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article imageQ&A: How you hold your phone can help protect it from hackers Special

By Tim Sandle     Jan 31, 2020 in Technology
How someone holds their phone is becoming a criteria for securing their identity from hackers. If you usually hold it with your right hand, and a left-handed hacker is trying to gain access to your phone, they can be stopped because of that difference.
According to the CEO of Deep Labs, Scott Edington, such activities are becoming part of a vastly improved behavioral-based security approach that is taking ID verification to new levels. Behavior-based security is starting to look much more at how users hold their phones, how hard they press the buttons, and how quickly they might type.
Digital Journal spoke with Scott Edington about how artificial intelligence and machine learning can help to create user ‘personas’, that red flag if your phone is being used even slightly differently than how you use it.
Digital Journal: What are some of the biggest security concerns affecting the financial sector?
Scott Edington: Well known data breaches, the increasing sophistication of cyber threats, and the near ubiquity of mobile devices have created a challenging dynamic for those organizations requiring the traditionally conflicting deliverables of a high degree of certainty for authentication and a reduction in consumer friction in order to maximize payment approvals.
Current fraud screening capabilities simply were not built to leverage contextual and behavioral information and as a result have adopted a ‘hair trigger’ on declines, introducing material transactional friction without a commensurate improvement in fraud detection.
Revenue loss due to actual fraud was $28 billion in 2018. False declines, transactions that should have been approved but were not, were $330 billion. With the proliferation of digital wallets, in-app purchases, and the ongoing migration of consumer purchases through siloed merchant networks, these staggering false decline rates continue to grow exponentially. The ultimate goal for all parties in the Payments ecosystem is to increase the approval rates of legitimate transactions, thereby increasing revenue.
DJ: How did you come to form Deep Labs?
Edington: I’ve been fortunate in my career to be afforded the unique opportunity to conceptualize, create and enable some of the most advanced capabilities in the Defense, Intelligence, and Payments spaces. This unique vantage point has provided a view into the latest advanced persistent threats and ultimately how to combat them. Deep Labs was founded with the core belief that extreme-scale machine intelligence coupled with the myriad of ‘sensor’ data now available has led to a confluence enabling true context-aware, artificial intelligence risk systems capable of combating the next generation of advanced persistent threats to be created.
DJ: What does a behavioral-based security approach involve?
Edington:At Deep Labs we believe the notion of ‘context’ is extremely important. An actor’s behavior can change based on a myriad of factors – location, social construct, weather, seasonality, etc. At a glance, a human being is capable of discerning context and then altering their decisions based on that new-found knowledge.
A machine intelligence backed behavioral-based security approach enables the inference capabilities of a human being with a machine’s ability to discern signals from noise at extreme scale. These signals can range from how a user interacts with their mobile device, to what and where financial transactions are made, all the way to how a user holds their phone or walks throughout their day. This enables a persona-based intelligence® approach that is capable of understanding the context of a consumer in real-time, eliminating authentication and approvals friction, while reducing fraudulent behavior through its Persona-Based Intelligence® approach, enables its customers to fully understand the context of the consumer in real-time, eliminating authentication and approvals friction, while reducing fraudulent behavior.
DJ: What type of technology is required to support this approach?
Edington:From a broad perspective, extreme-scale compute platform coupled with advanced real-time learning and decisioning capabilities. Essentially, to successfully execute the approach, the technology needs to be capable of processing tens-of-millions of data points to arrive at signals that are immediately actionable in less than 5 milliseconds. This machine intelligence platform needs to be signal ‘agnostic’ and capable of processing both static and dynamic signals to create a multi-dimensional and context-rich understanding of an actor’s behavior.
DJ: Which types of companies do you work with?
Edington:Deep Labs works with the largest payment networks and financial institutions in the world. We have also deployed our solutions to the United States government.
DJ: What will be your next phase of development?
Edington:From a technology perspective we will continue to work on next generation capabilities that will enable our customers to provide a frictionless and hyper-personalized Payments experience. Organizationally, we are actively recruiting the best and the brightest to come join our team!
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