AI, Machine Learning

Rules Are Meant to Be Broken: Machine Learning vs. Rules Based Systems

Posted by Deepanker Saxena on Aug 6, 2018

There is a famous saying that goes “Learn the rules like a pro, so that you can break them like an artist.”

Identity verification, Manual Review

Busting 4 Identity Verification Myths

Posted by George Tubin on Jul 23, 2018

The traditional methods of identity verification—for instance when a customer wants to open a new bank account or start doing business with a new institution— are, unfortunately, stale. They just don’t work anymore. Part of the problem is that the data the traditional identity verification providers - the credit bureaus - use to verify users is the same identity data that gets stolen in breach after breach. How does it make sense to use social security numbers, addresses, and birthdays to verify an identity when that same information can be easily bought on the black market? The truth is, it doesn’t.

AML, KYC, Data Breach

Data Breaches Are Making the Credit Bureaus Obsolete

Posted by Sunil Madhu on Jul 16, 2018

We’re all familiar with the credit check, that ubiquitous step when we’re applying for bank accounts, loans, or credit cards. But while credit checks may be a well intentioned means of identity verification, the truth is that they’re outdated and unreliable—and increasingly a playground for fraudsters. Something needs to change, and fast.

AI, Machine Learning, Know Identity Conference

It's Time for AI & Machine Learning to get Un-ruly

Posted by Socure on Jul 6, 2018

At the Know Identity Conference held in March 2018, Socure’s founder and chief strategy officer, Sunil Madhu, shared his perspectives on the use of machine learning (ML) and artificial intelligence (AI) for identity verification. Sunil discussed his unique insights, gleaned from Socure’s market-leading predictive analytics platform, on a panel with the CEOs of ThreatMetrix and Emailage. Sunil’s views were often not shared by his fellow panelists, leaders of traditional fraud prevention companies that rely primarily on human-developed rules-based systems. Sunil argued that human-developed rules hinder effective machine learning, and in order to properly address fraudsters machines must be allowed to go beyond human intuition.

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