Digital transformation has taken its hold in payments, banking, commerce, and beyond. But both the rapid transformation of processes and the rate of consumer adoption present significant challenges for businesses. One of the biggest questions of our day is how do you verify customer identity in a way that prevents cybercrime without disrupting the user journey?

Capitalizing on Behavioral Data

Traditional online identity verification methods and fraud prevention technologies don’t sufficiently address the need for secure and seamless online user experiences. Instead, they generate unnecessary false alarms that in turn drive up costs related to password management and transaction verification.

Today, the cost of false declines is actually 13 times the cost of actual fraud – this comes from the loss from a particular transaction as well as loss of brand loyalty. Traditional fraud detection systems are also known to result in very high false alarm rates, driving up call center and other operational costs related to managing fraud.

Every day, terabytes of data are generated from many sources that can improve the verification process. But the sheer volume of data makes it difficult for business leaders and analysts to determine what information is relevant and useful amid the noise.

Behavior is a whole new class of data now being collected. Behavioral biometrics, a technology based on machine learning and artificial intelligence, runs passively in the background of an online application, generating 10 GB of behavioral data per minute.

The data gathered through behavioral biometrics is an untapped goldmine that, with the right domain expertise, can be harnessed to reveal powerful insights to drive many online identity verification-related decisioning processes. At BioCatch, we package these insights into actionable indicators for business leaders and analysts so they can maximize fraud detection rates, reduce false alarms, and optimize the user experience.

An Added Layer of Visibility

Behavioral insights provide an added layer of visibility beyond the risk score for greater understanding of the activity inside the digital channel. These insights are valuable in identifying trends, understanding top contributors to fraud alerts, and uncovering opportunities to better service clients.

For example, when a known user started displaying abnormal behavior, such as hesitating before submitting, making and correcting more errors than normal, and multitasking, the BioCatch system picked up on the anomalies and sent a real-time alert to analysts. The company was then able to stop a social engineering scam, a phone-based form of Authorized Push Payment fraud, in real-time. Each behavioral element on its own was meaningless, but putting them together pointed to a real threat.

Using advanced data science and artificial intelligence to connect the dots allows uncorrelated data and signals to make more sense and to make online identity verification actionable.

Correlating Disparate Data Sets

The typical fraud stack contains multiple layers. In an enterprise, a typical six-layer tech stack can involve 336 products, provided by 57 vendors. Each tool is aimed at stopping intrusions, account takeover, and fraud at the endpoint and within applications. Managing such a complex installation is daunting, especially as attackers constantly change their methods and customers look for convenience and minimal disruptions. Analyzing user behavior and translating behavioral insights into action helps to prioritize alerts, identify threats in real-time, and enable the experience that today’s online users demand.

Applying behavioral insights to online identity verification for credit card applications, insurance policy applications, and more can have a real impact on the bottom line. By analyzing a user’s behavioral biometric profile, parameters like location, device, and past history are mere reference points, but in and of themselves do not drive the decisioning. On the other hand, how a user enters information into an online application and the context in which the activity occurs results in insights that can be used to reduce false alarms, facilitate service delivery, and ensure proper online identity verification that protects consumers and their financial institutions.

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