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Deep Labs Featured in RIS on How Retail Needs A New Approach To Fraud Using Artificial Intelligence

Robber in Black Hood

Deep Labs Chief Strategy and Product Officer, Michael Lynch discusses how account opening fraud is a rapidly increasing challenge for issuers due to the plethora of identity data available to fraudsters. The 2018 Identity Fraud Study by Javelin Strategy & Research shows that the number of identity fraud victims increased by eight percent in 2017, with the amount stolen totaling $16.8 billion.

Some of the top financial institutions employ specific and often expensive point solution providers for device risk, behavioral risk, mobile phone intelligence, social reputation, email reputation, call center fraud defense, bot and malware detection. And each of these providers typically provides a risk score or a rules-based approach, and a potentially long list of data attributes.

But this approach creates an issue and an opportunity. It isn’t necessarily a bad investment to add new point solution or data providers as long as you are getting value out of these investments. However, that is often the hardest determination to make. Personally, I have worked in large organizations where we invested in the latest and greatest data source, but never fully realized the potential of what we had purchased.

To complicate this further, often there is no cross-channel communication for authentication or identity decisions. Each channel is working in its own silo as is each point solution provider with their scores and rule sets. And from a consumer view, they must prove their identity or authenticate across different lines of business or for different types of transactions. It starts to be obvious why we are losing the battle against fraud with this approach. Many systems, many rules, many scores, no central decision maker or analysis to determine the optimal blend of accurate decision, cost, and performance.

Read the full article here.
account opening account takeover artificial intelligence behavior analytics data deep learning device recognition fraud fraud detection fraud prevention fraudsters Identity machine intelligence Payments risk analysis risk engine signals intelligence supervised machine learning
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