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WHY GRAPH DATABASE COULD BE THE ANSWER TO EUROPE’S MONEY LAUNDERING WOES

04/04/2019

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

 

So far legislation in the Eurozone has failed to stop illicit funds the result of criminal activities from pouring in. At the same time, financial institutions have been unable to put defences in places that have worked, thanks to the open nature of the banking framework. But, graphs could be the simple answer to this growing problem.

 

Emil Eifrem

Scandinavian banks in particular have been in the spotlight, with some unfortunate scandals including the head of Sweden’s oldest bank stepping down after allegations of money laundering under her watch, while other revelations have been negatively impacting on Danish and Norwegian financial institutions.

 

Following these money laundering trails is difficult. Graph software, however, has the power to join the dots and read relationships in data that previously financial institutions have been unable to do. Uncovering questionable links through hidden patterns. Graph software, after all, was the technology used to investigate the highly complex Panama and Paradise Papers which uncovered the financial dealings and mis-dealings of some of the world’s wealthiest people.

 

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.

With graph databases, financial institutions aren’t tied by semantically-limited data models and expensive, unpredictable ways of running queries through joins as required by the relational approach. On the contrary, graph technology supports working with extensive named, directed relationships between entities (also known as nodes) that offer a far richer semantic context for developers to work in. This provides much more granular detail. At the same time, graphs are mega fast at exposing patterns, giving financial institutions a truly trackable and in-depth picture of their assets and associated relationships.

Getting ahead of the money launderers

Financial criminals are clever and cyberspace gives them many places to hide. To flag up money laundering, financial institutions need to know exactly where funds are coming from and where they are going to at all times.

But financial criminals are very sophisticated at using misdirection to make it very difficult to track funds from their start to end point. They are adept at creating intricate networks of identities which makes it very hard for financial institutions to sift them out from legitimate transactions in gargantuan amounts of data that flow through banking systems every day.

 

Yes, there are anti-laundering solutions out there, but they have not been developed to link the dots between the large number of steps criminals might take to launder money. Often they require laborious manual processes which are time consuming and costly. With graphs, however, anti-money laundering teams in financial institutions can model companies, accounts and transactions far more efficiently to pinpoint possible money laundering.

 

Graph databases have the power to analyse know relationships and uncover hidden links and networks. This is faster and much more effective and productive than trying to run queries across tables that are joined together.

 

A way to map all sorts of complexity

 

With the cyber threat landscape widening, money laundering is only going to get more pervasive and sophisticated unless action is taken. With our connected world increasing the amount of data financial institutions have to deal with, it is imperative they can analyse their data in real-time to have any chance of stopping illicit funds going into accounts.

 

Graph software is capable of bringing together various pieces of the money laundering puzzle  to create a logical picture. Using graph analytics, financial institutions finally have a tool that can help them fight money laundering from all corners.

 

 

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Finance Derivative is a global financial and business analysis magazine, published by FM.Publishing. It is a yearly print and online magazine providing broad coverage and analysis of the financial industry, international business and the global economy.