See AI underwriting in action

Pick an example applicant below. Each report is fully de-identified — you’ll see the depersonalized data and the complete underwriting assessment, including the probability of a performant (non‑charge‑off) loan.

Choose an example

Explore example reports

Each is a fully de-identified credit report. Select one to see the depersonalized data and the full underwriting assessment. The Clarity (deep-subprime) examples are scored by a model fit to Underwrite.ai’s production engine.

Step 1 · Depersonalized report

The depersonalized report

When a report is submitted through the Underwrite.ai API, direct identifiers are stripped and protected-class data is excluded — only de-identified financial features reach the model. The examples below show that de-identified view.

Step 2 · Decision

Probability of a performant loan

A floating-point estimate between 0 and 1 (four significant figures) — the modeled probability the loan performs and does not charge off.

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P(performant)

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Risk tier and recommended action are derived from the modeled probability. Thresholds are illustrative and configurable per lender risk appetite.

Step 3 · Explainability

What moved the score

No black boxes. Every factor's signed contribution to the decision is shown — positive lowers default risk, negative raises it.

Step 4 · Underwriter analysis

Full narrative

Strengths

Risk factors

This interactive demo runs a transparent, illustrative scoring model to show how the Underwrite.ai pipeline works. It is not a credit decision, is not FCRA adverse-action output, and does not use a production model. Custom production models are trained on your own anonymized loan and application history.