When identity verification becomes a double-edged sword
Why digitalizing financial services is key to preventing synthetic identity fraud, attracting the next generation of consumers and staying in business.
The financial system’s reliance on antiquated identity verification is a double-edged sword, cutting into both new revenue streams from the underbanked while also exposing financial institutions to increased attacks from synthetic identities.
According to the Federal Deposit Insurance Corporation (FDIC) in 2021, just over 14% of US households remain underbanked, meaning they may have a bank account, but still use non-bank transactions and/or non-bank credit to manage their financial needs. Many of these individuals are classified as thin-file and Experian reported back in. To add further context to these populations, these thin file/underbanked individuals are more often from minorities nearly a quarter are made up of 15-24 year olds.
Naturally, this population represents a key revenue opportunity for financial institutions. Indeed, this segment is dominated by Gen-Z who, despite not engaging in traditional banking, will be making up 27% of the workforce over the next few years. In fact, according to a forecast set by a recent banking report into Gen Z consumers, 42.9 million Gen Zers, with $360 million in disposable income and growing, will be using mobile banking by 2025.
Therefore, conservative, old-school processes that may have worked when all banks were brick and mortar, are no longer the way to go. Specifically, with no banking or credit history to refer to, these coveted Gen Z customers become a blind spot to traditional Know Your Customer (KYC) and credit-driven identity verification practices. This, in turn, forces banks to remediate with high-friction identity verification 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 new digital products, financial institutions watch these customers abandon applications as these digital native customers are forced into friction-filled workflows.
Traditional identity verification employed in KYC relies on data sourced when banks submit their applicants for credit scoring. Naturally, for those applicants who 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. Indeed, these thin-file 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.
Another limitation to relying on a traditional, deterministic data approach for the purposes of id verification (such as KYC and AML risk assessments) is how much it can open the door to synthetic identity attacks. A synthetic identity is a composite of the static data elements financial institutions rely on for identity verification (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.
To prevent both synthetic identity theft and losing out on thin-file custom, your digital identity verification processes must involve dynamic identity elements, which are attributes that can change. Importantly, these dynamic identity attributes are global and can leverage multiple linkages, metadata, history and activity patterns to validate even the thinnest file of identities.
Cutting through the data problem – with the right identity verification data
Legacy infrastructure within financial institutions is a foundation that will likely remain, especially when compliance and regulatory requirements come into play. 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 or reliant on data information from credit report trades scope is broader and more inclusive than the credit system. These are in fact the core dynamic identity elements that we consider the global standard for identity verification and synthetic fraud prevention.
Not only do these dynamic data elements enable financial institutions to positively identify more customers with greater confidence, especially from the underbanked segment, they can also provide these customers with the digital onboarding experience that they are accustomed to in this digital economy.
At the same time, by validating these elements and understanding how they are linked online, businesses across sectors can get a much more accurate picture of who their customer is and, importantly, if they are who they say they are. This, in turn, helps mitigate 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.
With Reuters estimating that financial institutions fail to detect some 95% of synthetic identities during the onboarding process, costing banks across America upwards of $6 billion a year, it has never been more pressing for this industry to re-evaluate their approach to identity verification.
Moreover, by bridging the gap between the rigors of compliance and the digital onboarding expected by new customers, financial institutions stand out from the pack in an ever-competitive digital marketplace, attracting the next generation of banking consumers.
To learn more about how the right data can help financial institutions avoid the double-edged sword of identity verification, contact us today.
