Carrington Labs

The model is the product. The company states its position directly, that most lenders already have the data and the decision engine and the gap is the model in between, and it builds that model from the lender's own borrowers and repayment outcomes using machine learning and explainable AI over transaction data. Its scoring product runs solely on bank transaction data rather than bureau files. Remove the models and nothing is left, because it sells neither workflow nor data.

The boundary is drawn clearly and repeatedly: the company does not replace origination, decisioning or servicing systems, its models supply decision-ready signals that sit alongside the lender's bureau data, policy rules, scorecards and existing models, and use is described as lender-controlled with decisions kept in the institution's hands. A second-look pattern is offered as a contained way to test cash flow data on borderline applications without rebuilding the origination stack. Held at B because no review requirement or threshold is specified for the signals themselves.

Disclosure is well above category norm. The score is decomposed into five named behavioural categories covering velocity, liquidity, stability, leverage and resilience, so a credit officer can see what drives it rather than receiving an opaque number, the accuracy claim is stated against a defined baseline at up to 30 percent above traditional credit models, and the evidence base is quantified as millions of loans and billions of data points.

Explainability is described as expanded in the second version rather than assumed. Held at B because no validation methodology, discrimination statistic, independent testing or monitoring approach is published to support the accuracy figure.

The evidence base for the scoring product is stated with unusual specificity, backed by the performance of millions of loans and billions of data points, and the parent is a listed company with published financial growth recognition. One lender customer is named, a month-to-month car lease company, alongside three named technology partners through which the models are delivered. Held at B because only one lending customer is identified and no deployment count, portfolio volume or customer outcome figure is published.

The position is mixed and no boundary statement resolves it. Bespoke models are built from each lender's own borrowers and repayment outcomes, which limits cross-client exposure, while the scoring product is explicitly trained on the performance of millions of loans pooled across the business and its parent's consumer lending operation. Nothing states what a lender contributes to that pool by adopting the product, or whether its borrower outcomes improve a score sold to competitors.

The consent basis is stated explicitly rather than assumed, with the platform described as turning customer-permissioned transaction data into decision-ready signals, which matters because bank transaction history is among the most revealing datasets about a person's life. Held at B because no data processing agreement, retention schedule or subprocessor register was located, and nothing describes how long transaction data persists after a decision is made.

No attestation, certification, trust centre or enumerated control set was located. Lenders supply complete borrower repayment histories to train bespoke models and route permissioned bank transaction data through the platform, and no control documentation accompanies either flow.

Models are described as built for each institution's specific regulatory requirements and designed for explainable, governed and lender-controlled use within the customer's compliance framework, and the protocol server exposes outputs built to those requirements rather than generic scores. Held at B because no regulator, statute or lending rule is named in any market, so a buyer cannot see which regime the compliance framing is calibrated against.

One mechanism does real work here: model features can map directly to adverse action reasons, which is the operational form the fair lending obligation takes, since a lender that cannot say why it declined someone cannot comply regardless of model quality.

The inclusion argument is framed well, that thin-file customers lack traditional credit history but are not low-information, and the score is positioned as an inclusive measure reaching thin-file and no-file applicants underserved by traditional methods. Held at B because no fairness testing, disparity analysis or approval outcome by borrower group is published.

No guarantee, indemnity or correction process was located. The applicant's position improves indirectly, since features mapping to adverse action reasons means a decline can be explained rather than merely issued, and nothing states whether a borrower can see which transaction patterns counted against them, correct misclassified transaction data, or contest a score derived from their own bank account activity.

The input is stated precisely rather than vaguely, with the score powered solely on bank transaction data, which tells a buyer exactly what the model does and does not consume and rules out the undisclosed alternative sources that make peer products hard to assess. Models are the company's own, developed within its parent group, so no external model dependency exists. Held at B because no open banking aggregator or data access provider is named, and those intermediaries determine coverage and data quality.

Three delivery partners are named individually, covering a decisioning platform through which lenders gain end-to-end control of credit strategy, a major customer platform's sales cloud, and an origination system, so the models reach several different points in a lender's stack. A protocol server additionally exposes institution-specific model outputs directly inside agentic workflows, which is a distinct delivery path from conventional interface access. Held at B because no core banking, servicing or bureau system is named.

No hosting provider, region, residency commitment or private deployment option was located. The company is based in Australia and serves lenders internationally with three countries excluded from availability, which makes the question of where transaction data is processed a live one for any buyer outside its home market, and it is unaddressed.

No pricing, packaging or basis of charge was located. Implementation speed is published and is the nearest thing to a commercial commitment, with a tailored model piloted in days and a lender onboarded in weeks, which bounds the implementation cost without indicating the licence cost.

Buyers span banks and non-bank lenders across consumer and small business lending, and coverage runs the full borrower lifecycle rather than the application alone, taking in underwriting, loan and limit sizing, risk-based pricing, post-origination limit management and early warning detection. Availability is global with three countries excluded. Held at B because one customer is named and no market-by-market presence is evidenced.