Machine Learning And Behavioral Biometrics: A Match Made In Heaven

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.