In today’s digital age, Know Your Customer (KYC) processes have become a crucial aspect of various industries, including banking, finance, and e-commerce. KYC procedures are implemented to verify the identity of individuals and ensure compliance with regulatory requirements. One key element of KYC is age estimation, which is used to confirm the age of an individual for age-restricted products or services. In this blog post, we will explore the process and challenges of age estimation in KYC.
Understanding Age Estimation
Age estimation, also commonly known as age verification, refers to the process of determining an individual’s age based on various characteristics and patterns. It is a field that combines elements of computer vision, machine learning, and statistical analysis. Traditional age estimation methods relied on human judgement, but with advanced algorithms and computer power, automated age estimation has emerged, which offers a much higher level of accuracy.
Age confirmation is essential in ensuring children do not gain access to services and sites that are not age appropriate.
How does AI-based Age Estimation Work?
Age estimation algorithms primarily leverage facial features to estimate an individual’s age. Facial appearance changes over time due to biological factors like bone structure alterations, skin texture changes, dental records, and the formation of wrinkles. Moreover, external factors such as lifestyle, environment, and stress contribute to the ageing process. It is also important to note that these factors look different depending on the gender of the client.
On top of that, machine learning techniques play a vital role in providing an accurate estimated age. These algorithms analyse large datasets of labelled facial images to learn patterns and correlations between facial features and age. Features like wrinkles, skin texture, hair colour, and facial contours are extracted and quantified, forming the basis for age prediction models. Deep learning models have shown exceptional accuracy in age estimation tasks.
The Challenges of Age Estimation
Age estimation is a complex task that confirms a user’s age based on various aspects such as identification documents, facial images, or self-reported information. However, this raises several challenges that we will look into now:
Accuracy:
Issue: The accuracy of estimating age is a critical factor in the success of KYC processes. Traditional methods, such as relying solely on self-reported information, may be prone to errors and manipulation. On the other hand, automated age estimation algorithms, while more objective, can still have inherent biases and inaccuracies.
Solution: AI and machine learning processes ensure that age estimation is not dependent on the honesty of the user.
Privacy:
Issue: Age estimation often requires collecting sensitive personal information, such as identification documents or facial images. As many users prefer to stay private when using services that are age-restricted, they may opt out of signing up altogether when asked for an ID.
Solution: With AI-based age estimation, personal documents are only required when there are any doubts regarding the person’s age.
Ondato’s Age Estimation
With our age estimation system, you can get accurate estimates of a client’s age in just a few seconds. We boost 95% accuracy and only require personal documents when there are doubts. Our solutions can be effectively implemented in a wide variety of businesses required to verify age before providing a service, which includes sites dealing in adult content. For example, you can read more about how one of our clients applies our age estimation solution in this OnlyFans case study.
Last Thoughts
Age estimation plays a crucial role in KYC processes. It ensures compliance with age restrictions for various products and services, from adult content and gambling to alcohol and tobacco industries or even gaming. However, it is essential to address the challenges related to accuracy and privacy, which currently cannot be achieved without AI.
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