BioCatch unveils Connect Portfolio in attempt to reduce financial scams 

BioCatch has launched a “first of its kind” platform designed to combat financial scamming. 

Known as BioCatch Connect, it is the first fully integrated fraud and AML product portfolio that proactively detects and accelerates targeted interdiction of fraudulent activities and identifies criminal actors targeting customers of the world’s largest brands. 

The announcement comes at a pivotal time for the global financial community, as schemes such as authorised push payment and remote access scams rose by 30% last year according to data from the Israel-based organisation. 

More recent research by Datos Insights (formerly Aite-Novarica Group) found that two-thirds of financial institutions have made organisational changes within the last two years to improve collaboration and shared services between their fraud and AML practices. 

Trace Fooshee, Strategic Advisor, Fraud & AML at Datos Insights said; “A trend has been building around how financial institutions approach the challenge of managing trust relationships with their customers in a manner that is more deliberate and formally structured around preserving, protecting, and promoting customer safety. 

“This trend has been accelerated by the alarming increase in authorised payment fraud, synthetic identity fraud and mule activity that is increasingly prompting regulators, legislators, and payment networks in many markets to consider shifts in liability for reimbursing scam victims to sending and receiving FIs.  

“The shift towards a more holistic corporate strategy for mitigating all aspects of financial crime is increasingly reflected in trends in how FIs are transforming their fraud, AML and cybersecurity units.” 

The BioCatch Connect portfolio reimagines the traditional technology approach to fraud fighting and money-laundering investigation. 

Unlike legacy fraud detection solutions, BioCatch Connect puts behavioural biometric intelligence at the centre of its artificial intelligence and machine-learning models rather than as a secondary signal. 

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