Not only has the sanctions landscape become much more volatile, it has also increased in complexity. This has necessitated financial institutions to implement more sophisticated screening solutions to handle the complexities.
When sanctions were first enacted in the nineties, they were applied to whole countries, such as Cuba. However, the situation moved from whole country sanctions to targeted sanctions, such as sanctions covering specific entities, transactions, materials and so on.
This shift meant that financial institutions had to implement more complex rules that would distinguish between those name matches that had to be considered and those that could be safely ignored.
The situation has become even more convoluted recently with the latest wave of sanctions against Russia. Previously, screening solutions only had to compare names in signaletic databases or transactions against names detailed on a watchlist. With the sanctions against Russia however, the Office of Foreign Assets Control stipulated that entities where sanctioned persons have more than 50% direct or indirect ownership are also to be considered off limits. This is even when those entities themselves are not on the list.
These new rules change the way that screening systems collect, sort and present data. They require new methods of information gathering, data correlation and visualization. This what future R&D efforts need to focus on.
Future solutions will have to be developed that incorporate ways for organisations to amass data from different online sources and implement big data components. Screening systems will need the ability to retrieve, store and analyse data in order to detect relationships between listed and non-listed entities. It’s no small order.
What’s next? Post 10, the last in this series of identity matching, will summarise the main points of previous articles.
Also in our series on identity matching