Most lenders already have the data and the decision engine. The gap is the model in between. Carrington Labs builds that model from your own borrowers and repayment outcomes, then delivers it into the systems you already run.
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Assess credit risk more accurately and approve more loans with confidence, including thin- and no-file borrowers.
Set value-maximizing loan and line amounts with risk-based pricing that balances expected loss against contribution margin.
Identify repayment risk earlier and uncover new opportunities with early risk signals and proactive line management.
Carrington Labs delivers decision-ready outputs through API or batch.
Your data
Start with the data you have — no source is mandatory.
Carrington Labs credit analytics layer
Choose a model
PD: 1.2% Risk score: 89/100
Carrington Labs integrates with
For illustrative purposes only
For illustrative purposes only
How it works, in text
Each model is built on your own borrowers and repayment outcomes, with features engineered from cash flow and traditional data, explainable reason codes, and retraining included.
A tailored model estimating each borrower's probability of default using lender-specific data.
Transactional, bureau, application, internal performance/portfolio, and financials — any appropriate combination; transaction data is helpful but not required.
Probability of default, a personalized risk score, product-specific risk ranking, and explainable drivers/reason codes.
Recommends how much to lend and at what price to maximize value within policy.
Borrower/account data; bureau data (where available); requested amount and term; the lender's optimization ranges (amount, term, interest rate, fee); policy limits (max default probability, min acceptance probability, min contribution margin, regulatory rate/term bounds).
A recommended (value-maximizing) offer, the borrower's preferred offer, and alternatives — each with amount, term, interest rate, fee, PD, expected loss, contribution margin, acceptance probability, and expected contribution.
A fast, standardized, explainable credit-risk score based solely on account and transaction data.
Account and transaction data only — no bureau data, no PII required.
A standardized score (1–100), generalized risk segmentation, and explainable drivers/reason codes across behavioral areas (Velocity, Liquidity, Stability, Leverage, Resilience). Complements bureau scores — not a bureau score.
A flexible insight layer that returns interpretable borrower insights, metrics and attributes — defined and adjusted to the calculations your scorecards and policy rules require.
Transaction, bureau, and other lender-provided data; custom metric requirements defined by the lender.
Income/expense metrics and variability/stability measures, defined to the lender's rules and scorecard requirements. Surfaces metrics, not scores or decisions — feeds the lender's own scorecards, policy rules, and decisioning.
Post-origination monitoring that flags repayment risk and portfolio deterioration.
Persisted borrower data and repayment context — bank transactions, balances, payment history, loan tape/repayment schedule, live servicing context, and prior outreach outcomes.
Upcoming repayment-risk flags, borrower-level alerts, portfolio health signals, and next-best-action — helps detect deterioration before delinquency.
The potential uplift Carrington Labs models can deliver across risk separation, pricing, and portfolio margin.
Potential uplift our solutions can deliver based on a sample set of anonymized data. Actual outcomes vary by lender, product, portfolio, and implementation approach.
See the impact on your own portfolio before you commit, working with de-identified data throughout — then we retrain and recalibrate the model for you as your outcomes mature.
Start with a priority workflow where a lender-specific model can have the fastest impact.
No PII required — models are built and tested on de-identified transaction and performance data.
The model is tested against your historical outcomes so you can see performance and impact before deployment.
Compare outputs against your current approach and apply within your policy, retaining control over final decisions.
Once live, the model moves onto our managed service — performance monitored, and retraining scheduled around your portfolio rather than re-quoted as a new build.
Models are produced on purpose-built infrastructure rather than assembled by hand, and built on your own borrowers within client-specific data boundaries — so retraining and recalibration are routine rather than new projects.
As outcomes mature we retrain, revisit the target definition and engineer new features where the data supports them — included in the service, not quoted as a separate build.
The Credit Offer Engine turns each risk estimate into a recommended amount, term and price within your policy limits — so sharper separation shows up in margin, not just in a score.
Multi-source feature engineering across cash flow, bureau, application and portfolio data — within client-specific boundaries, never pooled into a shared standardized score.
Meets strict compliance standards while delivering on speed, fairness, and transparency.
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