UltraFICO® Score: Modernizing credit decisioning with cash flow intelligence

For nearly four decades, the FICO® Score has helped lenders answer one of the most important questions in credit: how likely is this borrower to repay? That question hasn't changed, but the depth and breadth of the information available to answer it has. Lenders can make better-informed decisions without overhauling the underwriting frameworks they already trust and more consumers can gain access to the credit.

Consumers manage more of their financial lives digitally than ever before. Bill payments, subscription charges, account balances, transfers and savings patterns all leave a real-time record of financial behavior. With consumer permission, that data can give lenders a more current view of income, liquidity, recurring obligations and financial volatility — and many borrowers stand to benefit. Some have limited credit histories. Some earn income from multiple sources. Some use newer financial products that may not be fully reflected in bureau data. Others may look strong on paper while showing early signs of financial strain in their transaction patterns.

That's the opportunity in front of lenders: combine those real-time signals with traditional credit data inside a proven underwriting framework, so decisions become more precise, more explainable and easier to govern at scale — without asking risk and compliance teams to start from scratch.

Why cash flow intelligence, and why now

Cash flow underwriting has moved from early experimentation into broader industry adoption, driven by three converging forces.

According to a 2024 Datos Insights survey of 434 global consumer lending institutions, 88% of U.S. consumer lenders report increased confidence in using alternative credit data compared to a year ago. Confidence in traditional credit data remains strong, and lenders are increasingly looking for additional signals that can help them compete, expand access responsibly and better understand borrower risk.

Open banking infrastructure has matured. Financial institutions, data aggregators and technology providers have spent years building the connections that let consumers share their financial data with lenders and other providers. That infrastructure is now widespread: according to Mastercard's 2025 Open Banking Trends report, 80% of U.S. consumers already link their financial accounts via open banking. While the regulatory framework around open banking is still being settled, consumer-permissioned data sharing is already standard market practice, and the obligations that matter most to underwriting-data accuracy, explainability and dispute resolution-are grounded in the FCRA and ECOA regardless of where Consumer Financial Protection Bureau’s Section 1033 lands. Lenders who build cash flow capability on that foundation now won't have to rebuild it when a final rule arrives.

Competition for growth has intensified. According to the J.D. Power 2026 U.S. Consumer Lending Satisfaction Study, fintech lenders have made significant gains in overall borrower satisfaction and now hold the majority of unsecured personal loan debt, whereas banks lead in repurchase intent and have a 23-point Net Promoter Score advantage, but that lead reflects yesterday's experience. Meanwhile, banks are holding the line on policy: in the Federal Reserve's July 2026 Senior Loan Officer Opinion Survey, banks reported consumer lending standards at the tighter end of their historical range in every category, and tighter than a year earlier in most. And the pool they are competing over is barely growing — total consumer credit outstanding expanded at just a 2.6% annual rate in the second quarter of 2026. Share has to come from somewhere other than market growth, and loosening risk standards to keep pace is the one move a regulated lender shouldn't make. What is left is better signal on the same applicants.

Borrowing has become a shopping process, and it now bears directly on underwriting. J.D. Power found that 53% of bank borrowers begin with a general internet search and that 74% of fintech borrowers submit two or more applications. Speed decides who wins them: satisfaction falls 41 points when approval takes more than an hour and 47 points when funding takes more than a day.

For lenders, the practical question that follows is how to bring cash flow data into lending decisions in a way that's measurable, disciplined and useful.

How lenders can layer cash flow data onto a framework they already trust

Traditional credit data gives lenders a proven view of how consumers have managed credit obligations over time. Cash flow data adds a more current view of liquidity, expenses and financial volatility. Together, they give lenders a view of both long-term repayment history and current financial capacity.

A lender might test cash flow data in second-look underwriting, where additional information can help distinguish applicants who look similar under traditional credit criteria. Another might use it for initial line assignment, where recurring obligations or balance trends provide context beyond the credit file. Others may start with a controlled proof of concept, scoring prior applications with and without cash flow attributes to measure incremental predictive lift.

Before cash flow data can be used in a production underwriting process, it should be standardized, categorized, validated, monitored and explained, and that work doesn't have to fall on the lender. Where cash flow signal is delivered as a validated, ready-to-deploy score rather than a payload of raw transaction data or a set of attributes to model and defend, the heavy lifting is already done: the lender's job becomes confirming that the lift holds on its own population, not building and governing a new model from scratch. The strategic path is integration: combine long-term credit performance with current financial behavior, validate the lift, monitor the outcomes and govern the process inside the same risk, compliance and model management frameworks lenders already use.

Transparency and defensibility are non-negotiable. The challenge most lenders face is to balance loss forecast stability, reserve volatility, regulatory scrutiny of AI and machine learning models, fair lending and explainability requirements, with the competitive pressure to grow originations-often simultaneously. FICO has long operated within these parameters. The structures that have made the FICO® Score a trusted standard for decades, including data accuracy obligations, explainability requirements and consumer dispute resolution, are the same structures responsible cash flow underwriting must be built on. Aligning a new score to the same odds-to-score mapping lenders already rely on is one practical way to preserve that stability, keeping cash flow signal from becoming a bespoke model that requires new cutoffs, new policy logic and new monitoring.

Fintech lenders proved the model works by having built underwriting workflows around transaction-level data, using it across origination, second-look decisions, line management, portfolio monitoring and servicing. Banks and credit unions want that same access to consumer-permissioned financial behavior, but need to introduce it inside frameworks built for regulated lending: validated attributes, documented model logic and clear third-party oversight rather than ad hoc data feeds. The opportunity for banks and credit unions isn't to copy the fintech model; it's to bring its data access into bank-grade risk and compliance processes, so cash flow insights strengthen the decision without becoming a new source of exposure.

What UltraFICO® Score delivers

UltraFICO® Score is FICO's answer to that opportunity. Made available through the FICO-Plaid partnership, it incorporates consumer-permissioned cash flow data through Plaid while maintaining compatibility with FICO® Score frameworks. The result is deeper predictive precision: credit and cash flow signals optimized into a single score that provides incremental lift on top of the industry's most trusted credit score. In FICO's validation, that translates to a 7% relative increase in approvals among thin-file and new-to-credit applicants with no incremental risk, and a 15% relative lift in model performance in key emerging consumer segments-delivered with the operational simplicity of a score designed to fit existing infrastructure rather than replace it.

For lenders, that operational simplicity is the point. Rather than handing lenders raw transaction data, UltraFICO® Score organizes cash flow behavior around concepts risk teams already understand-account depth, cash on hand, consistency, positive account history, and the presence or absence of negative information-and applies the same validation rigor and reason-code approach used in established credit-score compliance. The score is designed to be FCRA-compliant, supports consumer dispute processes and works alongside FICO® Score 8, 9, 10 or 10T, from any of the three major bureaus. UltraFICO® Score also aligns with FICO® Score odds, so it deploys within existing scoring frameworks immediately reducing system risk, retraining costs, policy changes and governance overhead, and making adoption easier for teams that already rely on FICO Scores across underwriting, account management, reporting and compliance workflows.

The bottom line

Cash flow underwriting is becoming a lasting part of credit decisioning as open banking infrastructure expands, lender confidence rises under growing regulatory attention. The case for it is straightforward: lenders get a more complete, more precise view of borrower risk without overhauling the frameworks they already trust, and consumers, particularly those underserved by traditional credit data get a shot at the credit. UltraFICO® Score is how FICO and Plaid are putting that combination into practice: cash flow intelligence, layered onto a stable and explainable scoring foundation, validated and governed at every stage.