3D Secure (3DS) is a security layer in which issuers authenticate online shoppers before they are authorized to complete an online transaction. Since the card issuer authenticates the user, liability for fraud rests with the bank rather than the merchant. In earlier iterations, the technology asked users to authenticate themselves with a password for every transaction. As a result, merchant adoption was low, despite the liability shift.
Recently, however, companies have attempted to evolve 3DS into a less intrusive solution that retains the advantages. This week, a Silicon Valley firm has launched a solution that leverages machine learning to flag only the riskiest transactions, which then are returned for 3DS authentication by the consumer. The company, Simility, calls its solution Adaptive 3-D Secure and said using 3DS as a final step optimizes its advantages.
“What we saw over the years is that 3D Secure is a very reliable way to block online fraud, but it challenged too many transactions, which is a bad idea and leads to a lot of checkout friction and cart abandonment,” said Simility CEO Rahul Pangam. “Simility pinpoints only high-risk transactions, so instead of checking hundreds of potentially suspicious online purchases, merchants only need to invoke additional security on about 10 percent of those, and 3D Secure is a great way to protect the merchant in those cases. As more e-commerce shifts to mobile, new tools are needed.”
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