Behavioral biometrics has become an essential method of authenticating online users, yet its use is often mistaken with the more commonly used biometric ID authentication, often found in mobile devices. Physical attributes such as a fingerprint, face, the iris of the eye, palm prints and even a voice can be scanned to authenticate the ID of device users. Although effective, they are tied to real-world unique identities, which can deter their use among privacy advocates, worried about how such data is stored and used by companies. Behavioral biometrics works slightly differently, authenticating users solely via their behaviors and not their identity – and it’s all done within data-compliance measures to allay privacy fears. These capabilities are currently expanding, as is the size of the market. In 2017, the global behavioral biometrics market was worth $675 million – a stark contrast to its estimated worth of $2.5 billion in 2023.
The simplest definition of behavioral biometrics is the field of study to measure uniquely identifiable and measurable patterns of human behavior. And despite being a hot topic in fintech and cybersecurity circles, it is certainly nothing new. The first modern applications of behavioral biometrics stretch back to the 1860s when it was used to recognise senders of telegrams. The same principle was applied in World War II to authenticate Morse code messages – it was crucial to distinguish them from potentially misleading recipients. The key to authentication was in the small details – how the senders would behave when typing etc.
The concepts for authentication remain the same today – to determine genuine users from threat actors. The taps on a Morse code device have evolved into the behavioral analysis of the taps of a keyboard, the taps and swipes on touch screens and even the general movements of mobile devices. For example, a stationary mobile device, standing vertically and fully charged at 100% may indicate it being hooked up to a larger bot factory used to create multiple fake accounts for later fraudulent activities. The means by which user authentication takes place have become sophisticated and continue to evolve to improve accuracy.
People are becoming more comfortable with sharing biometric data, with the end goal always to ensure the security of anything from personal mobile devices right up to fraud-free online payments. Many enjoy having peace of mind that the services they use are secure – any additional security measures are better than weak or limited security. But when it comes to the use of physical biometric scanning, there will always be potential ethical dilemmas and security concerns to consider regarding privacy – can the company storing your data be trusted? Does it have a positive reputation when it comes to cybersecurity? These are valid concerns, especially when stored data can help identify you as ‘John Smith’ who can then potentially be surveilled.
The use of behavioral biometrics is particularly effective in anti-fraud systems to protect your online accounts, with real-world applications in eCommerce to financial services. Although even with the use of behavioral biometrics, there exist different levels of efficiency. Anti-fraud setups using only ineffective rules-based fraud models are trained to look for a particular set of suspicious user behaviors, almost like working from a checklist. This leads only to a narrow line of focus, which could in fact lead to false positives, resulting in blocking genuine users from using a service, therefore requiring the need for manual reviews. Worse still, potential threat actors may attempt (sometimes successfully) to imitate the behaviors associated with regular online users and get past this anti-fraud setup.
This is where AI-powered solutions prove their worth. They can deal with automatic risk analysis, performed in real-time, completely unnoticed by service users, resulting in a truly frictionless authentication experience. Legitimate users go through continuous passive authentication without experiencing any intrusive requirements to type in passwords or codes, all while thousands of data attributes are analyzed to give them a green light while only blocking threat actors. Fraud can be pre-empted and prevented before it happens.
A fraudster that has committed an account takeover fraud (ATO) or is using stolen or synthetic IDs to create multiple fake accounts will certainly behave differently from the original account holder (or fake persona). They will deploy a number of masking techniques, from changing network, hardware and software settings, all of which can be effectively detected with AI-powered solutions. The telltale signs are always there – hence, the more data there is to analyze in real-time, the more effective anti-fraud can be in stopping threat actors.
Behavioral biometric authentication is by no means the silver bullet to preventing security breaches, but using it as part of a holistic approach to general security measures can vastly improve the security of every user’s device of choice, all the way through to their chosen online accounts and services.
About Nethone
Nethone offers a proprietary machine-learning-based product designed to protect the entire user journey, from onboarding to post-payment, at the same time helping merchants to holistically understand their end-users (good and bad). With the award-winning Know Your User (KYU) profiling technology and AI-powered tools, Nethone blocks all risky users without friction to the good ones by exhaustively screening every single one. Nethone has been recognised as one of Europe’s fastest-growing companies in 2023 by The Financial Times newspaper and data provider Statista. We are also the highest-ranked Polish company in the Fintech category.
