Yoti blog
Stories and insights from the world of digital identity
Digital identity verification for DBS checks
The Disclosure and Barring Service (DBS) has updated its guidance on how to check someone’s identity for a criminal record check. Previously, the process was only possible by seeing physical documents. During the pandemic, employers enjoyed relaxed rules which allowed them to do this via video call. However, the government has now updated their guidance to allow for digital ID verification technology. This means candidates can prove their identity online, which is an absolute game changer for employers grappling with a remote-first world. But how does the process work and should you use it? Here’s our guide to digital ID
Myth busting Digital ID
There’s no denying we’ve moved most of our lives online, and the majority of the time, our phones are the instant portal we use to get us there. They’ve become our personal shoppers, our social life planners and documenters, our music, films and books all in one. With everything already in the palm of our digital hand, it makes sense that the way we prove our age and identity should be too. A Digital ID is your ID on your phone, replacing physical documents with a smarter, instant way of proving who you are. We think it’s the future but
Muzz introduces selfie and identity verification from Yoti for safer Halal dating
Leading Muslim dating and marriage app Muzz (formerly muzmatch) has partnered with digital identity company Yoti to give singles increased confidence and transparency over who they are connecting with online. Muzz has integrated Yoti’s photo verification technology to confirm every dating profile belongs to a real person. On account creation, users are required to take a selfie, which will be analysed by Yoti’s proprietary passive liveness technology, MyFace. The technology checks that the selfie is captured from a real person by detecting different presentation attacks, such as printed and digital photos, videos and masks. If the technology detects the image
On Facial Age Estimation, improvements and updates
We continue to invest in improving the accuracy of our world-leading facial age estimation and we will soon be releasing a new ‘Jan 2023’ model with improved accuracy across all skin tones and gender for those aged 6-70. At Yoti we constantly strive to improve all our services given our commitment to tech for good, and given the growing market importance of these services and increasing regulatory engagement. For example, coming soon we’ll be introducing multiple, concurrent age estimation models, which will help improve accuracy even further, whilst still completing a check within seconds. We will also continue to update
Konfir partners with Yoti for certified income and employment attributes
We are delighted to be working with Konfir, a B2B SaaS platform that lets employers instantly access employment or income records for any UK-based worker, with their consent. Konfir is at the forefront of the shift in determining how employment and income data is captured, verified, and shared, and has become the first certified Attribute Service Provider (ASP) under the UK Digital Identity and Attributes Trust Framework (DIATF). As the first government certified provider of employment and income data, globally, Konfir provides employment and income information, including work history, previous job titles and dates of employment, to companies or Identity
Yoti digital age verification trialled at supermarkets
Last year, UK supermarkets – including Asda, Morrisons and The Co-op – trialled our digital age verification at self-checkouts in a scheme run by the Home Office. During the trial, shoppers in participating stores could try two new ways to prove their age: Facial age estimation technology: shoppers purchasing alcohol looked at a camera on the self checkout and age estimation technology estimated their age. A privacy-preserving solution, it didn’t require any personal details or ID documents, and all images were instantly deleted once someone received their estimated age. If the system detected they looked younger than the set
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