Consumer onboarding becomes a utility

The move to provide consumers with one-click enrollment experiences like single sign-on across apps (ex. bankID, Project Verify, GSMA RCS universal profile etc.) will ramp up. In parts of Europe and Canada where a handful of very large institutions control most of the banking structure, moves are underway for a coordinated, shared identity verification and management solution that will be made available to merchants and other providers.  

While this type of utility is certainly attractive, it is not without tremendous risk. If, and when, bad actors make their way into the into the system with fictitious identities and fraud is propagated through the trusted network, liability issues will certainly become center stage.  Given the ongoing cadence of breaches involving consumer identity information and the history of criminals successfully bypassing fraud detection systems, it’s highly likely that this type of fraud event will make the headlines sometime soon.

Omni-channel multifactor authentication becomes the norm

Customer experience and security in the web and mobile channels have been quasi-coordinated, but managed quite differently. Even within the mobile device, web and application capabilities have require different strategies and technologies. While organizations try to make the customer experiences similar across channels, the backend effort is extraordinary and resource intensive.   

Fortunately, the W3C has implemented an interoperability standard proposed by the FIDO Alliance that allows common capabilities to be shared between the web and mobile channels using the same code base. This will save developers time and money, provide new data streams for analytics and behavioral analysis, and provide a much more consistent user experience across channels.

AI becomes prevalent for Identity Verification

While Artificial Intelligence (i.e. neural network technology) will not become more prevalent in production environments for identity verification in the short term, the technology will become much more involved in the development of production models. We know of at least one very large financial services provider that was told by regulators to pursue AI-based solutions to help with their AML program.   

In the short term, AI (non-linear) models will be used as a benchmark for explainable (linear) machine learning models. When validation data sets are run in parallel with linear and non-linear models and converge on same answer, the linear models can be used to approximate the decision from the neural network. Fraud, risk management and AML, CIP/KYC processes stand to benefit from this approach as compliance officers see the benefits of explainable and transparent machine learning models over their legacy, opaque and unwieldy rules-based systems.

Topics: Identity verification, Machine Learning



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.