AI Credit Underwriting Development
We build credit risk models, automated decisioning engines, and alt-data ingestion for lending products. Per-decision explainability and adverse-action reasoning are wired in from the start, ready for fair-lending review.
What we build
Credit risk & scoring models
Probability-of-default and affordability models trained on your book, benchmarked against a transparent baseline, and calibrated as repayment data comes in.
Alternative-data ingestion
Cash-flow underwriting from open-banking feeds, plus bureau, transactional, and thin-file signals normalised into model-ready features.
Automated decisioning engine
A policy layer over the model that decides approve, refer, or decline, with hard-coded lending rules, pricing tiers, and limit assignment a credit officer can read and change.
Explainability & reason codes
Per-decision feature attribution (SHAP or equivalent) mapped to plain-language reason codes, so every decline carries a defensible, human-readable rationale.
Fairness & bias testing
Disparate-impact and adverse-impact analysis across protected classes, proxy-variable detection, and model-fairness reports your compliance team can put in front of a regulator.
Adverse-action & audit trail
Automated adverse-action notice generation from the same reason codes, plus immutable decision logs and model-version records for every credit decision.
Financial workflows need evidence at every decision
Reference architecture
- Application + Consent
- Alt-Data & Bureau Ingestion
- Feature Store
- Risk Model + Reason Codes
- Policy / Decisioning Engine
- Decision (approve / refer / decline)
- Adverse-Action + Audit Log
A typical AI underwriting pipeline: an application and consent feed alt-data and bureau ingestion into a feature store; the risk model returns a score with per-feature reason codes; a policy engine turns that into an approve/refer/decline with pricing and limits; and every decision, along with its adverse-action reasons and model version, is written to an immutable audit log.
Integrations shipped across 20+ partners.
Credit bureaus
- Experian
- Equifax
- TransUnion
- Nova Credit
Open-banking & cash-flow data
- Plaid
- TrueLayer
- MX
- Tink
- Finicity
Income & employment verification
- Plaid Income
- Argyle
- Pinwheel
- The Work Number
Decisioning & rules
- Taktile
- Provenir
- Custom rules engines
ML & explainability
- SageMaker
- Vertex AI
- SHAP
- Custom Python pipelines
Which engagement fits
Project Build
For a new underwriting platform: a focused build covering data ingestion, the risk model, the decisioning engine, and the explainability and audit layers end to end.
Explore Project BuildEmbedded Squad
For ongoing model iteration and policy expansion: a dedicated team that ships new features, retrains on fresh repayment data, and adds products as your credit box widens.
Explore Embedded SquadTech Audit
For assessing an existing underwriting system: a 5-day diagnostic of model health, decisioning logic, explainability coverage, and fair-lending readiness.
Explore Tech AuditCompliance considerations
| Standard | Status | What we ship |
|---|---|---|
| Fair lending / disparate impact | In progress | We build disparate-impact and proxy-variable testing into the model pipeline and produce fairness reports for review. The policy call on what the model may use, and the fair-lending sign-off, stay with your compliance team. |
| ECOA / Reg B (adverse action, US) | In progress | The decisioning engine emits specific, accurate reason codes and auto-drafts adverse-action notices from them. We engineer the mechanism; your compliance officer owns the notice content and timing obligations. |
| FCRA (bureau data handling, US) | Compliant | Permissible-purpose gating, consent capture, and dispute-handling data flows around credit-bureau pulls, engineered so bureau data is used and retained the way the FCRA expects. |
| GDPR / UK GDPR (automated decisions) | Compliant | Lawful-basis handling, retention controls, and the human-in-the-loop and explanation paths that automated-decision rights require across the ingestion and decisioning layers. |
Built for financial-grade delivery
Money movement leaves a trail
Every payment, every risk decision, every compliance check: engineered to be explainable and audit-ready. The architecture ships with the evidence, not as an afterthought.
Frequently asked questions
Yes. Cash-flow underwriting is a core use case: we ingest open-banking feeds (Plaid, TrueLayer, MX, Tink), normalise transaction data into affordability and income-stability features, and combine it with bureau and transactional signals so thin-file applicants can still be scored fairly.
Building an AI underwriting product? Schedule a meeting.
We’ve shipped the risk models, decisioning engines, and explainability and audit layers that lending teams run in production. Tell us what you’re building.