Credit and insurance AI evidence records for regulated decisions
For AI systems used in lending, creditworthiness, underwriting, insurance eligibility, claims triage, pricing, or risk scoring, teams need a traceable record of the decision and the evidence behind it.
Built for fintech, lending, insurance, risk, compliance, and model governance teams that need defensible AI decision records across regulated financial workflows.
Sector risk context
What compliance teams need to prove
Credit and insurance AI decisions can affect access to essential services and financial opportunity. A reviewable record should capture the applicant or claim reference, decision label, risk context, model version, policy rules, data references, human review status, and cryptographic verification metadata.
Evidence model
Evidence fields to capture at decision time
Applicant or claim reference
Use a stable pseudonymous subject ID or claim ID, with sensitive source data referenced rather than copied where possible.
Decision type
Capture approval, denial, score, price, limit, escalation, claim disposition, or underwriting recommendation.
Risk and policy context
Record policy version, threshold, reason codes, and applicable workflow rules.
Model and data lineage
Reference model version, feature set, scorecard, data source, or certified artifact that materially affected the output.
Human review
Track whether a human reviewer was required, completed review, changed the outcome, or accepted the AI recommendation.
Verification metadata
Include canonical payload, hash, signature, key ID, timestamp, and verification URL.
Audit questions
Questions this evidence trail should answer
- Which model or policy produced the credit, insurance, or claims decision?
- Which data, scorecard, ruleset, or artifact influenced the result?
- Was the decision automated, assisted, escalated, or overridden?
- Can the reason codes and thresholds be reviewed later?
- Can the record be verified outside the production system?
Workflow
From AI output to reviewable evidence
- 1
Capture the decision
Record the decision event, subject, system, model version, inputs or references, and reason codes at the moment the AI system acts.
- 2
Sign the payload
Canonicalize the record, compute a SHA-256 hash, sign with Ed25519, and preserve the key ID for later verification.
- 3
Link evidence
Reference datasets, model artifacts, prompts, policy versions, human review actions, and system configuration where they affect the outcome.
- 4
Export for review
Generate JSON or PDF evidence bundles that compliance, legal, procurement, or regulators can inspect without production access.
Guardrails
Evidence support is not a compliance guarantee
Evidence does not equal legal conclusion
A signed record proves integrity and provenance of the evidence record. It does not prove that the underlying decision was fair, lawful, accurate, or sufficient on its own.
Minimize sensitive data
Use pseudonymous identifiers, references, and redaction rules so the evidence trail supports review without overcollecting personal data.
Start with proof
Generate one signed decision record and verify it yourself.
The anonymous demo shows the evidence model before any integration: payload, hash, signature, key ID, verification result, and exportable record.
Related evidence surfaces