Graph Database Use Case: Fraud detection

Fraud and financial crimes are a form of theft or larceny that occur when a person or entity takes money or property for their own use, or uses them in an illicit manner for their personal benefit. These crimes typically involve some form of deceit, subterfuge or the abuse of a position of trust, which distinguishes them from common theft or robbery.

For most countries, one of the financial crimes which is more difficult to prevent, detect and prosecute is money laundering. Money laundering is the process in which the proceeds of crime are transformed into apparently legitimate money or other assets. These kind of processes usually follow specific transaction patterns that can be simplified as the following (see figure 1):

1) Collecting the money coming from illegal activities.
2) Placing it into a depositary institution.
3) Adding a layer to the transaction (such as a payment of a false invoice or a loan to another company).
4) Integrating the money into the financial system by purchasing financial/industrial investments, luxury assets etc.


Figure 1 – Diagrammatic description of a money laundering scheme by ExplicitImplicity under CC-BY-SA-3 and GFDL.

All the information regarding these transactions is registered by the banks and financial entities that take part in the process, and it can be represented as a graph, being each entity (person, company, organization…) involved a node and each transaction an edge of the network. Then, a fraud detection application would compare the before-hand known transaction patterns of previous prosecuted fraud cases with the patterns of our network to analyze if there are common points between them. Figure 2 is an example of a graph representing a money laundering fraud.

money_laundering_graph (3)

Figure 2 – Money laundering graph example.

In this case, Subject X transfers the illicit proceeds to the associate Company Y (placement), which pays a false invoice coming from Company Z. Company Z makes a loan to Company Y for the same amount than the false invoice, adding a layer to the process and making the fraud more difficult to spot. More layers can be added at this point, for instance, purchasing chips on a casino and changing them again for their value. Then, Company Y invests on a legit financial institution to integrate the money into the financial system, and finally it withdraws the capital transferring the earnings back to Subject X, who receives the “clean” money. As you can glimpse from Figure 2 a graph representation of the information would help us to more easily identify the loop that makes Subject X suspect of a possible fraudulent transaction.

Although all the connections happen necessarily at a specific point of time -e.g. Company Y cannot transfer the “clean” money to Subject X before making all the other transactions- note that we don’t need this information to compare one pattern to another.

Other similar use cases involving graph databases for fraud detection include tax evasion and illegal funding, where the key aspect also lies into searching known irregular patterns in the transactions graph.

If you want to know more about graph database use cases, scenarios and success stories, you can search for the “use case” tag on the blog or visit the “scenarios” section of our website. Remember that you can download Sparksee 5.1 for free and use it for your project!

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