Community banks lose creditworthy small business borrowers to online lenders every day because static FICO scores miss current cash flow signals. Creditfern integrates directly into your loan origination system and delivers a credit decision — grounded in 12–24 months of business transaction history — in under four minutes.
Creditfern is built for community banks and credit unions with $200M–$3B in assets that offer small business loans under $500K, where traditional scorecard models routinely miss the cash flow evidence that actually predicts repayment.
Community bank loan officers rely on static FICO scores and 2–3 year financial statement reviews that miss current cash flow signals, leading to 35–55% small business application decline rates for creditworthy borrowers and 10–18 day decision cycles that push SMB owners to faster online lenders. The cost compounds: declined-but-creditworthy applicants take their banking relationships elsewhere, loan production falls short of plan, and the bank's CRA numbers in underserved tracts stay flat year after year despite real demand sitting unaddressed in the loan officer's own queue.
Loan application data (business legal name, EIN, requested amount, loan purpose), applicant-authorized bank account transaction history via Fiserv or FIS core integration, and available financial statements — submitted through the bank's existing loan origination system or Creditfern's embedded application widget.
Creditfern's credit decisioning engine ingests 12–24 months of business bank account transaction history and extracts cash flow pattern features — revenue consistency, payroll regularity, seasonal variance, overdraft frequency, supplier payment timing — then combines these with traditional credit bureau data and the bank's own portfolio historical performance to produce a credit score calibrated specifically to the community bank's SMB lending book and risk appetite.
A credit decision recommendation — approve with suggested terms, counter-offer with adjusted amount or rate, or decline with specific factor codes — delivered within 4 minutes of application submission; includes a cash flow narrative summary explaining the decision rationale in plain language for the loan officer's file documentation, and a risk-tiered pricing suggestion based on the bank's rate sheet.
12–24 months of business bank transaction history analyzed to surface creditworthy SMB borrowers FICO misses.
Creditfern extracts over 80 features from business bank account transaction data — revenue trend direction, monthly gross deposit variance, payroll cadence regularity, recurring supplier payment consistency, overdraft frequency and resolution speed, and seasonal revenue pattern stability — to build a cash flow credit profile that reflects current business health rather than a 2–3 year backward-looking financial statement snapshot. The cash flow score is calibrated to the community bank's own SMB loan performance history, so approval thresholds reflect what actually predicts repayment in that bank's market, not a generic national model.
Direct integration with Fiserv, Jack Henry, and FIS core systems — no new banker workflow required.
Creditfern integrates with Fiserv DNA, Jack Henry Symitar, FIS Profile, and Finastra Fusion through their documented API and data export interfaces, pulling business account transaction history for connected applicants without requiring manual data entry or file uploads from loan officers. The credit decision result returns to the bank's loan origination system as a structured data field alongside a human-readable narrative, preserving the banker's existing workflow. For applicants who bank elsewhere, Creditfern connects to external transaction history through Plaid's business banking API with applicant authorization.
Credit thresholds trained on each community bank's own historical SMB loan performance, not industry averages.
A $300M community bank in rural North Carolina and a $2B credit union in Charlotte have different SMB customer bases, different industry concentrations, and different historical loss rates. Creditfern's model calibration process ingests 3–5 years of the bank's own SMB loan performance data — approved loans with repayment outcomes and declined applications with alternative credit outcomes where available — to set approval thresholds and risk-tier boundaries that reflect that specific bank's experience. Banks that complete calibration report a 20–30% reduction in decision inconsistency across loan officers compared to manual review.
Automated loan file documentation written in plain English for compliance, examiner review, and borrower communication.
Every Creditfern credit decision generates a narrative summary explaining the key factors that drove the recommendation: which cash flow signals supported approval or triggered concern, how the applicant compared to the bank's approval population, and what specific factor codes drove a decline or counter-offer. The narrative is formatted for loan file documentation and meets CFPB adverse action notice requirements for declined applications, reducing the loan officer's documentation burden and ensuring consistent regulatory compliance across every decision.
Built-in Community Reinvestment Act tracking and fair lending disparity monitoring across SMB applications.
Community banks face CRA obligations and fair lending examination risk that require documentation of credit decision consistency across geographic assessment areas and demographic proxies. Creditfern tracks application volumes, approval rates, and credit decision factor patterns across census tract income tiers, business revenue bands, and loan purpose categories — generating a quarterly CRA performance summary and flagging statistical anomalies in approval rate disparity that warrant investigation before examination. The fair lending module does not use protected class attributes in credit scoring; it monitors for proxy variable disparity as an early warning system.
Side-by-side view of Creditfern cash flow score, traditional FICO, and historical comparable loans for each application.
Creditfern presents loan officers with a structured decision support view: the cash flow score and its key drivers, the traditional credit bureau score, a comparison to the bank's approved and declined population at similar score ranges, and three to five historical comparable loans from the bank's own book with their actual repayment outcomes. This gives loan officers the context to understand what the model is and isn't seeing, support exceptions with documented reasoning, and build intuition for which cash flow patterns in their market tend to be predictive — turning the model from a black box into a coaching tool.
Creditfern is purpose-built for community banks and credit unions serving small business borrowers. We are not a generic platform that happens to work for community banks — every product and integration decision is filtered through that segment's specific workflow, regulatory posture, and core system environment.