Why Lenders Should Look Beyond Red Flags

Artem Lalaiants is the Founder and CEO of RiskSeal with 10+ years in fintech and deep expertise in alternative credit risk scoring.

gettyAlternative data is giving lenders a wider view of credit risk. This shines when traditional credit history leaves major gaps. The Federal Reserve estimates that around 32 million U.S. adults cannot receive a conventional credit score. Seven million are credit invisible, while another 25 million have thin credit files.

Digital data can help fill some of those gaps, but a digital footprint contains more than established accounts and subscriptions. It can also reveal behaviors that seem unusual at first glance. An applicant might use a VPN, have almost no social media presence or hold an account with an online betting platform.

None of these observations automatically indicates higher risk. Yet without proper context, they can easily be seen as red flags.

Alternative data can help lenders assess people whose traditional credit records are limited. A young professional may have a stable income but little borrowing history. A recent immigrant may have years of financial experience that domestic credit bureaus cannot see.

Their digital footprints can tell us a lot about them as potential borrowers. Established online accounts, professional activity and consistent contact details help lenders build a more complete picture. However, the same digital footprint can contain information that seems concerning when viewed separately.

Let’s look at VPN usage from this perspective. An anonymized connection may raise questions during identity verification, but VPNs are also common tools for privacy and secure remote work.

Or consider someone with almost no social media presence. That might appear unusual in a world where most people maintain several online accounts, yet choosing not to use social platforms says little about someone’s ability to repay a loan.

Even registration with a betting platform can be misleading. An account’s existence does not establish how frequently someone uses it or whether the activity affects their finances.

The problem is not that lenders can see this information. It’s when they assign meaning without looking at the surrounding evidence. A model that treats every unusual behavior as a warning may end up excluding applicants it was supposed to help assess.

A single digital signal rarely tells the whole story. The essence of alternative credit scoring lies in seeing the relationships between observations.

When an unusual signal appears, I would examine four areas before assigning it significant weight.

One old registration tells a different story from dozens of accounts created within a few days. The same applies to changes in contact information. Someone who recently changed their phone number may have a perfectly ordinary explanation. Several new phone numbers, email addresses and devices appearing within a short period could warrant closer examination.

Concentration matters because isolated events are common. A cluster of unusual changes carries more information than individual events. Even then, the goal is not to count red flags, but to understand whether independent signals reveal a consistent pattern relevant to lending.

Time speaks volumes. Consider someone who has used the same email address for nine years, the same phone number for six and maintains established accounts across financial, professional and commercial services. Now compare that with an applicant whose email, phone and most online registrations appeared within the last few weeks. Both applicants might use VPNs or have limited social media activity, but their broader digital profiles provide very different levels of context.

Long-term digital continuity does not guarantee repayment. Likewise, a newly established digital identity does not mean someone is unreliable. However, looking at these signals together can help lenders distinguish ordinary behavior from patterns worth investigating.

Lenders can also examine an applicant’s wider participation in the digital economy. Long-standing registrations with financial services, marketplaces, e-wallets, subscription platforms or professional tools add useful context.

For instance, a self-employed applicant may have limited conventional employment records. Their established digital presence across professional and commercial services could provide evidence of economic activity.

These observations do not replace income verification or transaction records. They offer another layer of information that traditional credit reports may not capture.

This is where lenders can move beyond the question, “Does this applicant have a suspicious digital signal?” A more useful question is: “What does the broader digital footprint tell us about this applicant, and how does it relate to credit risk?”

Finally, lenders can examine how different pieces of information align during the application process. A familiar device, consistent location and established contact details provide one type of context. Recently created identifiers, conflicting locations and rapid device changes provide another.

Again, every observation needs restraint. People travel. They use VPNs. They replace phones. They apply for loans while away from home. None of these behaviors automatically indicates fraud or higher risk. However, inconsistencies across several independent signals may justify additional verification.

The distinction matters. Evidence of possible identity fraud should not automatically be treated as evidence that a legitimate borrower cannot repay a loan.

In my experience, that’s where alternative data drives the greatest predictive uplift: when models combine multiple signals rather than relying on each one in isolation.

Alternative data creates opportunities to assess people whom traditional scoring struggles to understand, but those opportunities come with responsibility.

The more information lenders collect, the easier it becomes to find something unusual about almost anyone. A privacy preference, an unfamiliar online service or a recent change in digital behavior can look concerning without context.

The challenge is to distinguish observations that genuinely improve risk assessment from those that simply make an applicant look different. That requires testing whether signals have predictive value, examining them together and avoiding automatic conclusions about individual behaviors.

A wider view of applicants should lead to better-informed decisions, not a longer list of reasons to reject them. Alternative data fulfills its purpose when it helps lenders understand people more accurately, rather than judging them by a single piece of their digital footprint.​

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