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  /  Artificial Intelligence   /  How AI Models Can Eliminate Bias for Digital ID
AI, ML, Deep Learning, ID Scanner, Security Solutions, Identity Authentication

How AI Models Can Eliminate Bias for Digital ID

Roles of AI Models in Removing Digital ID Biases

Identity theft is deemed as a growing problem. Specifically, with the increase in online shopping, the number of online identity theft increased rapidly. A 2019 Internet security report shows cybercriminals diversify their targets and use stealthier methods to commit identity theft and fraud. In 2018, FTC processed 1.4 million fraud reports resulting in USD 1.48 billion losses. The number of fraudulent transactions and enormous data breaches continues to rise as the fraudsters and cybercriminals become more sophisticated. Various ID scanners and security solutions have been implemented using artificial intelligence (AI) to deal with these issues.

AI enables computers to make human-like decisions and automating a particular task. It empowers day-to-day technologies like search engines, self-driving cars, and facial recognition apps. The technology is also used for customer identity authentication and fraud prevention.

Machine learning (ML) and deep learning make it possible to authenticate, verify, and accurately process users’ identities at scale. Although identity verification can be well-achieved by AI models, the teams creating those models need access to data that doesn’t have a built-in bias.

A starting point of conducting identity verification is document-based. That typically begins with an individual presenting a government-issued ID to confirm identity.

In that event, the first portion of the whole equation ensures that the identity document is authentic. After that, AI-driven models tied to face verification and authentication must ensure the scanned and analysed face matches the picture on the ID.

In the case of recognition, an individual’s image might be compared to millions of other faces in a database one by one. Verification serves to match skin texture, colour, and a host of other features to an example that has already been enrolled or documented on a mobile device.

Boots on the Ground

The additional layer of scrutiny demands supervised learning. The correct human, unbiased governance must be in place to make sure that the people entities that generated the data have consented to its collection.

A Person would examine a slew of facial images to determine authenticity and audit the data. Having localised teams in a given region can go a long way toward eliminating some of the biases that might exist in the other areas.

The first step generally involves ensuring that the individual isn’t a politically exposed person and is not on any sanctions list. Companies like Jumio can provide such screening.

Although there are different verification methods, such as email verification and phone verification, without connecting to platform models like Jumio’s offer, compliance and risk officers need to talk to different vendors. Then craft contracts and work with those providers on an individual basis can be created for verification strategies.

If that vendor faces problems because of load, lack of disaster recovery and redundancy, he is stuck.

A platform can offer a one-stop-shop for know your customer (KYC), identity verification, and other services to Fls. Platforms also provide flexibility.

For instance, a bank could decide after looking at an email address and conducting a quick phone check with enough information to verify the individual’s identity. On the other side, a different individual might be subjected to further scrutiny to assign the person a risk profile based on additional, triangulated data provided by the platform.

Well-crafted datasets scrubbed of bias ensure enough evidence by dipping into all these different services that people can create a profile for the person.