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How Graph Databases Fight Organized Crime

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How Graph Databases Fight Organized Crime

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According to Philip Rathle on The New Stack, graph databases can be used for more than just finding football stadiums. In fact, they can help with some pretty interesting problems: breaking up organized crime, for example.

The example Rathle relies on for this article isn't the Sopranos-style organized crime you might be picturing, but rings of bank and credit card fraudsters. These are perpetrators of "first-party fraud," defined by Rathle as people who "...apply for credit cards, loans, overdrafts, and unsecured banking credit lines with no intention of paying any of them back."

This type of fraud is a major problem for financial institutions, largely because of the way fraud rings mirror the strengths of graph databases: a small number of real addresses and fake phone numbers can be tied together in different combinations to create a vast web of dummy accounts attached to fake identities.

This structure of fraud is hard to detect, Rathle says:

...traditional methods of fraud detection are either not geared to look for the right thing: in this case, the rings created by shared identifiers. Standard instruments—such as a deviation from normal purchasing patterns—use discrete data and not connections. Discrete methods are useful for catching fraudsters acting alone, but they fall short in their ability to detect rings.

And particularly using relational databases:

Uncovering rings with traditional relational database technologies requires . . . a set of tables and columns and then carrying out a series of complex joins and self-joins. Such queries are incredibly complex to build and expensive to run. Scaling them in a way that supports real-time access poses significant technical challenges, with performance becoming exponentially worse not only as the size of the ring increases but also as the total data set grows.

This is where graph databases come in uniquely handy. Rathle points to languages such as Cypher as providing a semantic that lends itself to navigating these types of relationships, and it is fairly clear, as Rathle demonstrates with a visual, how graph relationships can pinpoint rings.

Take a look at Rathle's full article for more details on how graph databases can be used to traverse complex relationships and detect fraud rings.


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