What We Mean by Borderless Behavior: Surprising Ways Behavior is Reshaping Banking
When we talk about how behavioral analytics and digital body language know no borders, most people envision a world map with boundless, borderless opportunity. And that’s part of what we mean by borderless—but behavioral analytics also breaks down borders that have nothing to do with lines on a map, and everything to do with your bottom line.
There are other highly impactful, but often hidden, barriers to growth that borderless behavioral analytics can make a huge difference.
Breaking Borders: New-to-Banking Consumers
The World Bank estimates that more than 1.4 billion adults are unbanked, having no access to the financial system, and that number would be even larger if it also included the underbanked: those who have a banking relationship but can’t apply for a loan due to short or non-existent credit history. Also known as thin-file individuals, these potential customers have very little traceable financial or credit data, payment history, or personal information that traditional models rely on for identity verification. Because of their unverifiable thin-file, they are denied banking services, which then in turn prevents them from building a verifiable history that would enable credit and banking access, and the cycle continues.
Verifying thin-file applicants is a major issue for developing and emerging markets. In several Latin American countries, 30 to 50 percent of the population over the age of 15 have an account with a financial institution, compared to more than 90 percent in the US, UK, or Spain, or even roughly 80 percent in China. This means that identity verification through credit-based factors in Latin America can be nearly impossible. But the thin-file problem is not limited to geographically confined emerging markets, but also applies to emerging consumers. Many of the 62 million thin-file Americans are young people new to the credit system, new Americans without a U.S.-based credit history, and those with credit who simply haven’t used their accounts for a while.
Gen Z and young millennials are a very competitive growth market for banking providers. Their deposit and credit habits are young and often fluctuate, as brand loyalty comes up against their distrust of the banking industry. Whether they’re new to banking or new-to-country: whatever the rationale, many of today’s youngest, most lucrative target consumers have non-traditional banking histories locking them out of traditional fraud prevention and identity verification tools.
What the unbanked and underbanked do have is predictable behavior that can be used to determine intent. While PII-based identity verification technology looks at what information applicants input during their application, such as physical addresses, emails, etc., this information is useless with thin-file applicants. Countless of these genuine applicants get rejected because of data-gaps, not because of credit-worthiness.
NeuroID’s behavioral analytics break through this border by analyzing pre-submit data and helping clients base decisions on how the applicant interacts with their application (taps, keystrokes, pastes, etc.). This indicates authenticity and intent, providing predictive power that delivers at the top of the fraud stack. With pre-submit data, it’s not what applicants put into their online applications, it’s how they put it in and their demonstrated familiarity (or unfamiliarity) with the data they’re claiming as their own. For example, high-familiarity behavior indicates they are likely valid and genuine. Unfamiliar behavior markers means they might not be who they say they are, and are more likely risky or fraudulent. Using behavior to determine the “how” ensures alignment between whether or not applicants pass fraud checks and if they should pass fraud checks, without holding anyone back due to a lack of credit history.