Identity verification techniques are continually being improved as more businesses begin to favor customer experience as a way to increase market share. It means that while developing an effective business model, security must be considered alongside convenience. And moving your business into the digital sphere implies implementing innovative customer identity authentication techniques that include a liveness detection to aid in fraud prevention.
Cybersecurity threats exist due to vulnerabilities and risks involved in doing business with clients online and remotely. To mitigate the risks, organizations integrate advanced technologies into their frameworks for the onboarding of customers and even their staff. The right strategy, which includes Know Your Customer (KYC) processes and platform audit ensures that all loopholes that can be exploited by criminals are blocked to maintain system integrity and boost users’ trust. It’s also important to ensure that your security strategies comply with anti-money laundering (AML), which not only keeps your site and customer’s assets safe, but further sets you apart from competitors.
As you secure your business platform and enhance the user’s digital experience, onboarding and transaction processes should be streamlined in a way that makes authentication of users frictionless. With liveness detection incorporated into identity verification solutions, a balance between convenience and security is achieved. On the other hand, users will feel safe knowing that there are fraud prevention measures in place to safeguard their data. That said, let’s help you understand the process of liveness checks.
Demystifying Liveness Detection
Biometric identification has revolutionized financial services, ensuring that businesses can stay one step ahead in the fight against identity theft. One way they can do that is ensuring that customers are who they say they are, that activities in customers’ accounts are authorized by them, and that their identities have not been stolen. This can be done via liveness checks. Here, facial features of intrinsic value are observed and analyzed to determine if a user is real or not, and there is no identity theft. As documents, videos and selfies can be altered, liveness detection validates a customer’s authenticity.
Facial recognition alone is no longer sufficient as deep fake technology now makes it possible to bypass many verification processes. Fraudsters only need to get images or video recordings of victims to hack accounts or complete illegal transactions using different identities. Verifying the presence of an actual user will ensure the detection of identity spoofing in real-time. In live detection, criminal deterrence and fraud prevention doesn’t always directly translate to improved customer conversion rate, but it sure does help. Clients with genuine intentions could be willing to follow precise instructions and complete specific tasks to prove their identity. How far customers are willing to go on with their onboarding process without abandonment will depend on the form and ease of the liveness check implemented by the authentication platform.
Face liveness detection is more common than the fingerprint alternative because more internet-enabled devices are equipped with cameras than finger detection sensors. The camera makes it possible for detection algorithms to use artificial intelligence (AI) and motion analysis in spotting spoofing attacks. Changes in texture, facial expressions, and background can help in distinguishing between a real user and a spoof. There is the belief that multiple layers of security is effective in fraud prevention, hence active and passive liveness detection can run concurrently on a platform.
Comparing Active and Passive Liveness Detection
Active liveness detection takes the challenge and response approach while passive checks use involuntary movements. Whether the spoofing attacks involve the use of 3D inanimate objects or digital models in any dimension, they will be detected.
Active liveness detection could mean users being told to take 2 selfies with different facial expressions per selfie. The alternate photo could be one with the user smiling or holding the identification document uploaded during the verification process. Some require making a video where the client has to say specified alphabets or numbers out loud. The camera resolution does not affect detection accuracy. Video replays with randomized movements that do not match the instructions given in the verification session are flagged.
A passive liveness check on the other hand, can occur routinely without tipping off the hacker since it uses reflex signals to detect a user’s presence. Blinking and movement of the eyes and head can imply that an active user is present. Pupil dilation and light reflection patterns on the face when compared to the background can help differentiate between real-time client identification and a false representation. This technique ensures that fraudsters do not know when their activities get detected.
Fraud prevention and onboarding with Liveness Detection
Artificial intelligence and machine learning have made it possible to design realistic 3-dimensional digital images and videos that hackers use to fool identification systems. Telling the difference would require the use of technical tools, and that is where live detection comes in. Slight inconsistencies with human movement responses, excessive glare on reflective surfaces, and absence of delicate facial features are spotted instantly, and this can help prevent fraud on your business platform.
As online businesses continue to prioritize convenience, having only passive detection systems in payment platforms, for instance, may not be convincing for customers. A combination of standard techniques like challenge and reaction, use of reflex signals, and image depth analysis in the remote onboarding process will help build trust. There are several benefits of additional layers of security that impact customer confidence in a system.
1. Adds Legitimacy to Documents
During online client onboarding, uploading identification documents alone is not an effective cybersecurity procedure. Liveness detection helps authenticate them with ease in real-time. Even for returning customers, the liveness check is an effective fraud prevention measure that would identify spoofing attacks as it legitimizes whatever activity going on their account. When the system has verified that the user is the same person in the uploaded document during the onboarding process, the information is stored in a secure database. Continuous monitoring with passive liveness detection will ensure that every activity on that user account is monitored seamlessly. Discrepancies in information like the house address can trigger re-authentication with active liveness detection in order to clear document authenticity questions.
2. Reduces Wait Time for Approvals
Liveness detection uses advanced technology to analyze documents and user data collected during an ID verification process. The entire process is fast and reliable since it uses computer algorithms to detect differences in biometric data that may not be visible to the human eye. Imagine a process where someone has to verify and analyze documents from the back-end of an online system. Web traffic can cause verification to take many days and document inconsistencies might be missed due to human error. Customers awaiting approval may decide to switch to a competitor with a system that offers a shorter approval wait time. With liveness detection, the algorithm easily and immediately compares submitted photo with live facial expressions, then accepts or flags the customer
3. Deters Users from Committing Crime
Criminals will always favor systems with vulnerabilities and fewer security layers that can be bypassed without much trouble. It has led to the use of authentication frameworks in almost every digital system, including Automated Teller Machines (ATMs). The cameras installed in these machines can capture biometric data which is analyzed to track fraudulent users. Cryptocurrency ATMs are becoming a trend as the number of merchants accepting digital assets continues to soar. An FBI internet crime report revealed that scams using crypto machines increased in 2021 because AML regulations on that channel have historically been lax. A financial institution operating high-risk ATMs will experience a drop in customers without a proper security and anti-fraud solution.
However, some machines now have liveness detection frameworks that detect users and capture their faces even when wearing masks, hats, and glasses. This goes a long way in ensuring that spoof attacks remain difficult.
4. Makes Impersonation or Data Reuse Difficult
Many people have often wondered if growing a beard would mean denied access to their online account or unusable ID cards. Identity verification systems now use AI to ensure that slight changes in appearance do not prevent the identification process. Liveness detection technologies are implemented to ensure that customers can look how they want and still access their accounts, while impersonators using 3D prints cannot get in. Pupil dilation, motion, and depth analysis ensure that video or synthetic iris attacks are detected instantly. Recollecting and reassessing user biometrics by the live detection frameworks every time access to the platform is requested will make spoofing difficult.
Conclusion
Liveness detection may include automated processes or require user response to simple challenges with hints for smoother onboarding. Data collected during the authentication process is analyzed to establish the presence of an actual user while ensuring compliance with AML regulations. Passbase with iBeta/NIST (National Institute of Standards and Technology) Level 1 & 2 PAD (personal Account Dealing) compliance incorporates advanced liveness detection technology in verification processes. Integrating an intuitive and secure solution into your online platform will guarantee a frictionless digital onboarding experience.
