Machine learning is all the rage. It’s making a splash with ChatGPT, Bing, and that “AI Drake” song. Machine learning gives computers the ability to learn without being programmed. In essence, it takes large pools of data and uses them to discern patterns and take action.
In the identity validation world, machine learning is typically used in two distinct areas. The first involves assessing an ID, and the second involves understanding the risk associated with the identity in question.
Verifying an ID
Identity validation often starts with determining whether an ID is real or fake. The most common technology approach is called templating. It’s like the human process for reviewing IDs, except a computer does it. In essence, the computer looks for the elements of an ID to be present and in the right places. In some cases, it will check to see if that information exists in a third-party database.
The upside to this approach is that a computer can consistently evaluate the details, looking for tells in the formatting to determine whether the license is real or fake. Another upside is that it can compare the data in the fields to see whether there is such a phone number, email address, name, etc. While this is definitely a step up from the human eye, it is still just a guess.
Machine learning improves this process by distilling what makes a good ID and what makes a bad ID by looking at the patterns revealed in large quantities of processed data. Then, this algorithm is applied to inbound identity checks to improve the results.
Once an ID has been validated, depending on the use case, assessing the risk of the identity in question may be necessary. This is the second place where there’s likely to be identity validation in machine learning. Just because someone has a valid ID doesn’t mean they’re trustworthy. This is when it’s critical to look at the “signals” and put together a risk score for that identity.
Signals are another way to describe sets of information, such as email addresses, mailing addresses, phone numbers, IP addresses, device fingerprints, etc. Signals might also be behavioral, such as time on a website or number of login attempts. Looking at the patterns behind the signals can provide a view into the risk of working with someone. Which signals an organization uses are based on its business, risk tolerance, and use case needs. The advantage of machine learning is that it can discern patterns from large amounts of data in ways that people and old technology cannot.
In these scenarios, machine learning can improve results. But what it delivers is different in the two cases.
In the case of verifying an ID through templating, machine learning is the equivalent of making a faster tricycle. That is, templating is essentially a guess, and, as a result, it’s limited in how much it can be improved. Machine learning allows for better guesses by looking at things common in fake and real IDs and factoring those elements into the decision. This might take a process that is 60% accurate and move it toward 70% accuracy. The result is better, but flaws in the original templating process limit it.
In the case of identity risk scoring, machine learning provides a level of detail and speed that was not possible before. It can find relationships between signals that may have gone undiscovered and process quantities of data that may have gone unnoticed. So, if the other process produces a faster tricycle, this process turns that tricycle into a race car.
Helping Organizations Protect Their Businesses
Our identity validation process is already highly accurate and fast. We can process an ID in under a second with a level of certainty that lets our customers make confident decisions. This is why we focus our data scientists on using machine learning to improve our risk-scoring algorithms. Our goal is to provide customers with a high-fidelity tool for better decision-making at internet speed for the best experience.
We’d love to tell you about our identity validation process and how we are better than your typical solution. Contact us today to learn more.