Original author is Kurt Weiss, Director of Enterprise Sales at Ekata
The financial system’s reliance on antiquated identity verification is a double-edged sword, cutting into both new revenue streams from the underbanked while exposing financial institutions to increased attacks from synthetic identities.
The underbanked, those without sufficient access to financial institutions, represent a key revenue opportunity for financial institutions. This segment is dominated by Gen-Z and Millennials who now account for almost 50% of the U.S population and well over $2 billion in spending. Yet, many of them have not engaged in traditional banking.
With no banking or credit history to refer to, these coveted customers become a blind spot to traditional KYC and credit-driven identity verification practices, which forces banks to remediate with high friction identity proofing solutions such as document requests or ID card scans. To that effect, even after all the marketing dollars are spent to attract these underbanked consumers to financial products, banks watch these customers abandon applications as these digital native customers are forced back into analog workflows.
Data is the Crux of the Problem
Traditional identity verification employed in KYC relies on data sourced when banks submit their applicants for credit scoring. For those that have never interacted with the financial system before, whether they are first-year college students or new immigrants to the U.S., those data fields are blank. These applicants don’t yet exist in the eyes of traditional identity verification products and are forced to prove that existence manually – a stark departure from the anticipated digital onboarding experience.
Meanwhile, the reliance on this traditional data leaves bank doors wide open to synthetic identity attacks. A synthetic identity is a composite of the static data elements financial institutions rely on for identity verification (think name, address, social security number, and birth date), which have all been the spoils of countless hacks made available on the dark web for malicious schemes. Once a credit file is created for synthetic identities, it is nearly impossible for credit bureaus to identify or remove them.
Cutting Through the Data Problem
Legacy infrastructure is the foundation that will likely remain. However, machine learning-based solutions can help banks check their blind spot by providing a probabilistic data set with authoritative sources that aren’t built off of or reliant on data information from credit report trades. Ekata leverages 20+ years of sourcing public records data and a dynamic global network of names, phones, email, addresses, and IPs, whose scope is broader and more inclusive than the credit system. This data helps banks positively identify more customers, especially from the underbanked segment, so they can provide these customers with the digital onboarding experience that they are accustomed to.
At the same time, Ekata mitigates synthetic identity fraud with a probabilistic risk assessment that leverages machine learning and network behaviors to isolate the falsified discrepancies of these hacked together identities.
Altogether, Ekata’s data bridges the gap between the rigors of compliance and the digital onboarding expected by new customers.
