COMMENTARY: Identity has changed faster than most financial systems have kept up with. A growing number of legitimate customers don’t fit clean, traditional profiles, and treating them as edge cases creates real friction and missed growth. At the same time, fraud is getting better at hiding in that same gray space. The point isn’t to loosen controls or add more manual checks, but to look at how real people actually build identity over time versus how fraudsters try to fake it all at once. Institutions that make that distinction well will reduce risk and bring in customers others are turning away.
Across the world, young adults, new-to-country immigrants, and other underserved populations are rapidly expanding and actively reshaping the global financial landscape. By 2030, consumers ages 15 to 34 will make up 75% of the population in emerging markets. This demographic shift presents both a massive growth opportunity and a significant identity verification challenge for financial institutions. As these consumers enter the financial system seeking credit, payments, and other critical services, businesses need reliable, modern ways to welcome them without simultaneously opening the door to large-scale fraud losses.A single risk signal rarely tells the full story, but layered intelligence can. Indicators such as application velocity, device behavior, IP anomalies, email risk, and data recency collectively create a clearer picture of identity legitimacy.
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The Changing Nature of Identity
Today’s identities look very different from those of previous generations. Many young adults lack traditional financial documentation because they are delaying milestones such as owning a car or buying a home. The data shows how dramatically life has shifted: in Australia, 73% of 19-year-olds live with their parents, while in the U.S., 17% of adults ages 25 to 34 do the same. Across Europe, homeownership among 25- to 34-year-olds has dropped from 25% to 11% in a little more than a decade.Large portions of Gen Z also rely heavily on digital-first financial tools. More than half of consumers ages 18 to 25 say they use peer-to-peer payments more frequently, and people under 35 drive most of today’s buy now, pay later (BNPL) adoption. These consumers expect fast, mobile-first experiences, so much so that 60% say they would switch banks for a better digital experience.Why do these customers present thin, inconsistent, or unconventional identity footprints that often fail traditional verification methods? These groups can include many overlooked populations, such as individuals experiencing homelessness, international students, survivors of human trafficking, and people who have changed their names.Migration and Mobility Add Complexity
New-to-country immigrants represent one of the fastest-growing emerging identity groups and one of the most challenging to verify. More than 281 million people globally were living outside their birth countries in 2020, up from 93 million in 1970. Many immigrants arrive without domestic credit history, a permanent address, or local digital identity records. International students may hold valid passports and academic credentials from their home countries, yet still struggle to open bank accounts or secure mobile phone plans because legacy systems cannot authenticate cross-border identity data.These consumers also depend heavily on mobile technology. About 4.6 billion people were using mobile internet in 2022, and mobile accounted for 84% of broadband connections in 2023. Without modern identity systems that can validate foreign documentation and digital signals, businesses risk excluding this important segment.Synthetic Identities Hide in Plain Sight
The challenge is that emerging identities often look similar to one of the most damaging forms of fraud: synthetic identities. These fabricated profiles blend real, stolen, and falsified information into sophisticated digital personas that slip past legacy verification systems. Synthetic identity fraud has surged worldwide; the United Kingdom alone saw a 500% increase in high-risk synthetic identities between 2020 and 2023.Synthetic identity fraud causes up to $40 billion in global losses each year, underscoring how vulnerable traditional systems are. Yet the same data and technology that expose these risks also create opportunity. Businesses that adopt smarter, layered identity verification can safely onboard emerging customers while detecting and preventing fraud. By understanding the subtle differences between legitimate emerging identities and synthetic profiles, companies can turn this challenge into a competitive advantage.Spotting the Difference
While synthetic and emerging identities can appear similar, advanced analytics reveal patterns that distinguish real customers from fabricated ones.Fraudsters operate at what we call the “speed of light,” rapidly submitting applications to build credit-ready profiles for fake personas. Real identities, even emerging ones, develop digital footprints gradually as life events and relationships accumulate.This difference becomes especially clear at the point of application. According to internal research at LexisNexis Risk Solutions, synthetic identities are far more likely to exhibit unusual behavior, such as being:- Seven times more likely to have no first-degree relatives
- Twenty times more likely to appear in clusters of credit applications over a short period
- Seven times more likely to first surface at a credit bureau at an unusually late age
A Smarter Approach to Emerging Identity Verification
To safely onboard emerging identities without slowing trusted customers, businesses must look beyond traditional data sources and manual reviews. Industry leaders are adopting layered strategies that treat identity as a continuous, dynamic picture, including:- Using alternative and referential data such as education and transaction metadata
- Leveraging collaborative intelligence networks to identify risk signals across industries and geographies
- Authenticating documents with liveness detection to reduce friction while confirming legitimacy
- Layering digital behavior analytics, including device reputation and email age, to distinguish real consumers from synthetic attackers
