Replacing Face-to-Face Interactions With Video-Based Customer Identification

customer identification

Businesses whether finance, e-commerce, cryptocurrency, and many others require accurate and error-free identification of their customers. Due to the rapid increase in fraud, industries are undergoing several consequences ranging from legal proceedings to hefty non-compliance fines. To overcome limitations of manual IDV process, businesses are integrating digital identification solutions. However, insufficient identity checks are still paving the way for cybercriminals to exploit automated onboarding processes. 

Countering identity fraud, document forgeries, and illegitimate onboarding has become a liability for businesses. Moreover, simple passwords, usernames, and OTPs are prone to hacking attempts. This makes video-based customer identification a must-have for industries. These AI-powered IDV tools detect liveness, analyze facial features, and verify customers in real time. This blog covers how artificial intelligence is transforming industries’ onboarding process. 

Video Verification of Identity – a Preferable Substitute for Physical Interactions

From opening a bank account and making digital payments to online shopping, biometric video-chat verification is replacing the need for face-to-face interactions. Industries are developing solutions such as remote onboarding and virtual assistance in this ever-evolving world. While they strive to bring convenience without compromising customers’ security, businesses are undergoing a massive threat of criminal activities. 

The use of synthetic identities for availing medical benefits, funding terrorism through fraudulent transactions, and exploiting sales of retail stores are some of the major risks industries are facing. Due to strict regulatory scrutiny, businesses need to develop mechanisms for ensuring compliance with legal obligations. In making this happen video-based customer identification is helping companies add additional layers of accuracy and security to their operations. 

AI-powered analysis of facial features, liveness detection, and ensuring the real-time presence of customers enables industries to revamp their services. Hence, the video customer identification process provides more convenience, accuracy, and security than manual verification methods.

Use of Artificial Intelligence (AI) in Overhauling Existing Identity Verification Methods  

Video-based customer identification refers to the legitimation and validation of customers’ identity claims in real time. Criminals impersonate customers to hide their true selves and carry out malicious intentions. For this, they either forge documents or use pre-recorded videos. Fraudsters steer clear of the identification checks by using 2D/3D masks and faking backgrounds to bypass liveness detection. This requires industries to deploy more efficient video-based customer identification checks.

Customers can record real-time video by simply using webcams on their devices such as mobile phones, desktops, tablets, or others. For more accuracy, the system asks users to upload pictures of identity documents that serve as supporting evidence. The AI-powered video-chat verification tools then cross-check the facial features within documents. 

Working Mechanisms of AI-Powered Video-Chat Verification 

AI-powered video-based customer identification is automating the industry’s onboarding process. By providing a step-by-step process, one-touch submission, and hassle-free document uploading, digital IDV solutions are driving more convenience for customers. 

In case of complex and time-taking identification steps, clients are more likely to skip the hassle while driving away from the platform. In turn, this will lead to an increase in customer drop-off and reputational loss at large. Therefore, businesses need to integrate better video-based customer identification solutions to retain a huge user base while keeping security and convenience intact. 

Upon their arrival on the digital industries’ platforms, users are asked to fill in a form that requires their Personally Identifiable Information (PII). After submission, the system asks customers to upload supporting documents. In the next step, users provide short real-time clips for video verification of identity. The system then uses AI-powered tools such as OCR to extract information and mathematical algorithms to map facial features. Similarly, liveness detection also adds to more reliable video-based customer identification. 

Validity Checks Provided by AI-Powered IDV Solutions

AI-powered IDV services have their applications within various industries such as finance, e-commerce, healthcare, and others. Due to their advanced tools, instant analysis, and accurate verification, video-based customer identification is taking the lead against manual methods. They further provide the following validity checks:

  • Detection of forgery within documents while analyzing identity proofs such as name, social security numbers, and region of residence.
  • Document analysis tools validate features such as holograms, watermarks, signatures, and ink type
  • Real-time analysis of information’s authenticity.
  • Biometric screening of customers by analyzing facial features within the video.
  • Detecting liveness by scanning backgrounds, movements of objects, and subjects in the surrounding.
  • Extraction and cross-verification of facial features within documents and across global databases.
  • Video-based customer identification authenticates customers by deploying a combination of validation techniques including real-time presence check.

Concluding Remarks 

As per video KYC regulations within the UK, USA, and rest of other countries, customer real-time identification has become a liability for businesses. The increase in fraud risks is further accelerating the need for industries to bring in more efficient IDV solutions. The AI-powered video-based customer identification is leveraging a variety of advanced tools that are driving more accuracy, convenience, and security for digital industries.

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About the Author: John Watson

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