Written by Jamie Twiss, CEO of Carrington Labs
For decades, credit scores have served as the primary measure of borrower readiness. Lenders have relied on FICO scores and credit histories to assess risk, and financial advisors have operated under the assumption that a client with strong credit would face few obstacles in securing loans. The system functioned well enough that it became foundational to how both industries evaluate financial capability. But traditional credit scoring has significant limitations that are becoming harder to ignore.
Lenders are modernising how they assess borrowers, moving away from credit scores alone toward cash flow data, an approach that evaluates actual financial behaviour over time rather than just debt management history. The shift has gained substantial momentum: JPMorgan Chase announced in September that it will deploy cash flow underwriting software for credit card decisions, and research from FinRegLab found that cash flow data provides a stronger and more accurate basis for predicting loan performance than credit scores alone, particularly for borrowers with limited credit history. For financial advisors, this change carries implications that extend well beyond individual loan applications.
It affects how clients qualify for credit, yes, but it also points toward analytical capabilities that could strengthen advisory practices and deepen client relationships.
The move toward behavioral metrics
Someone can have an excellent credit score while living paycheck to paycheck, or conversely, have a thin credit file but demonstrate strong financial discipline through consistent savings and thoughtful spending. Traditional scoring also invites strategic behaviour, borrowers learn to pay down credit cards right before applying for loans, or time major purchases to avoid temporary score impacts.
Cash flow data gives lenders insight into behavioural patterns over months of transaction history rather than snapshots. Using these metrics, lenders can evaluate several key behaviours. Do borrowers spend immediately when money arrives, or allocate it deliberately? Do they save for large purchases or spend first and figure out repayment later? Can they reduce expenses when necessary?
These behaviors indicate whether a borrower has the discipline and margin to handle mortgage or credit payments over time. The approach closes gaps that traditional credit scoring leaves open, particularly the ability to game the system through short-term credit manipulation.
What this means for financial advisors
Client borrowing capacity may look different from what credit scores alone would suggest. A client with a strong FICO score but inconsistent cash flow patterns might face approval challenges that wouldn’t have surfaced under traditional underwriting.
On the other hand, clients with thinner credit files but who demonstrated financial discipline, regular saving, deliberate spending, and consistent money management may now qualify for credit when they previously wouldn’t have. Advisors who understand these metrics can help clients prepare for applications more effectively and can better explain approval or denial outcomes that don’t align with conventional credit expectations.
But there’s a broader opportunity here that extends beyond lending decisions. Many advisors work with incomplete financial pictures. They see assets under management, review self-reported spending estimates, and ask clients to assess their own risk tolerance, questions that even financially sophisticated clients struggle to answer accurately. Cash flow behavioural data offers a way to supplement these traditional inputs with evidence-based insights.
Consider retirement planning. An advisor might work with a client who estimates they’ll need $5,000 per month based on current expenses. But if cashflow data reveals spending patterns that consistently run closer to $10,000 once irregular purchases, seasonal expenses, and lifestyle spending are factored in, that projection becomes significantly more accurate.
The same principle applies to risk tolerance. Advisors typically use questionnaires that ask clients how they’d react to market volatility or whether they prefer growth or preservation. These are subjective assessments, and clients often don’t know how they’ll actually behave until they’re in the situation. Cash flow data can reveal behavioural patterns that indicate risk orientation more reliably: Does someone regularly contribute to an emergency fund? Do they make impulsive, large purchases, or do they plan significant expenses months in advance? Do they adjust spending downward when income dips, or do they maintain their lifestyle and draw from savings? These patterns provide a quantitative foundation for conversations about portfolio construction and risk allocation.
Emergency fund recommendations also become more precise. Rather than defaulting to “three to six months of expenses,” advisors can assess actual spending volatility and income stability to determine what size cushion makes sense for a specific client.
Richer data supplements an advisor’s experience and intuition with behavioural evidence that clients themselves often can’t articulate, strengthening the client relationship.
For advisors who differentiate on personalized service and deep client relationships, these tools offer a way to understand clients more completely and provide guidance grounded in actual financial behaviour rather than estimates and assumptions.
