The Quiet Revolution: How Data Is Unlocking Credit for 25 Million Americans

There is a hidden group of 25 million U.S. adults who are effectively invisible to the traditional banking system. The Consumer Financial Protection Bureau noted last year that these individuals lack enough recent credit activity to generate a usable score. They are not necessarily risky, but the standard metrics simply cannot see them. This is a massive blind spot in an economy that runs on credit.

The situation is stark when you look at the subprime segment. PYMNTS Intelligence found that 35% of subprime consumers have neither a credit nor store card. Compare that to just 12% of prime consumers and a mere 4% of super-prime borrowers. This disparity suggests that the traditional credit scoring model is leaving a significant portion of the population on the sidelines, regardless of their actual ability to repay.

Beyond the FICO Score

Lenders are starting to realize that a credit file is not the whole story. A credit file does not capture every signal available to judge whether a borrower will repay. Traditional underwriting relies heavily on historical data and limited variables. This approach often misses the nuances of a borrower's current financial health.

The industry is now turning to bank transactions, cash flow, and more detailed credit histories. These data points provide a richer picture of financial behavior. By supplementing conventional measures with this additional information, lenders are widening the pool of available credit. It is a fundamental shift in how risk is assessed.

Real World Results

The results are already showing up in underwriting data. Personify Financial, working with Plaid, reported that verified bank-transaction data increased approvals by up to 8% among underserved consumer segments. Crucially, this happened while maintaining credit costs. This is a significant win for both lenders and borrowers.

Affirm is seeing similar benefits. Their latest underwriting model generated 3.4% more completed purchases at comparable risk. This means they are approving eligible applicants that their previous system would have declined. The incremental loans generated by this model are performing better than previous expansions.

The Technology Behind the Shift

The technology driving this change is sophisticated. Affirm uses a transformer model that looks for patterns within detailed credit histories. These patterns can disappear when borrower behavior is reduced to summary measures. The model extracts more detail from the order and timing of events within consumers' credit histories.

Plaid has tested this broad category of information in its LendScore model. The results are compelling. Plaid reported a 73% approval rate versus 65% for a traditional benchmark at equivalent risk. At the same approval rate, Plaid reported 41% lower risk. This is a clear demonstration of the power of alternative data.

Expanding Access

The impact on thin-file consumers is particularly notable. Plaid notes that there had been up to 5x more approval for thin-file consumers at what it termed near-prime default rates. This suggests that these borrowers are not just taking on risk, but are actually managing their finances responsibly.

PYMNTS Intelligence data illustrate why current financial information would offer a more holistic view of an applicant. This approach would be useful across several types of financial offerings. The subprime consumer research identified about 44 million U.S. adults as subprime, or 17% of consumers. This is a large and growing segment of the market.