What if I told you that most e-KYC providers do not utilize their capabilities to their full potential? What if there’s more to verification than compliance? This article will explore the unexplored and dive into the imaginary world of (not quite) biometric verification opportunities.

Biometric verification – is a type of identity validation, where AI or humans analyze a person’s features to determine whether they are real and legit. It’s essential to keep this definition in mind as you keep reading the article. Today we will deviate from the traditional meaning and explore some of the related technologies.

What are some of the features that can be analyzed?

  • Fingerprints
  • Facial characteristics
  • Voice
  • Retina
  • DNA

When is biometric verification usually used?

Biometric verification is used everywhere. When you travel, the airport security staff uses biometric verification to compare your passport’s photo to your appearance. Besides, you likely use either a face-unlock or a fingerprint scanner on your phone. Biometric verification is also used as a part of the Know Your Customer (KYC) process. It helps prevent Money Laundering, Terrorist Financing, and other financial crime.

Why is biometric verification used?

When it comes to KYC, biometric verification is the only reliable way to verify users’ identities. Besides, biometric verification provides a fast, secure, and reliable authorization method and any security screening. Passwords, SSNs, and other security tokens can be forgotten or stolen. With new technological advancements, it becomes much harder to forge someone’s identity based on biometrics. If early face unlocks could be bypassed with a paper mask, a photo, or a person’s video, fraud attempts seem futile with current technologies.

Thinking inside of a KYC box

Among FinTechs, biometric verification usually means one thing – KYC. And that is not wrong thinking as KYC plays a massive role in our world’s economy. KYC is preventing Money Laundering and Terrorist Financing. Monetary authorities enforce KYC as it’s an integral part of the global AML strategy.

The RegTech industry is advancing rapidly. The e-KYC market size is expected to surpass $1 billion by 2026. It is not bad considering that Money Laundering accounts for 2% – 5% of global GDP involving $800 billion – $2 trillion each year.

Thanks to the abundance of resources in the industry, e-KYC providers were able to develop/improve superior technologies such as biometric verification, liveness check, Optical Character Recognition, and unique algorithms that recognize false positives.

The RegTech industry’s rise provided a playground for software developers whose constant challenge has been to reduce human power and automate as many complicated tasks as possible while retaining (and improving) the verifications’ accuracy.

Biometric verification is utilized at several steps in the KYC process:

  1. Verifying that the person is alive by analyzing blinks and head movements
  2. Making sure that the video is recorded solely for the verification purposes by tracking head movements
  3. Verifying the identity by matching video data with the photo in the document

At first, such procedures were done manually, employing hundreds of people. Now, however, AI performs biometric verification with a lot more accuracy than any human possibly could.

What can we learn from the biometric verification in KYC?

When e-KYC solutions started appearing, people were afraid at first to use them. They asked questions such as:

  • How can software accurately compare a person’s appearance to their picture in the document?
  • What if someone tricks the software?
  • What if the software misses a criminal, and they end up laundering money through the platform?

In practice, however, Machine Learning algorithms were able to surpass people’s expectations. Even professionals with 20 years of experience cannot as accurately identify the person in a document. Trained professionals can spot salient features, whereas software can analyze the visual data’s very subtleties. We learned that e-KYC solutions are much better at exposing criminals and mitigating false positives than humans are.

Besides, using software is much cheaper on multiple levels. There’s no need to hire or train people. If there’s an influx of users, it is not necessary to ask people to work overtime. Or if there’s a decrease of new users, you don’t need to lay people off. Besides, the software is not as demanding as people are. Hence, using e-KYC providers is much cheaper and more efficient.

There’s a drawback. If a company employed workers to perform tasks manually, it would eventually need to lay them off. Firing people, especially in large groups, is never a good thing for a company’s reputation.

The summary of learnings:

  • KYC providers developed special AI software capable of biometric verification, character recognition, liveness check, and much more.
  • E-KYC solutions are much more accurate both in catching criminals and mitigating false positives.
  • It is cheaper for a company to use e-KYC compared to in-house staff training.
  • Layoffs are almost inevitable, which is dangerous for a company’s reputation.

The power of analogies

The analogy is a robust tool that is exceptionally resource-efficient. Unlike trial and error or in-depth analysis, using analogies doesn’t take as much money or time. Giovanni Gavetti and Jan W. Rivkin write: “Analogies lie at the root of some of the most compelling and creative thinking in business as a whole…” They also describe how Taiichi Ohno used an analogy of American supermarkets to develop a Kanban board. He also supposedly got inspired by a bus “stop rope” for his andon cord.

Analogies are a powerful creative heuristic. Let’s think about how we can use the knowledge we got in the last two decades in the KYC field as an analogy for other industries.

Uber and mask verification

We don’t have to look far for sound uses of our knowledge.
Recently Uber has introduced mask verification.

Uber has reported a $2.9 billion loss in the first quarter of 2020 despite the surge in the food delivery business. Fewer people go outside of their homes, and they are less likely to use public transportation (including Uber) due to the high risk of infection. Uber has encouraged its drivers to stay home during the pandemic’s peak, but it becomes complicated to sustain the company. Besides, for many people, Uber is their only income. Therefore, there was a need to get back to business. However, Uber had to think of how to ensure both drivers’ and passengers’ safety.

Uber based their solution on the number of studies that signify the importance of wearing a face cover to stop infectious disease spread.

Uber has been using biometric verification to validate drivers’ identity. When they started thinking about the solution, they realized that they have the technology ready that can be adjusted to verify the presence of masks. Besides, drivers were already familiar with the steps necessary to complete the video verification. Hence, the learning curve would be much smaller.

It’s important to note that mask verification IS NOT a biometric verification in its traditional definition. “Unlike our Real-Time ID Check system, this technology detects the mask as an object in the photo, and does not process biometric information or compare mask selfies to driver photos in our database,” says Uber.

The technology for mask verification is much simpler than the software needed to match a face in the video to the document. It likely uses a simple supervised learning algorithm with two datasets: people with and without face coverings.

Uber has realized similarities and differences between identity verification and mask verification and used analogies to develop a compelling solution.

Exploring the unexplored: schools, shops, and hospitals

We just saw a successful implementation of analogy between KYC and mask verification. The same problem exists in many other places. Now schools, shops, hospitals, and most other institutions employ additional staff members to make sure that people wear face coverings. In the US, you cannot visit most of the public spaces without a mask.

Maybe there’s a way to use a similar approach?

Could we potentially use a similar technology to verify masks in other places?
We could. But there are differences.

One of the key obstacles is that unlike Uber, we cannot guarantee a phone presence. Moreover, Uber users already use the Uber app. Forcing people to install an additional app might take even more human resources than mask verification. What if we could adjust the solution accounting for those differences? Could we potentially utilize surveillance cameras? If yes, then how could we block a user from entering? How would the implementation of this technology into sliding doors look like?

There’s a world of unexplored opportunities. Especially right now.

Spending much time implementing mask verification solutions might not be useful in the long run. However, that’s why we use analogies – they help us easily adjust and pivot based on the external environment.

A challenge for you

Masks are just an example of how e-KYC providers can adjust their platforms to satisfy their customers’ ever changing needs.

Here’s a challenge for you:

Think about how you could apply the learning points of the KYC industry to solve other problems. What issues could you solve? Could it be an extra service for a KYC provider?

There are way-too-many-to-count e-KYC providers in the world. How many of them can be as innovative and flexible as Uber? The ability to adjust to customers’ needs and provide outside-of-the-box solutions is the type of service expected from the 21st-century companies. The days of the defined product offering are long gone. We live in the era of customer service and creativity. Do you?

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