Biometric authentication is one of the critical baseline services for digital payment transactions. Advanced scanning technologies like fingerprint scanning, pattern passwords, facial recognition and eye scanning on smart devices offer a powerful and robust defense against unauthorized access.
It is also essential to ensure operational efficiencies like accuracy, speed and scalability of these systems. This is where artificial intelligence (AI) makes a difference. Advanced biometric mechanisms use AI-based machine learning (ML) algorithms to continuously learn and optimize biometric scanning, performance and accuracy. AI-based logic enhances biometric systems to innovatively improve image quality, real-time motion detection and template security enhancement.
Improving and Securing Transactions
Conventional biometrics methods rely heavily on static objects generated by fingerprints and facial features. Although these methods are impressive to consumers, they are prone to spoofing by highly technically skilled hackers. On the other hand, behavioral biometrics offer continuously varying data objects by repeatedly analyzing a user’s behavioral patterns while interacting with the digital platform.
For example, typing on a keyboard with specific pressure, speed or pattern, recording mouse movements, and swiping patterns are examples of methods used to analyze user behavior. Embedding these behavioral aspects of biometrics with the help of AI-based algorithms adds an extra layer of security and creates a more robust and scalable dynamic authentication system for digital transactions.
It’s critical for these advancements to be applied with inputs of factors like customer consent and regulatory compliance in local markets. Educating customers about these advanced biometric features is also crucial to building customer’s confidence and trust in using the features.
Currently, most transactions are performed using physical plastic cards. It’s become common for large companies such as Apple or Google, in conjunction with payment networks like Visa or Mastercard, to develop digital payment modes via cell phones, tablets and even watches. It’s vital for these new modes of transactions to be as seamless and easy as possible, and as fast as physical card transactions.
If customers discover more secure and user-friendly ways to perform digital payments, they will be more adaptive to these digital payment methods. This approach will encourage companies to continuously improvise solutions that offer faster, easier and more secure methods for digital payments.
It’s essential for organizations that provide services for high-volume transactions for digital payments to implement and continuously enhance solutions that are highly robust, build on a cloud-based distributed architecture, store data on scalable data management systems, and provide advanced load balancing systems to manage high volume.
Behavioral Biometrics and Blocking Fraud
Organizations often rely on behavioral biometrics to identify legitimate users. They use ML to analyze customers’ digital and physical behavioral patterns to distinguish them from non-humans or cybercriminals and prevent fraud and identity theft. For instance, when customers use digital forms of payment for transactions, they might do so with scans of their eyes or fingerprints.
Biometrics uses advanced ML systems to verify the authenticity of such transactions. For example, biometrics consider whether customers typically use facial recognition to perform e-commerce transactions or prefer to use their thumbprints as identification or combination of both for high-value transactions.
Biometrics track the behavioral patterns of customers and implement behavioral aspects into the authentication of transactions, making the process more dynamic. Behavioral patterns that are not static are more difficult for bot software to identify patterns and replicate them.
AI and ML algorithms also enhance the accuracy and speed of biometric authentication systems in digital transactions. Video, audio, and digital images are susceptible to deepfakes and biometric spoofing. Deepfakes use AI to impersonate another individual, and biometric spoofing uses AI tools to “manipulate biometric traits in order to impersonate innocent targets.”
Cybercriminals use AI for these scams, but AI is also instrumental in preventing them from doing so. For instance, AI algorithms can analyze if a video is a deepfake. AI tools can differentiate between values at the sub-pixel level of images or video clips to determine if a video’s speed was manipulated or frames were removed. These systems continuously learn based on patterns and the data they receive, providing continuous authentication and improving the user experience.
Harnessing the Potential of Biometrics
There are advantages of combining static and behavioral biometrics to create more secure and reliable digital authentication systems. These include enhanced security, continuous authentication, multi-factor authentication, and reduced fraud risk.
At the same time, there are challenges associated with implementing biometric authentication in digital wallets. In addition to concerns about regulatory requirements, accuracy, reliability and the difficulty of building self-learning systems, it’s crucial for organizations to safeguard consumer privacy, especially regarding concerns about data breaches and data retention. These issues can be solved by implementing strong security measures and deleting irrelevant and consent-less data promptly and accurately.
Many high-profile companies have successfully addressed such concerns. For example, Amazon One’s palm-based biometric authentication uses the information embedded in users’ palms “to create a unique palm signature that it can read each and every time you use it.” And, as of this year, Seven Bank in Japan allows customers to use facial recognition for transactions at approximately 20,000 ATMs around the country, according to the company, which says the service is the first of its kind in Japan.
Shaping the Future of Secure Digital Transactions
Emerging biometric modalities such as iris recognition and gait analysis will increase the utility of biometrics. Combining the integration of biometric authentication with quantum computing could present a visionary outlook on the future of biometrics in digital payments. There is also potential for synthetic biology to create new modalities or bypass existing authentication systems.
The convergence of AI and biometrics is shaping the future of secure digital transactions. By continuously optimizing biometric systems, leveraging behavioral biometrics, and implementing robust anti-spoofing measures, organizations can create a more secure and user-friendly experience for their customers.
As AI technology advances, more sophisticated and secure biometric authentication solutions will emerge, paving the way for a future of frictionless and trustworthy digital interactions.