Machine Learning And Behavioral Biometrics: A Match Made In Heaven Posted on : Jan 18 - 2018

A recently released market research report shows the market for machine learning growing at a rapid 44.1% compounded annual growth rate over the next five years, driven largely by the financial services sector, where big data can yield critical and actionable business insights.

In the world of behavioral biometrics, machine learning, deep learning and artificial intelligence are all hand-in-glove. Behavioral biometrics identifies people by how they interact with devices and online applications. As opposed to something that someone has like a device, token or a static attribute like a fingerprint or a name, behavioral biometrics is a dynamic modality that is completely passive and works in the background, making it impossible to copy or steal. Today’s behavioral biometric technologies can capture more than 2,000 parameters from a mobile device, including the way a person holds the phone, scrolls, toggles between fields, the pressure they use when they type and how they respond to different stimuli that are presented in online applications. Behavioral biometrics is used primarily for preventing the use of stolen or synthetic identities in applying for credit online and in preventing account takeovers once a user is logged into a session. (Side note: Most of the fraud today occurs inside authenticated sessions.)

Data scientists have discovered an interesting fact: People work in very unpredictable ways. There is no magical or fixed set of behavioral parameters that are used consistently to tell people apart. There is also no generic model that everyone can be measured against, generating results that are accurate enough both on the false positive side, which is critical for preventing fraud and identity theft -- and on the false negative side, which is important to maintaining an optimal user experience. This approach is known as individual feature selection. It makes behavioral biometrics dramatically more malleable over the long term and across different applications and use cases because it means there is no underlying assumption in advance as to which parameters are good for each person. Instead, the optimal set for that user is selected.

Static Vs. Dynamic Biometrics

Physical biometrics -- like face, finger and iris technologies -- are mostly based on a static approach of measuring points captured in fixed images. As stated above, behavioral biometrics is governed by a dynamic approach that's driven by artificial intelligence. The amalgamation and processing of extremely large data sets are possible due to advances in the fields of data science, which in turn drives machine learning and, more recently, deep learning. View More