Trigger: AI may influence financial decisions or customer outcomes.
Financial AI Evidence Passport
Make financial AI reviewable
before it influences decisions.
Structured evidence for claims triage, fraud models, customer copilots, and risk analytics — performance, drift, explainability, and lineage included.
Evidence signals
Sensitive context
Why financial AI is a high-sensitivity context.
AI systems influencing financial outcomes — from insurance claims to credit decisions — affect livelihoods, access to services, and regulatory obligations. Model risk, unexplained outputs, and demographic disparities are well-documented failure modes with real customer harm.
Data categories in scope
People affected
Risk scenarios
What typically goes wrong.
Specific failure modes seen in this sensitive context — without structured evidence.
A claims triage model flags individuals at higher rates in certain postcodes.
No fairness Signal Receipts. No subgroup performance breakdown. Regulator asks for evidence. None exists.
A fraud detection API is updated by the vendor with no notification.
Model drift undetected. No Distribution Drift Monitor active. Evidence from initial approval no longer reflects live system behaviour.
A customer-support AI copilot provides product recommendations without a human review gate.
No evidence of human oversight. No audit trail. FCA Consumer Duty principles unaddressed.
Credit decision lineage cannot be reconstructed for a regulatory audit.
No Evidence Lineage Trace active. No Model Registry Trace active. Past decisions unreproducible. Regulatory exposure.
A risk analytics model has no Feature Attribution Evidence for individual predictions.
Adverse customers cannot receive a meaningful explanation. Right to explanation under GDPR Article 22 unaddressed.
Scope
What needs a Passport.
Stakeholder workflow
From trigger to access decision.
Trigger
AI system in scope
Evidence Request
Passport initiated
Review
DPO · CISO · Specialist
Decision
Access condition set
Monitor
Tide sweeps · Renewal
Trigger
AI system in scope
Evidence Request
Passport initiated
Review
DPO · CISO · Specialist
Decision
Access condition set
Monitor
Tide sweeps · Renewal
Chief Risk Officer
“A claims model or fraud API has no drift monitoring or explainability evidence.”
Require Passport with Distribution Drift Monitor Signal Receipts and Feature Attribution Evidence before live deployment.
DPO
“A financial AI model processes customer financial data and payment history.”
Review privacy section: legal basis, DPA, GDPR Art. 22 automated decision-making status.
Compliance Lead
“A customer copilot may give guidance that constitutes financial advice.”
Require human oversight gate in access conditions. Document in Passport.
Access decisions
Context Visa conditions.
The access decisions that apply in this sensitive context — and the evidence conditions that produce them.
- Performance and drift monitoring active
- Fairness Signal Receipts provided
- Explainability available for adverse decisions
- Human oversight gate for high-impact outputs
- Drift detected since last Passport approval
- Fairness evidence missing or outdated
- Subprocessor list not confirmed
- Lineage not available
- AI influences adverse customer outcomes
- Complaints or regulatory queries raised
- Subgroup evidence shows disparity above threshold
- No explainability for decisions affecting individual customers
- Demographic disparity flagged with no remediation plan
- Raw financial data exported without DPA or legal basis
Measurement
Evidence families we can structure.
The measurable evidence categories relevant to this context and the evidence signals they produce.
Performance & Drift
Accuracy, precision, recall, F1, and AUC metrics with drift detection across data and concept dimensions.
Explainability
Feature importance and prediction explanations for individual outputs — supporting customer-facing adverse decision explanations.
Fairness & Subgroup
Demographic parity, equalised odds, and disparate impact across protected attributes and customer segments.
Data Lineage
End-to-end dataset and model lineage enabling reconstruction of past decisions for audit.
Privacy & Data Governance
PII detection, data minimisation evidence, and GDPR legal basis for financial data processing.
Model Access Control
Policy-as-code governing which roles and systems can invoke the model, with audit logging.
Honest scope
What remains not assessable.
AffectLog does not overclaim. These items require external expertise, regulatory process, or long-term study.
Regulatory model risk compliance (PRA, FCA, Basel)
Regulatory model risk governance requires internal validation, supervisory review, and formal model risk management frameworks — beyond technical evidence tooling.
Instead: Engage your model risk management team and external validation for regulatory submission.
Whether a model outcome constitutes unlawful discrimination
Legal discrimination conclusions require legal analysis and regulatory adjudication — not evidence platform output.
Instead: Refer measured disparities to legal and compliance for interpretation against applicable law.
Consumer Duty or FCA compliance
Regulatory compliance assessment requires regulatory expertise and formal compliance review.
Instead: Use AffectLog evidence as input to compliance review — not as compliance certification itself.
Example
Sample Passport for this context.
Claims Triage Model v2.1
Insurance claims priority scoring · Financial Services
Access conditions
What we will not overclaim
AffectLog provides technical and operational evidence for financial AI access decisions. We do not claim regulatory compliance, model risk sign-off, or legal conclusions about fairness or discrimination. We show measured signals, data limitations, and required review conditions.
Common questions
Questions this context raises.
“We have internal model validation — that should be sufficient.”
Internal validation addresses your model risk framework. AffectLog structures the evidence that procurement, DPO, and external reviewers need: privacy, lineage, drift status, fairness signals, and access conditions — per AI system.
“Our claims model has been running for two years without issues.”
Evidence at deployment time does not cover drift, data shift, or subgroup disparities that emerge over time. Distribution Drift Monitor Signal Receipts provide ongoing evidence — not just point-in-time approval.
Get started
Make financial AI reviewable
before it influences the next customer.
Calculate the evidence scope for your financial AI portfolio. Identify which models need drift monitoring, which lack fairness signals, and which vendors still owe you a structured Passport.
AffectLog provides technical and operational evidence to support access decisions. Not regulatory compliance certification, legal advice, or model risk sign-off.