Is biometric authentication still effective?

By Bence Jendruszak, COO of SEON   

For over a decade, biometrics that leverage unique biological traits to verify identities have been widely adopted for user authentication. However, the rapid advancement of artificial intelligence (AI) has given fraudsters new tools to circumvent biometric methods such as fingerprinting, facial recognition and voice identification. As technology evolves, it is crucial to assess and address potential vulnerabilities with biometric authentication to ensure ongoing security and continued effectiveness of anti-fraud measures.

The ‘cat and mouse’ game continues

As biometric authentication has grown more popular, it has increasingly become a target for fraudsters looking for ways to break its defenses. As early as 2012, Accenture noted that fraudsters were  exploiting predictable patterns in biometric security measures to compromise systems. These methods have evolved with technology, and this year alone, biometric-based fraud attempts have surged by nearly 40 percent. This includes sophisticated tactics that leverage AI to bypass authentication systems.

With the rapid advancement and accessibility of technologies, the efficacy and security of biometric authentication methods are under threat. Fraudsters are using spoofing techniques to replicate or falsify biometric data, such as creating synthetic fingerprints or 3D facial models, to fool sensors, mimic legitimate biometric traits and gain unauthorized access to secured services. .

Deeper into spoofing and deepfakes

In the past year, there has been a noticeable rise in “selfie spoofing” scams, where fraudsters use an individual’s selfie to authenticate a stolen identity and subsequently open fraudulent accounts. This fraud method accounted for 20 percent of all identification document fraud cases last year.

Advanced spoofing techniques, including those that replicate an individual’s voice, are becoming increasingly prevalentm, highlighted by recent headlines such as a finance worker in Hong Kong who was tricked into transferring $25 million to a fraudster that impersonated his chief financial officer during a video call earlier this year.

These sophisticated models utilize AI algorithms to create highly realistic synthetic media, including videos and audio recordings that convincingly depict individuals saying or doing things they never did. These methods have various uses for fraudsters, but one of the most concerning is their ability to trick biometric solutions with falsified information.

A response is needed

Many device intelligence solutions currently built on standard device fingerprinting techniques to identify and block fraudulent devices using device characteristics, configuration details and network information. However, fraudsters can mask these characteristics to evade detection, rendering these methods less effective. To counter this, many modern fraud prevention companies have started integrating behavioral biometrics into their solutions.

Unlike traditional biometric authentication, which relies on static physical attributes, behavioral biometrics verify user identity based on unique interaction patterns, such as typing rhythm, mouse movements and touchscreen interactions. This shift is essential because behavioral biometrics offer a more dynamic and adaptive layer of security, making it significantly harder for fraudsters to replicate or mask. Companies can achieve a more robust and reliable means of verifying identity by focusing on how users interact with their devices, thereby enhancing fraud prevention strategies.

Going further than before

While action can be taken to mitigate the worst effects of these emerging methods, the rise of these advanced attacks should prompt companies to reassess the overall effectiveness of their broader fraud prevention strategies. Right now, many businesses suffer from an overreliance on point solutions, which results in a disjointed approach that fails to provide a holistic view of fraudulent activities. Instead of implementing a comprehensive strategy, businesses often resort to layered tech stacks to address singular aspects fraud detection – frequently relying on outdated technologies that make management a nightmare.

With data scattered across different systems, it’s challenging to correlate information, connect the dots and identify overarching patterns of bad behavior. A decentralized approach causes businesses to overlook crucial fraud indicators and struggle to respond effectively to emerging threats due to the lack of visibility and coordination among disparate fraud prevention tools. While some platforms offer comprehensive fraud prevention capabilities, they may heavily rely on third-party data sources, including legacy datasets, to inform their decision-making processes.

In addition, while certain third-party data providers offer valuable insights, they can be accompanied by limitations, such as latency and incomplete coverage, especially when dealing with quickly evolving fraud tactics. Finally, some companies partner with orchestrators – entities that rely on third-party solutions and integrate them into their platforms. This approach offers convenience but comes with drawbacks, as multiple solutions extend timelines, lead to delays in implementation, and create a disjointed experience. Ultimately, none of this is suited to tackling emerging challenges.

Finding the right approach

The emergence of advanced technologies like AI and machine learning have weakened biometric authentication’s effectiveness by making fraud tactics like spoofing and deepfakes more accessible and applicable. By enhancing existing measures with innovations that include behavioral biometrics, companies can develop multi-layered anti-fraud measures that are capable of addressing the complex and emerging fraud times becoming prevalent in today’s landscape.

About the author

Bence Jendruszak is COO of SEON. SEON aims to set a new standard of security and trust with its AI-powered anti-fraud and AML solution.

Article Topics

behavioral biometrics  |  biometric authentication  |  biometrics  |  deepfake detection  |  fraud prevention  |  SEON  |  spoof detection