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The application and use of financial crime analytics is widespread, ranging from risk assessment and detection engines to investigative tools and reporting. This allows financial institutions to better quantify and categorise their risk exposure, assess the effectiveness of their financial crime programmes, and detect and deter criminal behaviour. Our client is a global banking and financial services company headquartered in both London and New York. It has retail, corporate and investment bank in most significant countries and territories worldwide.
Traditionally financial services companies organised their transactional data by country and line of business. In the modern financial crime environment this is no longer adequate. Criminals, sanctions evaders and money launderers operate globally and will quickly reconfigure their activities to escape detection.
Our client needed to build an effective global store of customer’s payments and allow teams of users to quickly identify themes, trends and look at individual customer activity.
With SWIFT MT103, MT202 and MT202COV messages at the heart of the solution, we built a business-as-usual process to extract the payment information from our client's core banking system. A central database was constructed to store the payment information, along with satellite databases where local laws prohibited the export of information.
The database was optimised for certain predefined searches by customer, by value, by country, etc. Flows down specific corridors and in certain currencies could be readily understood. We also allowed for target ‘free-text’ searches to look for identical or similar phrases or patterns, all on a global scale.
The user community could easily interact with the information in an intuitive and natural way.
Using big data and other new analytical techniques to detect patterns of behaviour allowed our client to gain a deeper understanding of their customer activity across the world. Non-traditional users of financial information, including internal audit, could utilise the power of the centralised database to test the effectiveness of the payment control environment and identify any weaknesses.