizing these patterns simply and without reasoning is an absurdly tall order that veers into the realm of the absurd. However, with reasoning, it becomes easier.
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First of all, we can mark a transaction involving a suspicious party as suspicious. This single transaction node can be considered as a chain of length 1 which then becomes the anchor from which we build the rest of the chain, adding the following transaction nodes that match the criteria. Assuming that our anchor is not always at the beginning or end of the chain, we must look forward (to the heir) and backward (to the creators) to see the whole picture. By doing so we create two sub-chains, each from a suspicious transaction on a different side as well as the full chain.
As explained before, we need to define what connects the two transactions, and also the obvious property is that the beneficiary of one is the creator of the other. Next is to ensure that the value in each field is the same — now that is a very easy task. By specifying tolerance, all eligible transaction pairs will be found incrementally. The sub-chains start to take shape as we add another constraint, now at the time they occurred. We don’t need to specify any details about the order, just because it happened at a certain time — discount prices separated by years but subject to certain restrictions. This process continues until there are no more suitable interactions or we reach a self-imposed limit on the length of the chains.
Finally, with two chains now complete — one starts with a suspicious transaction and the other ends with it — we connect them, creating a chain that covers the entire criminal ring.
With the reasoning power of RDFox this whole process is done in a random manner and at a scale with as much detail as can be expected in real-world situations. As for the most important thing, speed is maintained, and the rules mean that the system is easy, it can be done to give the best results.
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As we noted earlier, solutions often suffer due to the size of the process, but this is not the case with RDFox. After applying our method to a small dataset
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