Transforming Lending with Open Banking Cash Flow Data

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Open Banking enhances lending through cash flow insights

  • Open Banking is poised to reshape lending in Canada by enabling permissioned, real-time financial data access.
  • Cash flow underwriting uses transaction data to assess income patterns, spending behaviour, and stability—often more current than traditional credit files.
  • Industry voices argue cash flow data will complement, not replace, credit scores, strengthening identity, ability-to-repay, and willingness-to-repay checks.
  • Verified banking data can also reduce friction in onboarding and add a new layer of defence against rising fraud.

These themes reflect the Open Banking Expo Canada 2026 main-stage discussion moderated by Mark Sam (Major Street Advisory) with Andrew Graham (Borrowell) and Brent Reynolds (Payson Solutions).

Open Banking Cash Flow Signals
Open Banking “cash flow data” in lending typically means consumer-permissioned access (via secure APIs) to bank-account signals such as deposits, balances, recurring bills, and transaction patterns. In underwriting, it’s most often used to strengthen ability-to-repay (income stability, liquidity, expense load) and to reduce manual verification (fewer uploaded statements and pay stubs). In practice, most lenders treat it as an additive layer alongside bureau data—especially for thin-file consumers and small businesses.

The Impact of Open Banking on Lending in Canada

Open Banking is expected to change how credit is assessed in Canada, particularly for small businesses and consumers who have historically been underserved by traditional underwriting. The shift is less about a single new “score” and more about a new evidentiary layer: access to bank-transaction data that reflects a borrower’s financial reality in close to real time.

That promise was a central theme at Open Banking Expo Canada 2026, where a main-stage fireside chat brought together Mark Sam (Major Street Advisory), Andrew Graham (Borrowell), and Brent Reynolds (Payson Solutions). Their discussion framed Open Banking as an enabler of more data-driven lending decisions—moving beyond models that can be limited, lagging, or incomplete.

Graham distilled lending into three core factors: identity, ability to repay, and willingness to repay. In that framing, Open Banking is not a niche add-on; it can strengthen all three by providing verified, up-to-date financial signals. Reynolds went further, describing the rise of cash flow underwriting as one of the most significant developments in lending in years—precisely because it offers a dynamic alternative to traditional credit data.

The Canadian impact, in practical terms, hinges on implementation: whether lenders integrate Open Banking data into core products and workflows, or treat it as a secondary “fallback” option. The panel’s message was clear: the biggest gains—better decisions, faster onboarding, broader access—come when cash flow insights are embedded into the lending lifecycle rather than bolted on at the end.

Cash Flow Data Transforms Lending
From the Open Banking Expo Canada 2026 fireside chat (Mark Sam moderating Andrew Graham, CEO of Borrowell, and Brent Reynolds, founder of Payson Solutions):
– Reynolds on why cash flow underwriting matters: “I think that’s going to be the biggest innovation since the introduction of the consumer credit score, the FICO score, back in 1989.”
– Reynolds on inclusion for new-to-credit borrowers: “Especially for people that are new to credit, it really solves that chicken and egg problem that you need a good credit history to get credit, but you need credit to get a good credit history.”
– Graham on fraud pressure and why permissioned data helps: “If there’s one thing I hear a lot about… is a big increase in fraud… I think consumer-permissioned banking data is one more tool to help fight that because it just is one more level of complexity for a fraudster.”
– Sam’s push to move from pilots to execution: “Really go for it… Figure out what the bigger piece of the pie is from a business case perspective. Get the stakeholders in line.”

Transitioning from Traditional Credit Scoring to Cash Flow Underwriting

Traditional credit scoring has long served as a proxy for risk, largely by summarizing past repayment behaviour captured by credit bureaus. But that history can be thin, delayed, or absent—especially for people new to credit or those whose financial lives don’t map neatly onto conventional products. Cash flow underwriting approaches the problem from a different angle: it evaluates what is happening now, using transaction-level data to understand income, expenses, and overall stability.

At Open Banking Expo Canada 2026, Reynolds argued that this evolution is on par with major historical milestones in consumer credit scoring—pointing to the introduction of the FICO score in 1989 as a reference point. His emphasis was on the “chicken and egg” problem: borrowers may need credit history to access credit, but need access to credit to build that history. Cash flow data can help break that loop by letting borrowers demonstrate creditworthiness through consistent inflows and responsible spending patterns.

Importantly, the direction described by the speakers is not a wholesale replacement of credit scores. The more realistic near-term model is hybrid: cash flow data complements bureau data to create a fuller view of risk. That combination can support better decisions not only at origination, but across the customer lifecycle—where lenders may want to adjust limits, refine pricing, or detect early signs of stress.

Borrowell’s Rent Advantage product illustrates this bridging approach: it enables consumers to report rent payments to credit bureaus, effectively translating a recurring cash flow behaviour into a form that traditional credit infrastructure can recognize. The broader implication is that “cash flow” and “credit file” are not competing universes; Open Banking can connect them.

Hybrid Cash-Flow Underwriting Path
A practical path from bureau-first to hybrid cash-flow underwriting (with checkpoints):
1) Start with a narrow use case (often “second-look”): keep the bureau decision as the primary path, and use cash flow data only when the file is thin or borderline.
– Checkpoint: define what “second-look eligible” means (e.g., thin-file, new-to-credit, SMB with limited bureau depth).
2) Add permissioning at the right moment in the funnel: request consent only when it can clearly reduce friction (fewer documents) or improve odds (more evidence).
– Checkpoint: track opt-in rate and drop-off at the consent screen.
3) Normalize transaction data into underwriting signals: income cadence, volatility, recurring obligations, average balance, and cash-buffer days.
– Checkpoint: set minimum data sufficiency rules (e.g., number of days/transactions/accounts needed) so decisions aren’t made on partial feeds.
4) Run a hybrid decision: combine bureau + cash flow signals (not necessarily a single blended score) to support approve/decline, pricing, and limit setting.
– Checkpoint: monitor overrides and exception reasons so policy and model gaps are visible.
5) Expand across the lifecycle: use refreshed cash flow signals for line management, early stress detection, and collections prioritization.
– Checkpoint: confirm operational ownership (risk, servicing, fraud) for each lifecycle trigger.

Benefits of Cash Flow Data in Assessing Borrower Creditworthiness

Cash flow underwriting is built on a simple premise: bank transactions can reveal patterns that matter for credit—how income arrives, how expenses behave, and whether a borrower maintains enough liquidity to absorb shocks. With Open Banking, that data can be accessed securely, reducing reliance on manual documents and static snapshots.

The benefits are most visible where traditional credit data is weakest: thin-file consumers and many small businesses whose financial strength may not be captured well by bureau-centric models. By analysing transaction data, lenders can form a more current view of financial position—income patterns, spending behaviour, and overall stability—rather than relying on lagging indicators.

Cash flow data can also improve underwriting accuracy. Research cited in industry analysis points to measurable lifts in delinquency prediction when cash flow signals are incorporated—particularly among thin-file and subprime segments. In other words, the same mechanism that can expand access can also sharpen risk differentiation, helping lenders avoid blunt “approve/decline” decisions based on incomplete files.

Finally, cash flow underwriting can support more nuanced product design. When lenders understand variability in income and expenses, they can better align repayment schedules, limits, and affordability checks with real behaviour—especially in segments where income may be irregular.

Benefit from cash flow data What lenders typically measure Example outcome (publicly reported research)
More current ability-to-repay view Income stability, expense load, liquidity/cash buffer Equifax (2025) reported improved prediction of 90-day delinquencies when cash flow signals are added—average lift of 13.2% for thin-file consumers and 8.3% for subprime borrowers (results vary by portfolio and model).
Better decisions for thin-file / credit-invisible applicants Consistency of deposits, recurring payments, balance trends FinRegLab’s research on cash flow data in underwriting highlights improved risk differentiation for applicants where bureau data is limited, supporting expanded access without relying solely on traditional files.
Less reliance on manual documents Reduced need for uploaded statements/pay stubs; fewer verification touchpoints Faster “time-to-decision” and fewer follow-ups when permissioned data replaces document collection (operational impact depends on how deeply APIs are integrated into the origination workflow).
Earlier stress detection Declining balances, rising essential spend, missed recurring payments Earlier intervention opportunities (limit management, outreach, hardship options) before issues appear in bureau reporting.

Enhancing Financial Inclusion

Financial inclusion is one of the most frequently cited outcomes of cash flow underwriting, and for good reason: it targets the structural gaps created by credit-history dependence. Reynolds explicitly highlighted how cash flow data can help people who are new to credit overcome the “chicken and egg” barrier—demonstrating reliability through real financial behaviour rather than a long credit file.

This matters for underserved consumers and small businesses alike. Transaction data can show consistent income deposits, stable recurring payments, and responsible cash management even when bureau data is limited. For lenders, that can translate into more confident approvals and fewer “no” decisions driven by missing history rather than actual inability to repay.

Borrowell’s Rent Advantage offers a concrete example of inclusion-oriented design: rent is a major recurring expense for many households, yet historically it has not always been reflected in credit bureau records. By enabling rent reporting, the product effectively converts a cash flow behaviour into credit-building data—helping consumers strengthen their profile over time.

Open Banking also reduces friction in proving financial health. Instead of assembling paperwork, borrowers can permission access to verified data, which can be especially helpful for applicants who find documentation burdensome or who have non-traditional income patterns.

Improving Risk Management

Cash flow data doesn’t just widen the top of the funnel; it can also improve risk management by making underwriting more current and granular. Traditional credit data can be “limited and lagging,” as Reynolds put it. Transaction data, by contrast, can reflect recent changes in income, spending, and liquidity—signals that matter when lenders are trying to assess ability to repay.

Industry research referenced in broader analysis suggests that adding cash flow data can improve prediction of serious delinquency (such as 90-day delinquencies), with particularly strong gains among thin-file consumers and subprime borrowers. The practical takeaway is that cash flow signals can help lenders better separate risk tiers within groups that might otherwise look similar through a bureau-only lens.

Graham’s three-factor framing—identity, ability to repay, willingness to repay—also points to risk benefits beyond pure affordability. Banking data can strengthen identity and verification steps, while transaction patterns can support a more evidence-based view of repayment capacity.

Used well, cash flow underwriting can also support earlier detection of stress: declining balances, irregular income, or shifting expense burdens can show up in transactions before they appear in traditional credit reporting. That creates opportunities for proactive risk mitigation—adjusting exposure, offering restructuring, or tightening controls—before losses crystallize.

Streamlining the Lending Process through Open Banking

One of the most immediate operational benefits of Open Banking is reduced friction. Traditional lending often requires manual documentation—bank statements, pay stubs, and other proofs that borrowers must gather and upload, and underwriters must review. Open Banking replaces much of that with secure, permissioned access to financial data, delivered through APIs. In this model, borrowers explicitly consent to share specific account data for underwriting and verification.

At Open Banking Expo Canada 2026, speakers emphasized that this can improve onboarding and customer experience by reducing repetitive steps and shortening decision cycles. In practice, it can mean fewer back-and-forth requests, less time spent validating documents, and faster movement from application to decision.

But the panel also warned about a common failure mode: treating Open Banking as a secondary option rather than integrating it into core lending products. If it’s only used as a “fallback” when traditional methods fail, lenders may miss the compounding benefits—automation, better decisioning, and improved conversion—because the process is still designed around manual workflows.

Broader industry analysis aligns with that view: open banking APIs can automate data collection and analysis, lowering underwriting costs and making smaller or more complex loans more viable to originate. For lenders, this is not just a customer-experience upgrade; it can be an operational redesign that shifts underwriting from document handling to data-driven assessment.

The strategic implication is that streamlining is not purely technical. It requires product decisions—where in the funnel permissioned data is requested, how insights are presented to underwriters, and how models and policies incorporate the new signals.

Open Banking Friction Reduction
Where Open Banking most often removes steps (or turns them into automated checks):
– ☐ Bank statements: replace uploads with permissioned account access (and consistent formatting).
– ☐ Income verification: validate deposits/payroll patterns against stated income.
– ☐ Balance/liquidity checks: compute average balance and cash-buffer days without manual review.
– ☐ Recurring obligations: identify rent, utilities, loan payments, subscriptions to improve affordability views.
– ☐ “Chasing documents”: reduce follow-ups by pulling refreshed data when consent allows.
– ☐ Underwriter time: shift effort from document reading to exception handling (edge cases, anomalies, mismatches).
Checkpoint to watch: if you still require the same documents “just in case,” you’ll keep most of the old friction while adding a new integration—so the ROI won’t show up.

The Role of Cash Flow Data in Fraud Prevention

Fraud has become a growing concern for lenders, and the Open Banking panel framed permissioned banking data as an additional tool to address it—particularly before funds are disbursed. Graham noted that, in conversations with lending and credit card partners, a “big increase in fraud” has been one of the most common concerns in the past six to 12 months.

The fraud-prevention logic is straightforward: consumer-permissioned banking data adds verification strength and increases the difficulty for fraudsters. If a lender can directly access verified transaction data—rather than relying solely on uploaded documents or self-reported information—it becomes harder to fabricate income, misrepresent cash position, or manipulate identity signals without detection.

Graham described this as “one more level of complexity for a fraudster.” That phrasing matters: Open Banking is not positioned as a silver bullet, but as a layered defence. In modern risk management, incremental friction for attackers can be valuable, especially when combined with other controls.

Cash flow data can also help detect inconsistencies. If an application claims stable income but transaction data shows irregular deposits, or if spending patterns contradict stated obligations, lenders can flag the case for additional review. Industry commentary similarly points to transaction data as a way to verify income and identify anomalies that may indicate fraud.

As with underwriting, the effectiveness depends on integration. Fraud tools work best when embedded into decisioning workflows—so that verification happens early, exceptions are handled consistently, and risk teams can act before money moves.

Layered Cash Flow Fraud Screening
A simple layered approach to using cash flow data against lending fraud (pre-disbursement):
1) Identity & account ownership
– Does the applicant control the account they’re linking (consistent identifiers, stable account usage patterns)?
2) Income plausibility
– Do deposits match the stated employer/source, cadence, and magnitude—or do they look synthetic/one-off?
3) Ability-to-repay consistency
– Do balances and recurring obligations support the requested payment, or is the application overstated?
4) Anomaly & manipulation checks
– Sudden “window-dressing” deposits, unusual reversals, rapid balance swings, or mismatched spending patterns.
5) Decisioning actions
– Auto-clear low-risk matches; route mismatches to step-up verification; block high-confidence fraud signals before funds move.

Challenges in Implementing Cash Flow Underwriting

Cash flow underwriting may be compelling, but implementation is not trivial. The Open Banking panel and broader industry analysis point to two recurring obstacles: integrating new data sources into existing lending systems, and earning consumer trust so that borrowers consent to share their data.

There is also a strategic challenge: organizations can pilot alternative data in narrow use cases, but scaling requires governance, analytics capability, and stakeholder alignment. Mark Sam’s advice to lenders exploring or piloting cash flow underwriting was to “really go for it”—build the business case, align stakeholders, and continue pushing toward a full Open Banking framework where API access becomes standard.

In practice, many lenders are already exploring these capabilities, but broader adoption depends on the rollout of a complete Open Banking framework. Without standardized access and clear rules, implementations can remain fragmented, increasing cost and limiting impact.

Finally, compliance and fairness considerations sit in the background. Industry research notes that while cash flow data can reduce bias by focusing on objective behaviour, it can also introduce new fair lending risks if models inadvertently discriminate. That makes governance and validation central to responsible scaling.

Data Integration Issues

Integrating cash flow data into lending is often less about the data itself and more about the plumbing. Legacy loan origination systems were built around documents and bureau pulls, not real-time transaction ingestion. Industry analysis highlights that lenders may need technology stacks capable of real-time data ingestion, analytics, and secure storage—capabilities that can be difficult to retrofit.

Cash flow underwriting also draws from multiple potential sources: bank account records via Open Banking APIs, payment processors, and—especially for small businesses—accounting software feeds. Each source can have different formats, refresh rates, and data quality issues. Normalizing that into consistent signals for underwriting models is a non-trivial engineering and data-governance task.

Another practical constraint is historical depth. Compared with credit bureau archives, cash flow data may not always provide long histories suitable for back-testing and model validation. Industry guidance suggests mitigating this by piloting narrow use cases first—such as “second-look” underwriting—before expanding across products and the full lifecycle.

The integration challenge is also organizational. Risk, product, compliance, and engineering teams must agree on how cash flow insights affect decisions. Without that alignment, Open Banking can end up as a bolt-on feature rather than a core capability.

Cash flow underwriting depends on consent. Borrowers must be willing to share their financial data through secure, permissioned channels. Industry analysis stresses that lenders need to prioritize data security and clearly communicate benefits to build trust—because even a technically perfect system fails if consumers decline.

The Open Banking panel’s emphasis on data access is important: the model is designed around consumer control. But that control also means adoption is sensitive to perception. If borrowers fear misuse, don’t understand what will be shared, or worry about security, they may opt out—reducing the lender’s ability to use cash flow insights and potentially reintroducing friction through manual documentation.

Trust is also shaped by how Open Banking is presented in the lending journey. If it’s framed as a surveillance-like requirement, it can backfire. If it’s framed as a way to reduce paperwork, speed decisions, and potentially improve approval odds—especially for thin-file applicants—it can be easier to accept.

Ultimately, consent is not a one-time checkbox; it’s part of a broader relationship. Lenders that treat data access as a core product feature—transparent, secure, and clearly beneficial—are more likely to see sustained adoption.

Open Banking Adoption Tradeoffs

Challenge What it can break in the real world Practical mitigation that keeps momentum
Legacy system fit (LOS built for docs + bureau pulls) Slow rollouts, manual workarounds, underwriters ignore the new data Start with a narrow “second-look” path; integrate outputs (signals + reason codes) into the same screens/workflows underwriters already use.
Data quality & normalization (multiple banks, formats, refresh rates) Inconsistent decisions, false flags, brittle models Define “data sufficiency” rules (minimum days/transactions); use standardized categorization; monitor drift and missingness by institution.
Limited historical depth vs bureau archives Harder back-testing and model validation Pilot where recency matters most; combine with bureau history; expand only after performance is stable across cohorts.
Consent drop-off (users decline linking accounts) Lower conversion; reversion to manual docs Ask at a moment of clear benefit (fewer uploads, faster decision); explain what’s accessed; offer a document-based alternative without punishing the user experience.
Fairness/model risk (new signals can create new bias pathways) Uneven outcomes across groups; governance friction Use transparent features where possible; test outcomes by segment; keep human-review paths for edge cases; document policy intent and monitoring thresholds.
Treating Open Banking as “fallback only” ROI never materializes; duplicated processes Design the funnel so permissioned data is a first-class input for the target segments/products, not an exception path.

The Future of Lending: Embracing Open Banking and Cash Flow Data

Transforming Credit Assessment

The direction of travel is clear: lending is moving toward more comprehensive, data-driven decisioning, where cash flow insights complement traditional credit scoring. The Open Banking Expo Canada 2026 discussion framed this as a shift that can strengthen the fundamentals of lending—identity, ability to repay, and willingness to repay—by grounding decisions in verified, real-time financial data.

Cash flow underwriting’s most transformative potential may be in segments where the current system underperforms: consumers new to credit and small businesses whose financial health is not well represented by bureau files. By using transaction data to understand stability and affordability, lenders can make decisions that are both more inclusive and more precise.

Just as importantly, Open Banking can change the operational experience of borrowing. When permissioned data replaces manual documentation, the lending process can become faster and less burdensome—benefiting both borrowers and underwriting teams.

The next phase is less about proving the concept and more about scaling responsibly. That means solving integration challenges, building governance and analytics capability, and ensuring consumer trust through clear consent and strong security practices.

It also means avoiding half-measures. The panel cautioned that treating Open Banking as a fallback limits impact; the real gains come when it is integrated into core products and workflows. And broader adoption will depend on the rollout of a full Open Banking framework—so that API access for cash flow underwriting becomes standardized and widely available.

For lenders, the competitive question is no longer whether cash flow data matters, but whether they can operationalize it: align stakeholders, build the business case, and embed data into decisioning in a way that improves outcomes without compromising trust.

In that sense, Open Banking is not simply a new data pipe. It is an opportunity—and a test—of whether the lending industry can modernize credit assessment for a world where financial lives are increasingly digital, dynamic, and measurable in real time.

Signals Shaping Next Execution
What to watch next (the “future” signals that change execution, not just headlines):
– Framework maturity: clearer rules and standardized API access tend to move cash flow underwriting from pilots into core origination.
– Adoption pattern: many lenders start with second-look and thin-file segments, then expand to lifecycle uses (limit management, early stress detection).
– Product integration: the biggest operational gains show up when permissioned data replaces document steps end-to-end—rather than being added as an extra verification layer.

Perspective shaped by building and scaling technology-driven businesses in regulated fintech and payments environments across Mexico and Latin America (Martin Weidemann, weidemann.tech).

This article reflects publicly available information at the time of writing on how permissioned cash flow data is being used to modernize lending decisions and operations, including examples from a Canada-focused industry event. Reported performance impacts can vary significantly by portfolio, borrower segment, and implementation quality. Open Banking frameworks and API access models continue to evolve, so specific operational details may change as updates emerge.

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