For many households, credit cards and personal loans function as budgeting tools and financial lifelines when unexpected expenses arise. Credit helps smooth income fluctuations, supports emergency purchases, and offers rewards and payment flexibility that can stretch a tight budget.
Yet consumers’ perception of credit availability lags reality. A PYMNTS Intelligence September report, “Consumer Credit Economy: Strategy vs. Spontaneity—Navigating the Great Credit Divide,” found that more than 4 in 10 adults in the United States doubt they would be approved for a new credit card, nearly three times the actual denial rate of 15%
This psychological access gap causes many consumers to self-deny by never applying. Even high earners are affected, as one-third of households making over $100,000 a year believe they would probably or certainly be denied.
Income drives much of this pessimism. Only 41% of consumers earning less than $50,000 believe they would likely be approved for a new credit card, compared with 63% of those earning $50,000 to $100,000 and 67% of those earning more than $100,000.
The Limits of Traditional Underwriting
This misalignment is compounded by conventional underwriting, which relies heavily on static measures such as credit scores, outstanding debts and past payment histories. While those factors are important, they often fail to capture a consumer’s current financial capacity, especially for applicants with thin or no credit files.
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The nature of work and compensation is changing. Gig work, variable income and digital wallets are common, so a backward-looking credit score can miss a borrower’s real-time ability to repay.
Static models also struggle to recognize positive cash flow trends. Someone with a moderate credit score but steady income and strong savings habits may be more creditworthy than their score suggests.
Many consumers view credit as a tool for building their profile, with 26% of those without an active credit card citing credit-building as their primary motivation for applying, the PYMNTS Intelligence found. Without more dynamic assessments, lenders risk turning away precisely the customers who could benefit most from access.
How Cash Flow Underwriting Works
Cash flow-based underwriting seeks to close this gap by evaluating an applicant’s actual income and spending patterns rather than relying solely on a credit bureau snapshot. With the applicant’s consent, lenders review transaction data from bank accounts, payroll deposits and digital wallets. They look for recurring income streams, expense patterns and available balances to measure the borrower’s ability to service debt. Key data points include:
- Income stability: Regular paychecks or recurring gig economy earnings.
- Expense behavior: Rent, utilities, subscription services and discretionary spending.
- Liquidity: Average daily balances and savings trends.
Advanced analytics can turn these data points into a forward-looking risk profile, enabling lenders to extend credit to consumers who might otherwise be overlooked. Because the information is current and granular, it can highlight applicants with strong cash management habits even if their credit histories are limited.
Large financial institutions and FinTechs are beginning to operationalize cash flow underwriting. Chase announced Sept. 3 that it would be using Nova Credit to help power its cash flow underwriting capabilities.
Several digital lenders have launched products that use real-time bank feeds to pre-qualify borrowers in minutes, saying cash flow analysis broadens approval rates and lowers default risk. PayPal announced Sept. 4 its own tie-up with Nova Credit to underwrite consumer credit. A Sept. 10 announcement from neobank Dave highlighted the latest iteration of its artificial intelligence-driven, cash flow underwriting engine. Experian and Plaid partnered June 5 to provide proven risk insights and real-time cash flow data to banks, credit unions and consumer lenders.
A More Inclusive Credit Future
Cash flow underwriting is not a cure-all. It requires robust data security and consumer consent, and lenders must ensure that algorithms avoid unintended bias. However, the approach offers a path to narrow the perception gap and extend responsible credit. For consumers, it means their actual financial habits, not just a three-digit score, can determine access. For banks and FinTechs, it opens the door to new customer segments and deeper relationships.
