As technology becomes an essential part of our lives, combining artificial intelligence (AI) with traditional security techniques is no longer optional, it’s a critical step forward.
AI is driving a significant shift in security practices, creating opportunities to ensure that only authorized individuals can access restricted areas and sensitive information. This integration of AI is redefining the ways we protect our environments.
Yet, the true value of this shift emerges from the seamless collaboration between AI’s capabilities and the unique intuition and decision-making of human security professionals. Here are types of automated ID verification reshaping security.
Liveness
Liveness checks are essential for verifying the authenticity of identity documents. They confirm that the document presented isn’t merely a photo or video shown to the camera but a legitimate physical form.
While face matching is one approach to conducting liveness checks, Microblink focuses on document liveness verification. By analyzing the distinctive features and attributes of the document, Microblink’s technology ensures the identification is a genuine physical document.
Document-Based Verification
Automated identity verification using documents relies on a dual approach that includes both visual and data evaluations Visual checks analyze the physical characteristics of a document, such as its Visual Inspection Zone, by assessing features like watermarks, holograms, and how text is arranged to confirm its validity.
Meanwhile, data checks take a deeper look, examining information embedded in areas like the machine readable zone. A DL scanner and an ID scanner can process data from these areas and cross-reference it against trusted databases to validate an individual’s identity.
Automated identity verification via document checks is quick, secure, and accurate. It saves time by automating the process of manual document examination, making it less prone to human error.
Biometric-Based Verification
Biometric data, such as fingerprints or facial recognition, is being used more frequently in automated identity verification systems to enhance security. These methods are often praised for their efficiency offering swift authentication through a simple fingerprint scan or face recognition.
It’s essential to understand the difference between identification and authentication. While biometrics are highly effective for authentication, verifying that the data aligns with an official database remains a critical step for full identity verification.
Another challenge lies in the comfort level of individuals when it comes to sharing their biometric data. Although trust in companies protecting this sensitive information has grown in recent years, many people remain cautious about providing their facial images or fingerprints for verification purposes.
Selfie Video Verification
By now, most of us are familiar with how document-based video verification works. The automated system scans your ID, takes a photo or video of you, and checks the information against official government records. Based on its findings, it either confirms or denies the match.
This process is far from simple. The software has to search through a worldwide network of verification services, which explains why it can take some time. It’s also not the most user-friendly method. Customers need to locate the right documents, wait for the verification to complete, and deal with any potential technical hiccups along the way.
There’s also the risk of stolen IDs and sophisticated fraud attempts. Deepfakes can trick selfie verification, and paper documents can be easily forged or even purchased through editing services.
Conclusion
Automated identity document verification is now essential for businesses. It helps cut costs, saves time, and protects data, offering reassurance to both companies and their customers. By automating the verification process, it simplifies the experience for customers, eliminating long waits on the phone or the need to complete tedious forms.