Comparison
Nothing needs to be switched off. Carrington Labs builds the model around your own outcomes, whatever the data mix — and CashScore or Prism attributes can be evaluated among the inputs, where your data rights allow.
Carrington Labs offers a standardized Cashflow Score and goes further with lender-specific Credit Risk Models. We can combine cash flow, bureau, application, product and internal performance data, engineer features around the lender’s own portfolio, include retraining as outcomes develop, and connect risk to amount, term and price through the Credit Offer Engine.
The main difference is standardization versus portfolio-specific optimization. Prism Data supplies standardized cash flow products. Carrington Labs can build and manage the model around the lender’s own product, outcomes and commercial objectives.
Prism Data’s CashScore is built on consortium data spanning multiple lenders, products and credit profiles. It converts transaction history into a standardized cash flow risk signal that can be used alone or alongside traditional credit models and scores.
Carrington Labs can provide a standardized Cashflow Score, but its core enterprise capability is a lender-specific Credit Risk Model. The model can use cash flow alongside bureau, application, product and internal repayment data and is calibrated against the lender’s own observed outcomes.
Carrington Labs gives lenders a path from a standardized Cashflow Score to a custom Credit Risk Model without changing the underlying analytics partner. The lender can start with a faster standardized signal and progress to portfolio-specific modeling when sufficient outcomes are available.
Transaction data can be highly predictive, but it is not the only relevant source. Carrington Labs can test and combine cash flow with bureau, application, product and internal performance data in one model.
Carrington Labs engineers features around the lender’s product and performance definition. This goes beyond consuming a standard attribute library by testing which variables and interactions add predictive value for the lender’s own portfolio.
As the portfolio develops, Carrington Labs can retrain the model using newer outcomes, revisit calibration and build additional features where the expanded data supports them. Retraining is included in the managed model service.
The Credit Offer Engine can use the model’s probability of default together with expected loss, revenue, funding and direct costs, expected take rate, price elasticity and lender constraints to recommend amount, term and price.
Carrington Labs is built for lenders that want:
Use historical outcomes to test whether a lender-specific model improves risk separation and expected commercial value beyond a standardized cash flow score.
Comparison based on publicly available product information as of July 2026. Third-party names and trademarks belong to their respective owners. Carrington Labs is not affiliated with or endorsed by the companies referenced unless expressly stated.
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