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Anti Money Laundering (AML)

Analysing big data to find the odd or conflicting patterns which may help identify a money laundering transaction is an immense and expensive task. Indeed, it is estimated that financial institutions in the US spent roughly \\$25 billion on AML annually. Traditional databases are not flexible enough to tackle these complex data structures that are always in flux. These siloed databases store data in separate tables and columns, requiring complex code and business/technical knowledge to join up relationships and understand meaning. A graph database, however, stores the data as “nodes” or relationships, which can be easily linked without specific knowledge of the often very limited and inflexible underlying database schemas.

Graph technologies are powerful enough to join the dots and identify discrepancies. Take for example the Panama Papers.

The International Consortium of Investigative Journalists (ICIJ) had more than 200 journalists in 64 countries working on cross-border investigations. This required the processing of 2.6 terabytes of data and mining 11.5 million files. The ICIJ used graph technology to uncover the connections because it was able to make the link between relationships. These, journalists did not have to be data scientists or experts on the databases to work with a graph representation of a dataset. They could simply click their way through the data while following connections.

That is an example of an independent use case of the EKG. Most financial organizations have been monitoring AML in the same way many years. Typically, organizations struggle with very high levels of false alerts and reports to the regulator, in spite of recent scandals, have remained stubbornly low.

Clearly a new approach is needed if organizations are going to tackle financial crime and terrorism.