Expanding access to credit through smarter insights

Across today’s lending landscape, many creditworthy applicants are being locked out of the system, not because they present unacceptable risk, but because the data used to evaluate them tells an incomplete story.

Mastercard’s 2026 State of Credit Decisioning study explores how lenders are navigating growing operational complexity across consumer and small business lending. The findings highlight persistent friction in how credit decisions are made and point to a clear opportunity to improve access, efficiency and confidence through better insights.

Traditional credit decisioning models were designed for a different era. Rooted in static credit histories and conventional scoring methods, they often fail to capture the full financial picture of today’s borrowers. As a result, many applicants are excluded not because they are uncreditworthy, but because their financial behaviors are not fully visible.

This challenge is particularly acute for thin-file applicants, women-owned businesses and smaller businesses with fewer than 50 employees. Many of these borrowers have viable, growing businesses but lack the type of historical data that traditional models rely on.

The study highlights a clear gap in outcomes. A majority of lenders (58%) report that fewer than half of thin-file or underserved small business applications are approved. At the same time, nearly half of small business borrowers (48%) say they have experienced either a denial or unfavorable terms in the past two years. These challenges are especially pronounced among businesses with shorter credit histories, women-owned businesses and smaller businesses.

Survey findings clearly demonstrate that barriers to credit access are, in large part, informational. The vast majority of lenders wish their organizations had more data to evaluate both thin-file SMB (88%) and consumer (88%) credit applicants. However, the system’s status quo continues to rely on legacy decisioning models built on incomplete data. Financial statements and tax documents (66%) remain the top source of data for SMB lending, while 89% of lenders still use at least one form of traditional data for consumer lending, even though lenders agree traditional credit bureau data is insufficient for accurately assessing SMB (81%) and consumer (81%) creditworthiness.

Ultimately, 85% of lenders agree that “Lack of real-time applicant data is a barrier to approving more creditworthy applicants.” Lenders believe the way to close access gaps and reach a more diverse array of creditworthy borrowers is via the operationalization of more dynamic data sources: 89% agree that “We could approve more creditworthy applicants if we had better data and tools.” This is a critical insight. The issue is not that underserved borrowers are inherently high risk. Rather, the data models used to assess them are not equipped to show the full picture of financial health.

The good news is that the industry is moving in the right direction. Lenders broadly agree that the path forward lies in operationalizing alternative data such as transaction-based insights and cash flow inputs, in a way that reduces fragmentation, streamlines decisioning and enables faster, more confident outcomes at scale. More than three-quarters (76%) of lenders say their use of alternative data has increased over the past five years, with 87% planning further integration in the next two years.

When operationalized effectively, these data sources can reduce fragmentation, streamline decisioning and support more consistent outcomes. For underserved applicants, this means greater access to fair and transparent lending decisions. For lenders, it means the ability to identify and serve more creditworthy customers, with improved precision and confidence.