Solutions · Credit risk
Reproduce what your credit model knew when it made a decision.
Build credit-risk features once from bureau, application, income, and other decision-time reports — then execute the same feature DAG historically and during live scoring.
What a credit decision actually reads
An underwriting decision is assembled from structured reports that exist around decision time — each with its own shape, its own schedule, and its own moment of arrival.
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Credit bureau reports
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Application data
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Income & socio-demographic reports
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Transaction information
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Customer & account context
These are the kinds of structured decision-time sources ClearFeature is built to compute over — not a list of packaged vendor integrations.
The architecture problem in most lending stacks
Research features live in notebooks. Historical features are re-implemented in batch pipelines for training data. Underwriting features are re-implemented again inside the decision engine. Conceptually one feature — 'active monthly payment burden' — exists three times, and every model release renegotiates the differences between them.
The consequences are familiar to every credit team: validation findings that trace back to implementation drift rather than model behavior, slow releases because three teams must synchronize, and historical decisions that are hard to reconstruct precisely. The general problem →
The credit workflow on ClearFeature
One Feature Project defines the whole path from raw reports to the model vector — and the same DAG runs for portfolio history and for the application arriving right now.
Decision-time reports
Bureau report
Application data
Income / socio-demographic report
Feature DAG
Source features
per-report computations
Dependent features
affordability, burden ratios, aggregates
Model vector
PD / affordability / limit models
your models — conceptual examples, not ClearFeature components
Decision
One application, several independently landed reports
A bureau report and an income report for the same application rarely arrive together. ClearFeature joins multiple source manifests by the complete canonical entity key, so dependent features compute over a consistent snapshot of the application — however the underlying reports landed.
Availability is part of correctness
A bureau report can describe last month and arrive an hour after your decision. In ClearFeature, every source carries its availability, and training rows built from historical decisions exclude anything that arrived later — so offline development sees the same information constraints underwriting faced.
Decision reproducibility
When feature code is versioned and source availability is recorded, a historical decision's feature vector can be reconstructed: same implementation, same information boundary. For credit teams answering model-risk and audit questions, that is materially stronger than a saved score.
What changes for the team
The data scientist who designs an affordability feature writes it once, with golden tests. That tested code — not a specification document — is what materializes history and scores live applications. Data engineering stops re-implementing research; backend teams stop owning shadow copies of model logic.
Built to run where lending data lives
ClearFeature is self-hosted: bureau data, applications, and computed features stay in infrastructure your organization controls and governs.
Tell us about your credit decision pipeline.
How do underwriting features get built and kept consistent today? We can tell you concretely what a ClearFeature pilot would look like.