Beyond credit scores: What data really helps predict borrower risk

Credit scores still matter. But they were built for a narrower world – one where a steady job, a mortgage, and a long banking history were the norm. Today’s borrowers are more varied, and their financial lives don’t always leave the kind of paper trail that traditional scoring models were designed to read. Working with an alternative data provider has become one way lenders close that gap. Without it, good borrowers get rejected and some risky ones slip through.

Key takeaways

Why credit scores only tell part of the story

A credit score captures the past. It tells you whether someone has borrowed before and whether they repaid on time. That’s useful. But it tells you almost nothing about what’s happening in their financial life right now.

For thin-file borrowers – young adults, recent immigrants, gig workers, people who simply prefer to pay in cash – the score may be low or absent entirely. That doesn’t mean they’re risky. It means the system doesn’t have enough information about them.

At the same time, a solid score doesn’t guarantee repayment. Someone who recently lost income, took on new debt, or is under financial pressure may still carry a respectable credit history. The score looks fine. The situation isn’t.

This gap is where alternative data becomes genuinely useful.

The difference between more data and better data

Piling on more data points doesn’t automatically improve a decision. It can just as easily introduce noise, bias, or false confidence.

Useful data needs to meet a higher bar: it has to be relevant to repayment behavior, accurate, reasonably current, and collected in a way that’s transparent and consented to. Data that doesn’t meet those criteria isn’t an asset, but a liability.

The question lenders should be asking isn’t “what else can we find out about this person?” It’s “what signals actually help us understand whether this person is likely to repay?”

Digital signals that add real value

Some of the most informative signals come not from what someone reports, but from how they show up digitally.

Email and phone consistency are a good starting point. An email address that’s years old, used across multiple legitimate platforms, and tied to a real domain is a quiet indicator of stability. A freshly created address used only for this application is worth noticing.

The same logic applies to phone numbers. Whether a number is active, how long it’s been in use, and whether it’s linked to messaging platforms like WhatsApp or Telegram all contribute to a clearer identity picture.

Social and messenger presence reflects how embedded someone is in real digital and social life. An active, longstanding presence across platforms is a proxy for being a real, stable person with normal social connections.

No presence at all – particularly in markets where certain platforms are near-universal – is worth factoring in alongside other signals.

Paid subscription activity is a surprisingly useful income proxy. If someone consistently pays for Netflix, Spotify, or a gym membership, they’re demonstrating recurring financial discipline. Not wealth – just the habit of managing ongoing obligations.

Online shopping behavior follows similar logic. Active accounts on locally popular marketplaces, with purchase history over time, suggest a person who engages normally in everyday financial life.

Application-level signals matter too. Applying for a loan through a VPN isn’t automatically suspicious – but doing so at the exact moment of submitting financial information raises a legitimate question.

Combined with mismatched contact details or inconsistent identity signals across platforms, it shifts the risk picture meaningfully.

Gambling platform registrations don’t disqualify a borrower on their own. But they correlate with higher default rates, and when they appear alongside other impulsive-spending signals, they tend to reinforce a pattern rather than stand alone.

What responsible data use actually looks like

None of this should be about building a surveillance profile. The principle is proportionality: use the data that’s relevant to the decision, get proper consent, and avoid proxies that introduce demographic bias.

The goal is to understand the person behind the application. Not to find reasons to reject them, but to see them more clearly and make a fairer call.

And that means being just as clear about what’s out of scope as what’s in it.

Only data a person has already agreed to share with third-party platforms is ever in play. No covert access, no scraping, fully within GDPR. What someone writes in a private message stays private, always. Post content, social engagement, personal opinions shared online – none of it is analyzed, none of it is relevant.

The same goes for consumption behavior. What someone buys on a marketplace, what they stream on a Friday night, what music they listen to – that’s their private life, and it has no place in a credit decision.

What matters is presence, consistency, and stability, not content. The question is never “what does this person do in their personal life?” It’s “does this person’s digital footprint reflect a real, stable individual who is likely to honor a financial commitment?”

That’s a meaningful distinction. And it’s what separates responsible alternative data use from the kind of overreach that erodes trust in the system entirely.

Better data leads to better decisions

The lenders who get this right aren’t the ones with the most data. They’re the ones who use the right data, interpreted carefully, to say yes to more of the right people. And, of course, catch the real risks before they become defaults.

That’s not just better risk management. It’s more inclusive lending that can also be more profitable.