In today's increasingly digital interactions, robust Identity Verification (IDV) is crucial for security and trust. Artificial Intelligence (AI) is transforming IDV, enhancing accuracy and fraud detection. This paper introduces ``Zero to One,'' a holistic conceptual framework for developing AI-powered IDV products. This paper outlines the foundational problem and research objectives that necessitate a new framework for IDV in the age of AI. It details the evolution of identity verification and the current regulatory landscape to contextualize the need for a robust conceptual model. The core of the paper is the presentation of the ``Zero to One'' framework itself, dissecting its four essential components: Document Verification, Biometric Verification, Risk Assessment, and Orchestration. The paper concludes by discussing the implications of this conceptual model and suggesting future research directions focused on the framework's further development and application. The framework addresses security, privacy, UX, and regulatory compliance, offering a structured approach to building effective IDV solutions. Successful IDV platforms require a balanced conceptual understanding of verification methods, risk management, and operational scalability, with AI as a key enabler. This paper presents the ``Zero to One'' framework as a refined conceptual model, detailing verification layers, and AI's transformative role in shaping next-generation IDV products.

Building on related concepts, like, decentralized identifiers (DIDs), proof of personhood, anonymous credentials, personhood credentials (PHCs) emerged as an alternative approach, enabling individuals to verify to digital service providers that they are a person without disclosing additional information. However, new technologies might introduce some friction due to users misunderstandings and mismatched expectations. Despite their growing importance, limited research has been done on users perceptions and preferences regarding PHCs. To address this gap, we conducted competitive analysis, and semi-structured online user interviews with 23 participants from US and EU to provide concrete design recommendations for PHCs that incorporate user needs, adoption rules, and preferences. Our study -- (a)surfaces how people reason about unknown privacy and security guarantees of PHCs compared to current verification methods -- (b) presents the impact of several factors on how people would like to onboard and manage PHCs, including, trusted issuers (e.g. gov), ground truth data to issue PHC (e.g biometrics, physical id), and issuance system (e.g. centralized vs decentralized). In a think-aloud conceptual design session, participants recommended -- conceptualized design, such as periodic biometrics verification, time-bound credentials, visually interactive human-check, and supervision of government for issuance system. We propose actionable designs reflecting users preferences.

Last year, a foundation called Tools for Humanity went on tour to show off its eye-scanning Orb. The metallic globe--an actual, physical orb--was one part of a process where citizens would someday use their biometric information to verify their humanity. The project, called Worldcoin, might have been written off as another techno-utopian project bound to fail had it not had one name attached to it: Sam Altman, the cofounder and CEO of OpenAI, one of the most dramatic tech companies of the modern era. An inkling of Worldcoin began in 2019 when Altman began exploring identity verification that could be used in universal basic income schemes. He teamed up with technologist Alex Blania to turn the idea into a reality.

It's clear that AI is transforming the way banks operate, but what's not always clear is how they can successfully implement and deploy AI projects. Experts recommend a shared, centralized infrastructure for AI--a full-stack solution that includes both hardware and software. This approach, known as AI as a platform, is ideal for three reasons. One, it consolidates expertise, productivity, and scale. And three, it drives down total cost of ownership with an efficient utilization of compute and storage resources.

Socure is redefining identity verification with groundbreaking technology, supporting myriad organizations with the most accurate authentication tools in the industry. What we build helps businesses scale faster, stop fraud, and ultimately allows millions of people who are excluded from the digital economy (due to outdated fraud detection models) to take part in it like everyone else. Our culture is about innovation, winning, and customer obsession. We are full of top performers that prioritize excellence and results, as well as support for one another, on the path to achieving our mission: to verify 100% of good identities in real time and completely eliminate identity fraud for every applicant on the internet. To learn more about working at Socure visit our career page here: https://www.socure.com/company/careers

Onfido has added four new products to its biometrics and artificial intelligence (AI)-powered identity verification and authentication service known as the'Real Identity Platform,' promising superior results and performance. The update is comprised of the Onfido Verification Suite, Onfido Studio, Onfido Smart Capture, and Onfido Atlas AI. The Verification Suite is a curated library of trusted data sources and identity verification services to offer a user experience tailored around specific fraud and regulatory use cases, compliance requirements, global needs, risk appetite, and business objectives. It is integrated into Onfido's document and biometric identity verification solution and carries trusted data verification sources like a U.S. social security number and sanctions watchlist, and fraud detection verification through geolocation and phone verification among other options. Studio is described as an orchestration software built around a no-code platform and analytics tools for businesses.

If we consider that you might be suitable for other roles in the future, we will keep your details so we can contact you about these other roles. If you do not want us to keep your details for this purpose, please e-mail HR or let us know at any stage of the recruitment process. For more information please read our Applicant Privacy Notice.

Deepfake images and videos pose a significant threat to biometric systems used for remote identity verification, and existing liveness technologies can detect them, making an attack vector for non-deepfakes a vulnerability businesses need to be aware of. 'Why Deepfakes aren't the Real Challenge for Remote Biometrics' was presented by Ann-Kathrin Freiberg of BioID in the latest lunch talk presented by the European Association for Biometrics (EAB). More than 250 attendees from more than 40 countries around the world pre-registered for the presentation, many of whom were highly engaged in discussion throughout. The origin of the term based on the use of deep learning to manipulate or fake an image, video or audio file was reviewed, and Freiberg shared several examples of deepfakes, including a morph fake created by a BioID employee from a free app and a single image found on the internet. Some basic tips for spotting deepfake videos were shared, such as observing the transition between different areas of the face and head, and frequency or lack of blinking.

Biometric spoofing attacks are more easily spotted by artificial intelligence-based computer systems than by people, according to new research published by ID R&D. The new report, 'Human or Machine: AI Proves Best at Spotting Biometric Attacks,' compares the relative effectiveness of humans and computers detecting presentation attacks, in terms of speed and accuracy. Liveness detection was tested against images including spoof attempts with printed photos, videos, digital images, and 2D or 3D masks, according to the announcement. The company's IDLive Face accepted 0 percent of face biometric spoofs across all types of attacks and 175,000 images. People fared far worse, failing to spot spoofs in every category, including 30 percent of photo prints, one of the easiest spoof attacks for fraudsters to carry out.

Humans have far greater difficulty identifying images of biometric spoofing attacks compared to computers performing the same task, according to research released by ID R&D. The research report finds that computers are more adept than people at accurately and quickly determining whether a photo is of an actual, live person versus a presentation attack. Fraudsters attempt to imitate real customers during processes such as creating a new bank account or logging into an existing account. Liveness detection instantly validates whether a photo, taken in real time, is of a live person. The study tested humans and machines by presenting them with the most common spoofing techniques: printed photos, videos, digital images, and 2D or 3D masks.