How to use graph technology to detect money laundering

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May 17 2017

Money laundering happens across the globe, it underpins criminal and terrorist activities and it is on the increase leaving organisations, governments and law enforcement agencies firefighting. That means creating endless ‘millefeuille’ layers of fake identities, middlemen and multifarious financial schemes, making it extremely difficult and time consuming for law enforcement agencies, financial institutions and fraud detection professionals to correlate evidence against them.

Sifting through big data to find any odd or conflicting patterns that may lead to recognising a money laundering transaction is an enormous task. Relational databases are not flexible enough to tackle changing, complex data structures. NoSQL databases don’t underscore relationships as primary citizens. In both cases, relationships in the data are either missing or hard to compute.