What Alternative Data Do AI Credit Models Use Beyond Traditional Credit Scores?

Why Alternative Data Exists

Traditional credit scores are built largely from data reported by lenders to the major credit bureaus, including credit card and loan payment history. This works well for people with an established credit history, but it leaves out a meaningful number of people who are “credit invisible” or have a “thin file” — not enough traditional credit history to generate a reliable conventional score. This can include younger people who haven’t yet used credit products, recent immigrants without a U.S. credit history, and others who primarily pay for things with cash or debit rather than credit.

Alternative data refers to financial information beyond what traditional credit bureaus typically collect, and AI credit models are often better suited than older scoring methods to incorporate this kind of data because they can process a wider and more varied set of inputs at once.

What Kinds of Data Get Used

Common categories of alternative data include cash-flow information drawn from checking or savings account transaction history, which can show income stability and spending patterns over time; on-time payment history for recurring obligations like rent and utility bills, which aren’t always reported to credit bureaus even though missing them can be just as financially significant as missing a credit card payment; and, in some models, employment or education-related data. Some lenders also incorporate data like how long someone has held the same bank account or phone number, treated as a general stability signal.

The specific data sources used vary a great deal by lender, and consumers typically need to actively authorize access to certain data, like linking a bank account, for that data to be used in an underwriting decision.

The Trade-Off Regulators Watch Closely

Alternative data has a real potential upside: it can allow some applicants who would be denied or overlooked by a traditional credit-score-only approach to demonstrate creditworthiness through other evidence of financial responsibility. Cash-flow underwriting, for example, can show that someone consistently maintains a positive account balance and pays recurring bills on time, even if they’ve never had a credit card.

At the same time, regulators and consumer advocates caution that not all alternative data is equally reliable or fair. Some data sources may have weaker genuine predictive power for creditworthiness than they appear to, and depending on what’s used and how it’s weighted, alternative data can introduce new forms of bias just as traditional data can. This is why the Consumer Financial Protection Bureau and other regulators have emphasized that lenders using alternative data still need to test for and avoid discriminatory outcomes, and that the promise of expanded access doesn’t automatically mean a given approach is fair or accurate in practice.

Bottom Line

AI credit models increasingly draw on alternative data like bank account cash flow, rent, and utility payment history to assess creditworthiness beyond what a traditional credit score captures, which can expand credit access for applicants with thin credit files, though it requires careful testing to ensure it doesn’t introduce new fairness problems.

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