TRENDING another , then it is hard to see how they could ever be brought to book .
Fred Crehan , Area Vice President , Emerging Markets at Confluent
Data in motion
Investigators , human or machine , must be able to pick up on discrepancies like a transaction originating at an unusual time in an out-of-the-ordinary location . These datapoints may not give enough of a picture on their own , but when combined with the presence of a new recipient and a recent password change , they might indicate activity worthy of a red flag . Context is everything .
In BFSI uses cases , data is often not available in real time because of regulations or common practices surrounding transactions . When it does become available , it will be in different formats from different data sources . This calls for more collaboration between data custodians so that evidence can be aggregated and analysed cohesively as it becomes available .
We must move from a transaction-centric , data-at-rest processing model to a data-streaming architecture that can support a real-time , event-driven approach powered by AI that gets smarter the more cases it investigates . Otherwise , we are condemned to be forever investigating fraud after the fact .
Every bank , every clearing house , every crypto exchange , every organisation with skin in the AML , CFT game must build a proactive system capable of detecting anomalies in real time and empowering preventative actions up to and including the blocking of a transaction . This requires rich data gained through continuous collaboration , leading to contextual views of a stakeholder ’ s domain . Sophisticated machinelearning models must be available to assign useful threat scores to anomalous activity . Timely supply of data that allows all of this will ensure that AML compliance officers and fraud investigators can efficiently decide where to concentrate their efforts .
FSI entities are currently finding it difficult to keep pace with criminals , never mind remain one step ahead . As fraudsters up their game , those that would stand against them must stop bringing feather dusters to the swordfight . We must routinely get to know not just the basic data of the transaction but the full circumstances behind it , customer location , devices , software , travel patterns , recipients , social network , associated parties , be they friends or bad actors , favourite shopping haunts , and active subscriptions .
The data is out there . But if it is not all available at the right time in the right context , then the fraudster slips through our fingers yet again . And given the potential costs to the system , we can ill-afford such repeated escapes . p
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