Anti money laundering (AML): the network graph analytics approach

The fight against fraud and AML often hits a ‘brick wall’ when encountering labyrinthian international, corporate structures. Even well-known brands such as Google and Starbucks, use controversial, though currently accepted, international structures to optimize capital flows and to reduce tax liabilities, for instance the so-called Double Irish or Dutch Sandwich.

Due to this complexity, increasingly it is a challenge for regulators to determine whether a particular construction, and associated transactions, are acceptable or questionable. A part of the problem is that the tools for tracking capital flows between structures are typically linear, following paths on a static process diagram. When transactions are circular, or involve more than several ‘hops’, the linear method breaks down.

The reality of global conglomerates is that they are typically less a top-down organization, and more a complex, interconnected network of cross-ownership, control, and co-investment. Auditing such labyrinthian beasts is enough to bring a grown auditor to tears.  This is where network ‘graph analytics’ can be invaluable. By tracking complex structures as native networks, fraud and AML investigators can simplify the process of understanding and tracing complex transactions.

As an example, Samsung Group, the South Korean multinational conglomerate, is famously known for the complexity of its interconnected cross-ownership structure. Samsung has run into antitrust, bribery, tax evasion, and embezzlement accusations and scandals. A difficulty facing regulators is the complexity of the partial ownership structures involved. As per a recent Economist article attempting to explain the situation, “for example, the group’s holding company, which has just changed its name from Samsung Everland to Cheil Industries, owns 19.3% of Samsung Life, which owns 34.4% of Samsung Card, which owns 5% of Cheil.… This corporate hairball has let the Lees exert control over the group with a stake of less than 2%.” samsung

Samsung Group cross-ownership structure (Credit Suisse)

Indeed Samsung Group is much more a network of interconnected interests and ownership. As such, representing it natively as a network graph allows for a greater degree or insight and control. As an example to the use of graph analytics, we translated the complex Samsung Group ownership structure map into a network graph. The below visualization is via the open source network visualization tool Gephi.


Samsung Group cross-ownership structure in Gephi network visualization

More than a pretty picture, by storing the structure as a native network graph, we can go much further. By storing the network structure in a graph database, such as Neo4J, we set up a structure which allows for for deep quantitative analytics.

Once in a network format, using graph mathematics and network analytics, we can examine the network itself for classical network properties such as classical measures of centrality, such as degree and betweeness. Across many case of international constructions, it may be possible to correlate such measures with a propensity of fraud. This would involve examining known cases of fraud in conglomerates and examining the correlation with particular network measures.

Beyond this, forensic accounting is often flummoxed when encountering complex circular money flows between subsidiaries and cross-ownership structures. The native graph structure can also be used to input, track and calculate transactions between the businesses, and to summarize and aggregate the impact of these transactions across complex chains.

For instance, if profit distributions flow from one company to another via many different paths, these chains can quickly be queried and calculated using a graph database. Graph databases are a promising, powerful tool for tracking and mitigating complex cases of international fraud involving international business structures. The tools and expertise for powerful AML are available and we are ready to help you deploy!

Want to learn more?


Samsung Group cross-ownership structure in Neo4J graph database

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About SARK7

Scott Allen Mongeau (@SARK7), an INFORMS Certified Analytics Professional (CAP), is a researcher, lecturer, and consulting Data Scientist. Scott has over 30 years of project-focused experience in data analytics across a range of industries, including IT, biotech, pharma, materials, insurance, law enforcement, financial services, and start-ups. Scott is a part-time lecturer and PhD (abd) researcher at Nyenrode Business University on the topic of data science. He holds a Global Executive MBA (OneMBA) and Masters in Financial Management from Erasmus Rotterdam School of Management (RSM). He has a Certificate in Finance from University of California at Berkeley Extension, a MA in Communication from the University of Texas at Austin, and a Graduate Degree (GD) in Applied Information Systems Management from the Royal Melbourne Institute of Technology (RMIT). He holds a BPhil from Miami University of Ohio. Having lived and worked in a number of countries, Scott is a dual American and Dutch citizen. He may be contacted at: LinkedIn: Twitter: @sark7 Blog: Web: All posts are copyright © 2020 SARK7 All external materials utilized imply no ownership rights and are presented purely for educational purposes.

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2 Comments on “Anti money laundering (AML): the network graph analytics approach”


  1. From the Community: October 2014 - Neo4j Graph Database - November 7, 2014

    […] Anti money laundering (AML): the network graph analytics approach — Scott Mongeau […]

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