Anti-Money Laundering is the regulatory effort used by various government and international regulators to curb the movement of illegitimate funds by criminals through a legitimate channel. Between 2012 and 2018, EU banks paid over $16 billion in fines due to lax money-laundering checks, the five biggest UK banks have all been fined for money laundering offences, and in 2019 a record number of AML-related fines were issued globally, totaling £6.2bn in penalties. The United Nations estimates as much as 2 to 5% of global GDP is so ‘washed’ per year, a sum that could be therefore as high as $200 trillion. The problem, as Paul Westcott, product director of Customer Due Diligence Solutions at commercial data and analytics firm Dun & Bradstreet, revealed to diginomica is that working out who owns what in global financial systems can be a decidedly non-trivial task. His company was already aware of the issue, but attention became focused after a famous meeting of the G7 group of advanced industrial economies back in 2013 at Northern Ireland, hosted by then UK Prime Minister David Cameron where the problem of identifying fake companies was identified as something the commercial sector needed to take on board.
On paper, Dun & Bradstreet would look like a company in great shape to be able to answer the ‘Who Owns Me’ question, though. After all, it has details of over 300 million companies around the world, the personal details of 115 million individual shareholders and 160 million company principals in no less than 200 countries. The problem, Westcott says, is that its traditional technology approach to working through all that data to answer the ‘Who owns me?’ question didn’t seem particularly promising. Given that a single query on beneficial ownership from a customer could tie up a team for 10 to 15 days unravelling all this, as a new approach was needed. As a result, Westcott and his team started to explore graph technology, a form of working with data that is based more on the networks that connect items and their attributes instead of splitting data into rows and tables. Westcott also cites the easier storage of the kind of network he wanted to build in the graph, as well as the ease of querying he believed he could get, plus good response times. Using the term ‘property graph’ to describe what he’s built, Westcott claims it’s very easy to get the kind of answers he needed, based on the calculation on various levels of risk in an ownership relationship (e.g. 10% is lower than 25%).
Dun & Bradstreet’s graph journey began in 2017, he says, with software from Neo4j bolstered by visualisation technology from a specialist anti-fraud firm that also uses a graph approach, Linkurious. A proof of concept was delivered in about three months, and another two to bolt on the visualisation aspect. Now, he claims, Dun & Bradstreet customers have a holistic view of ‘Who owns me and who do I owe in turn?’, with the system physically separated into so-say ‘causal clusters’ in the various geographies the corporation operates in, with a recent addition to the system’s functionality being a change of ownership detection facility. Next steps include working out a way to get a more ‘360-degree’ view of individuals, as authorities start to work on the problem of working out who controls an entity as opposed to being an owner (or not) on file.