Worked example
Saffron Finance: credit pre-screening model
Saffron Finance, lender (fictional). One AI system, taken through all eight lifecycle stages, with the record produced at each step.
For reference purposes only. Not legal advice.
Fictional example Names, data and results are invented to show how the tools work.
What this example shows
RBI expectations (marked Verify), DPDP, EU high-risk credit, bias testing, a model card and a board report.
Regions: IN, EU. System: An in-house credit pre-screening model.
Try it in the tools
Fictional example. Names, data and results are invented to show how the tools work. Loaded records are marked "Example".
Load Saffron Finance: credit pre-screening model into your workspace to open the register, controls, reports center and board report with real-looking data. You can remove the example data again at any time.
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1. Set up governance
Saffron's chief risk officer becomes executive sponsor and names a model risk lead, Ravi, as AI governance lead. An AI use policy and a governance group (risk, compliance, data science, legal) are set up, with a RACI that makes the head of lending the single accountable owner for the model.
Artifacts: AI use policy, Governance charter, RACI. Stage 1 page
2. Inventory and intake
The credit pre-screening model is already live in India. Ravi adds it to the register as an in-house, provider-role system, and sets up intake so any new use of it, including the planned EU expansion, goes through review first.
Artifacts: Register entry, Intake for the EU expansion: full review. Stage 2 page
3. Assess risk and impact
Screening: the model shapes decisions about credit, so the EU tier is Possibly high-risk. Applicability for India lists the DPDP Act (phasing in) and RBI expectations for financial entities, and for the EU lists the AI Act and the GDPR. All are screening results, several marked Verify or Not settled, for counsel to confirm.
Artifacts: Screening result: Possibly high-risk, Applicability record, Impact assessment. Stage 3 page
4. Design controls and document
Controls for a high-risk, in-house system: risk management, data quality checks, documentation, human oversight, bias testing and logging. Ravi writes a model card and a datasheet for the training data, and records the lawful basis for the personal data used.
Artifacts: Control plan, Model card, Datasheet. Stage 4 page
5. Test and validate
Bias testing compares approval rates and error rates across applicant groups. One group's approval rate is lower. The team traces it to a proxy feature, removes it and re-tests. The report states the limits of the analysis, including small groups.
Artifacts: Bias testing report, Validation sign-off. Stage 5 page
6. Approve and deploy
The head of lending approves with conditions: every decline is reviewable by a credit officer, applicants get a plain explanation and a route to contest, and the model's limits are recorded. The decision and conditions are logged.
Artifacts: Approval record, Applicant notice, Oversight procedure. Stage 6 page
7. Monitor and respond
Monitoring tracks approval rates by group, drift in applicant data and override rate each month, with thresholds. A personal data issue in a test environment is logged as an S4 near miss, and the notification helper is used to list possible duties for counsel to confirm.
Artifacts: Monitoring plan, Monitoring log, Incident log. Stage 7 page
8. Review, report and retire
The board report summarizes systems by risk, the bias test and its fix, DPDP dates to watch, and a decision needed on funding independent validation. Ravi schedules the annual re-approval and the maturity reassessment.
Artifacts: Board report, Maturity roadmap. Stage 8 page
For reference purposes only. Not legal advice. Fictional example: names, data and results are invented to show how the tools work.
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