JUDI.AI

Take the models away and what remains is a loan application form. A proprietary categorization engine and risk detection logic read raw bank transaction data and turn it into a creditworthiness assessment. Both are described as the product of more than 10 million banking transactions analyzed and over $1 billion of small business applications evaluated. The models are the accumulated asset rather than a layer on top of one.

The same engine drives automated underwriting, continuous monitoring of a borrower's financial health after funds are advanced, and portfolio level reporting. Cash flow underwriting on live transaction data has no rules based equivalent.

Configuration to the institution's own credit policy is the control, and it is stated as a deployment property rather than an option, with each implementation tailored to that lender's unique policies inside eight weeks. That keeps the standard applied the accountable institution's own.

The chief executive frames the proposition the same way, saying the company is not selling a platform but championing a methodology, and that success blends people, process, technology and intentional growth, with people named first. What is absent is anything at decision time: no referral threshold, no manual review path for marginal applications, and no confidence indication accompanying an automated decision.

The headline performance claim is stated in the form a credit risk function would want: default rate held constant while measuring the change in approvals. That is a materially more disciplined claim than the approval speed or cost reduction figures vendors in this market usually publish. Behind it is a decade of performance data and ten million analyzed transactions, so the models have observed outcomes across a full credit cycle including a pandemic.

None of the underlying evidence is published. No accuracy figure, discrimination statistic, backtest result or validation documentation appears, and the default rate claim is asserted rather than shown by cohort.

More than 35 community lenders use the platform and seven are named. They include a $4.3 billion federal credit union with 240,000 members and 21 branches, and two further credit unions at 1.1 billion and 722 million in assets. A large Canadian credit union's community business vice president is quoted, and a regional credit union is quoted separately in trade press. A Seattle bank and a Hawaiian federal credit union complete the list.

Volume is stated at more than $2 billion in loan applications processed against ten million banking transactions analyzed, with a decade of performance data behind the models. A 2026 round drew investment from a credit union backed venture firm and from a community credit union directly, so customers are funding their own supplier.

Pooling is stated openly as the source of the engine's quality. The categorization logic and risk detection are attributed directly to ten million transactions and $1 billion of applications evaluated across the customer base. The pooling is disclosed as a strength rather than concealed. The institutions concerned include credit unions competing for the same small business members in overlapping regional markets. Nothing states whether a lender can decline to contribute its application and performance data, what is aggregated, or how a departing customer's contribution is treated.

No data protection agreement, retention schedule, subprocessor list or deletion commitment is published. The payload is live bank transaction data for small businesses, categorized at line level. Monitoring continues after a loan is advanced, so the platform holds an ongoing view of a borrower's finances rather than a snapshot taken at application. Nothing published states how long that data persists or what happens to the data of a declined applicant. Nor does anything say how it is handled across the Canadian and United States privacy regimes the company operates under simultaneously.

There is no public attestation, certification, trust center or named framework. More than 35 community lenders, including a $4.3 billion institution, have completed vendor due diligence on this platform. The assessments exist and have been passed repeatedly, and none of the resulting assurance is published. For a company whose growth depends on credit unions recommending it to one another, publishing that control set would remove the slowest step in every new sale.

No supervisor, statute or rule is named. That omission is becoming consequential in this market. Small business lending in the United States is moving from a lightly documented activity to a reported one, with data collection requirements designed to expose disparities in who receives credit and on what terms. Equal Credit Opportunity Act (ECOA) obligations, including adverse action notice, already apply to business applicants. A platform automating those decisions for dozens of community lenders sits directly in the path of that regime and does not address it.

One published claim is good inclusion evidence: approval rates rise by 20 percent at the same default rate. Holding the risk metric constant while moving the volume metric is the correct way to demonstrate a credit model improvement. It means the system found creditworthy borrowers the previous method rejected rather than simply accepting more risk. The company frames the problem accordingly: between 40 and 50 percent of small businesses rank access to capital as their top challenge.

What is missing is who those additional approvals went to. No analysis across borrower demographics, geographies or business types is published, and no fair lending testing is described. Continuous post drawdown monitoring means a business's standing can deteriorate on signals it never sees.

The vendor publishes no guarantee, indemnity or falsifiable accuracy commitment. The lending institution remains the decision maker and the regulated party. Because deployments are configured to its own policies, it can reconstruct which policy produced an outcome, which supports internal accountability. The small business applicant has nothing described.

There is no statement of what a declined borrower is told and no route to correct a miscategorized transaction that depressed a cash flow assessment. Nor is there a notice mechanism where continuous monitoring downgrades an existing borrower.

Input categories are named clearly, covering real time bank transaction data alongside conventional credit scores and financial statements, and the categorisation engine and risk detection logic are stated to be proprietary and built in house from the company's own accumulated data, which shortens the chain at the decisive point.

No individual provider is identified anywhere: no bank data aggregator supplying the transaction feeds, no credit bureau, no model provider and no hosting arrangement, and the aggregator in particular determines what the underwriting can actually see.

Built to supplement rather than displace, the platform adds automated analysis of live banking data to the traditional sources an institution already uses, credit scores and financial statements. That is the posture that lets a conservative lender adopt it. The company addresses the integration question publicly, confirming it can integrate with an institution's existing loan origination system while arguing that doing so may not be the optimal approach. That is a vendor arguing against the easier sale. No named system appears on either side: no origination platform, core banking system or bank data aggregator is identified.

Described only as cloud based, the platform has no published hosting provider, region selection or residency commitment. The question is live because the company operates on both sides of the Canada and United States border with customers in each. Financial institution data location is treated differently in the two jurisdictions, so a Canadian credit union will ask where its members' transaction data rests.

No pricing, packaging or basis of charge is published. Implementation effort is stated concretely at eight weeks or less with configuration to the institution's own policies, and the value case is quantified as three times the application volume with no additional staff, which tells a buyer what adoption costs in time and what it returns in capacity. Neither says what the platform costs, and nothing indicates whether charge falls per application, per institution, on origination volume or as a subscription.

Coverage is deliberately narrow and the narrowness is the strategy. JUDI.AI serves community based financial institutions, credit unions and community banks, in Canada and the United States, for small business lending specifically. The company argues explicitly that those institutions can and should lead this market rather than cede it to fintech lenders. Within that segment the range is real, from a credit union with 55,000 members to one with a quarter of a million. The product spans origination, underwriting, post drawdown monitoring and portfolio reporting rather than one step. The limit is that this is one buyer type and one loan category in two countries.