The author is CEO and Co-Founder of Neo4j, the world’s leading graph database company
In the past twelve months there have been alleged and confirmed cases of money laundering in Europe that have made global headlines. Neo4j’s Emil Eifrem believes that graph technology can help track and stop these fraudulent money flows
Using graphs to target money laundering
Graphs have the innate ability to unearth possible money laundering. Why? Because they differ from traditional relational databases in that they specialise in managing the relationships between a large number of data points, enabling the graph system builder or data investigator to better manage, read and visualise their data.
Relational databases have a role in indexing and searching for data, supporting transactions and performing basic statistical analysis. But, they were not created to connect the dots and identify links in relationships which are essential in detecting and analysing money laundering networks.
Keywords: anti-money laundering • bank fraud • banking • detecting fraud • graph database • nordics