Models · Credit / Risk
Credit Oracle
Federated credit risk model for SME lending, trained on anonymised loan tape from 14 partner lenders.
- Risk: medium
- Beta
- Federated
- US
- UK
- EU
- AUC (out-of-sample)
- 0.84
- Gini
- 0.68
- PSI (vs baseline)
- 0.04
- Federated lenders
- 14
What it does
Returns a probability-of-default and a recommended pricing tier for an SME credit application. Trained federation-style across 14 partner lenders without any borrower-level data ever leaving its lender of origin.
Why it works
Federated training pools the statistical signal across lenders without pooling their data. The result is a model with materially better out-of-sample AUC than any single lender’s bespoke model — without raising the data-privacy and competition-law issues that block centralised pooling.
Compliance posture
Each prediction comes with a SHAP-style explanation suitable for adverse action notices in the US and the UK. EU deployments include the additional documentation required under the EU AI Act for high-risk credit-scoring systems.
Pricing
Pro plan and above. Federated lender onboarding includes a data audit and an explainability review.
Audit & jurisdiction
- US — reviewed and routed to local counsel.
- UK — reviewed and routed to local counsel.
- EU — reviewed and routed to local counsel.
Every run of this model is appended to a hash-chained audit log. Anyone with a run id can fetch a Merkle inclusion proof — see the Compliance & Trust page.
Reported metrics are self-attested by the model author and verifiable against the audit log.