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The transaction surveillance analysts have to follow up on each alert, looking at all the relevant transaction data. They should also consider the profiles of the individuals concerned within the transaction, their past financial behaviors, no matter they have declared in “know your customer” and customer due diligence paperwork, and anything the financial institution would possibly know about them. Following up on alerts is a time-intensive course of.
Yes, robots are coming to take our jobs. That’s a very good thing, we must be comfortable they’re as a result of those jobs they’re taking kinda suck. Do you really need to go back to the times of manually monitoring, flagging and investigating the world’s daily bank transfers in quest of monetary fraud and cash laundering schemes? DBS Bank, Singapore’s largest monetary establishment, definitely doesn’t. The corporate has spent years growing a chopping-edge machine learning system that closely automates the minutia-stricken technique of “transaction surveillance,” freeing up human analysts to perform increased degree work whereas operating in delicate steadiness with the antique monetary rules that sure the trade. It is fascinating stuff. Working with AI by Thomas H. Davenport and Steven M. Miller is stuffed with comparable case research from myriad tech industries, looking at commonplace human-AI collaboration and providing perception into the potential implications of those interactions.
