The traditional methods of identity verification—for instance when a customer wants to open a new bank account or start doing business with a new institution— are, unfortunately, stale. They just don’t work anymore. Part of the problem is that the data the traditional identity verification providers - the credit bureaus - use to verify users is the same identity data that gets stolen in breach after breach. How does it make sense to use social security numbers, addresses, and birthdays to verify an identity when that same information can be easily bought on the black market? The truth is, it doesn’t.

That’s why we need to consider new approaches for identity verification. But in order to do that, we first need to bust some myths about what can and can’t happen in the verification process:

  • Myth #1: More rules lead to better security. Traditional identity verification process are rules-based, but with so much data being produced today, the rules just can’t keep up. And neither can humans. Rules-based systems ultimately deny too many legitimate applicants (often those without much credit history) and let too many fraudsters through. And creating more and more rules isn’t going to help. Rather, scrapping rules-based systems altogether and automating identity verification systems using artificial intelligence and machine learning techniques help to improve accuracy while keeping false positives and false negatives down.
  • Myth #2: It’s OK to use static data sources. Static data—like your birthday, SSN, and address—aren’t useful anymore because in this day and age it’s all too easy to find anyone’s information online. A better solution is to combine that data with dynamic data sources, such as social networks and online information. This type of information is  much harder to recreate or buy online—making it much harder for someone to create a fraudulent account in your name.
  • Myth #3: Manual review is required for first-time users. Customers who are new to an organization are the hardest to verify. Often, companies require manual review of applications, but we know humans are prone to error, leading to a lot of delayed and rejected applicants. In order to make the process frictionless and more accurate, we should reduce our reliance on manual review as much as possible. Again, a broad range of dynamic data sources can make this possible.
  • Myth #4: The rules can’t be broken. Of course they can! After all, rules are meant to be broken, right? Of course, it’s always hard to defect from a well-established approach, but when the “tried-and-true” methods aren’t working anymore, it might be time to take a step back and reevaluate.

If you’re interested in reading more on this topic, I wrote about these myths—and potential solutions—in more detail at CSO earlier this year.

Topics: Identity verification, Manual Review

Socure

Socure

Socure is the leading platform for digital identity trust. Its predictive analytics platform applies artificial intelligence and machine learning techniques with trusted online/offline data intelligence from email, phone, address, IP, device, velocity, and the broader internet to verify identities in real time. The company has more than 400 customers across the financial services, gaming, telecom, and e-commerce industries, including three of the top five banks, seven of the top 10 card issuers, three of the top MSBs, the top payroll provider, the top credit bureau, and over 100 of the largest and most successful FinTechs. Marquee customers include Chime, Varo Money, Public, Stash, and DraftKings. Investors include Accel, Commerce Ventures, Scale Venture Partners, Flint Capital, Capital One Ventures, Citi Ventures, Wells Fargo Strategic Capital, Synchrony, Sorenson, Two Sigma Ventures, and others.

Socure has received numerous industry awards and accolades, including being named to Forbes America’s Best Startup Employers 2021, being awarded Best New Technology Introduced over the Last 12 Months – Data and Data Services at the 2020 American Financial Technology Awards (AFTAs), being ranked number 70 in Deloitte’s Technology Fast 500™, being listed as a Gartner Cool Vendor, being recognized by Forbes as one of the Top 25 Machine Learning Startups to Watch, being named to CB Insights: The FinTech 250, and being awarded Finovate’s Award for Best Use of AI/ML, to name a few.