For example, if a customer transfers a large amount of money to a conflict-stricken region, that would probably raise alarm bells in a rules-based system. If, however, the sender and receiver accounts are trusted, this behaviour might be routine for this particular customer. By applying analytics that monitor and interpret how each customer makes financial transactions, institutions can effectively fight money laundering, and improve the customer experience by avoiding flagging "risky" transactions that are actually perfectly legitimate.
Letting analytics help
For these innovative technologies to work effectively, it is important that they are compatible with a business' processes, as well as quick and easy to deploy. Following a full business risk assessment to understand the customer and their operating environment, the solution needs to be fully integrated into the customer's IT environment using their platform, payment system, and data stores.
Furthermore, the banking sector has to manage the huge and constantly evolving challenges associated with combating money laundering whilst delivering excellent customer experiences. So, AML technology cannot afford to adversely impact how easy it is for customers to make legitimate transactions.
Innovate and protect
For banks, the ultimate goal is to ensure that potential cases of money laundering or the financing of terrorism are identified without troubling honest customers. AML is an issue that affects everyone, from world leaders through to bank clerks, so we all need to work together to combat it. Data science can help to achieve this while limiting any impact on honest, every-day customers.
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