Lender-Specific Credit Risk Models

The problem

A general-purpose score can be useful, but it was not necessarily developed for your product, customer population, acquisition channels, repayment structure, or definition of credit loss. Two lenders can serve different borrowers, offer different amounts and terms, apply different policies, and define default differently, so a score that performs well across a broad market may not separate risk best for your book.

How Carrington Labs can help

Model the outcome that matters

Define the target using your product, performance horizon, repayment structure, and bad-outcome definition.

Use the data you have

Combine suitable application, bureau, internal performance, transaction, product, and repayment data.

Benchmark, validate, deploy

Compare against your current approach, validate discrimination and calibration, then deliver via API or batch.

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How this fits in your workflow

Define the use case and outcome

Agree the product, population, performance horizon, target event, and intended model use.

Develop and validate

Build candidate models, then assess discrimination, calibration, stability, and segment performance.

A Person giving a pitch infront of a graph.

Deploy and monitor

Deliver outputs through API or batch and monitor performance and stability over time.

Who is this for

Carrington Labs supports lenders aiming to responsibly expand access.

Digital lenders
Issuers
Fintechs
Financial Institutions

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What a Carrington Labs model uses and returns

Inputs it can use

Application, bureau, internal repayment, portfolio, product, and (where available) customer-permissioned transaction data. Transaction data is not required.

Outputs it returns

Probability-of-default estimates, risk scores or segments, explainable drivers, benchmark and calibration results, and simulations, via API or batch.

You keep control

Carrington Labs provides model outputs and analytics. The lender retains control over policy, approvals, declines, and customer treatment.

FAQs

How is a lender-specific model different from a generic credit score?

A lender-specific model is developed against the lender's product, customers, data, and observed outcomes. A generic score is developed for broader use and may target a different population, outcome, or performance horizon.

Do we need transaction data?

No. A model can use application, bureau, internal repayment, portfolio, and other suitable data. Transaction data can be included when it is available and relevant.

Can Carrington Labs improve an existing model?

Carrington Labs can benchmark an existing model, identify areas of weak separation or calibration, and develop a challenger model using the available data.

Can the model be validated before deployment?

Yes. Historical validation, out-of-time testing, and strategy simulation should occur before production use, subject to the volume, maturity, and quality of available data.

Does the model replace our decision engine?

No. The model supplies risk estimates and explanatory outputs. The lender's decision engine or policy framework continues to execute the decision.

Related resources

Credit Risk Models and Analytics

Guide & Explainer

What Should a Lender Capture From Day One So Future Credit Models Can Learn From It?

The data a lender fails to capture at launch cannot be rebuilt later. Learn which decision, offer and repayment records future credit models depend on.

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