Yoti blog
Stories and insights from the world of digital identity
Why facial age estimation, the most accurate age checking tool, shouldn’t be left on the sidelines
Many of us have been there: standing at a self-checkout, scanning our shopping, only to hit a roadblock when the till flags an age-restricted item like a bottle of wine or a pack of beer. With age verification accounting for between 40 – 50% of interventions at self-checkouts, it significantly disrupts and slows down the checkout experience. We wait for a retail worker to approve the sale. The retail worker does a visual estimation of our age – they look at our face and guess whether we’re old enough to buy the item. Most retailers follow the Challenge 25
Why testing data is as important as training data for machine learning models
When developing machine learning systems for facial age estimation, the conversation often centres on the training data: how much you have, how diverse it is, how inclusive it is, and how well it represents your end users. Not to mention, where the data comes from. Intuitively, that focus makes sense. More data presumably leads to better models. But test data is just as important, and in some ways, even more critical for ensuring models perform effectively. Training data: more isn’t always better Common sense would suggest that for a machine learning model “the more data, the better.” And that’s
Texas App Store Accountability Act: what it means for age assurance worldwide
The State of Texas has passed a landmark law – the App Store Accountability Act – that places legal responsibility for age checking squarely on app store operators. Utah was the first state to enact this type of legislation, now followed by Texas. This new regulatory shift has far-reaching implications for digital safety, privacy and innovation around the world. As an age assurance provider, we believe it’s critical to explain the significance of this development, highlight the practical challenges it raises, and offer a path forward that protects both users and platforms. One of the main weaknesses of this
Thoughts from our CEO
In this blog series, our CEO Robin Tombs will be sharing his experience, whilst focusing on major themes, news and issues in the world of identity verification and age assurance. This month, Robin chats about the UK’s supermarket trials, the growing momentum behind digital ID and developments in online safety regulations. Facial age estimation and Digital ID supermarket trials Many Yoti followers will know we developed effective facial age estimation at the end of 2018 – over 6 years ago. After several UK supermarkets pleaded for the Home Office to trial the technology at self-checkouts, trials were completed
Protecting your business and customers from account takeover
In today’s digital world, we have dozens of online accounts. These range from online banking to social media, dating apps to gaming platforms. Though convenient, this opens the door to the rapidly growing threat of account takeover fraud. Account takeover fraud is surging, with global losses expected to hit $17 billion by the end of 2025. The number of account takeover attacks is rising sharply too, increasing by 24% year-over-year in 2024. This blog walks you through what account takeover is, how it happens and what you can do to prevent it. What is account takeover? At its
Effective ways to improve your AML compliance
Managing financial crime presents a complex challenge for financial institutions. Due to its covert nature, the full scope of money laundering is difficult to truly know. The United Nations Office on Drugs and Crime (UNODC) estimates that between 2-5% of global GDP (up to $2 trillion in US dollars) is laundered every year. As financial crime becomes more sophisticated and regulations grow tighter, businesses must prioritise robust anti money laundering (AML) measures. Industries like banking, fintech and financial services need strong AML processes to protect themselves from fraud, penalties and legal risks. We explore how your business can strengthen
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