Central Bank Eyes Insurers' AI Use
Central Bank scrutiny of insurer AI is here; expect questions across pricing, underwriting, claims, fraud, and customer protection. Act now: inventory models, set policy, test bias.
Central Bank to watch AI use by insurers
Translation: supervision on AI is moving from theory to practice. Expect questions on where you use AI, how you control it, and how you protect customers. If you work in pricing, underwriting, claims, or distribution, this is your moment to get ahead.
Where scrutiny will land first
- Pricing and underwriting: explainability, discrimination testing, data sources, and approval gates.
- Claims automation: denial rationale, appeals flow, human review points, and error rates.
- Fraud models: false positive management and proportionality of investigations.
- Marketing and lead scoring: use of sensitive data or proxies, and customer outcomes.
- Chatbots and copilots: record-keeping, advice boundaries, and disclosure to customers.
What to do this quarter
- Create a board-approved AI policy and a clear RACI for model ownership.
- Build a single model inventory: purpose, data sources, risk tier, owner, last validation, next review.
- Stand up independent model validation for high-impact use cases.
- Require pre-deployment approval: business case, risk assessment, privacy check, security check.
- Set minimum controls: explainability, bias testing, drift monitoring, fallback procedures, human oversight.
- Tighten vendor due diligence: training data, evaluation results, logs, support SLAs, and exit plan.
Controls auditors will ask for
- Documentation: model card, data lineage, feature rationale, known limits, and change history.
- Testing: backtesting, stability checks, challenger benchmarks, and stress scenarios.
- Monitoring: drift alerts, performance thresholds, incident log, and retraining criteria.
- Access and change control: approvals, segregation of duties, and versioning from data to deployment.
- Customer outcomes: adverse-action letters, complaint analysis, and remediation playbooks.
Bias and explainability basics that pass scrutiny
- Define protected attributes and likely proxies. Test at both feature and outcome levels.
- Track metrics like demographic parity, equalized odds, and calibration by segment.
- Provide case-level reasons customers can understand. Keep the technical trace for audit.
Data practices that reduce risk
- Minimize personal data. Mask early. Keep only what you need, for as long as you need it.
- Record consent and purpose limits. Run DPIAs for higher-risk use cases.
- Guard third-country transfers and vendor sharing with contracts and encryption.
Report to the board with this one-pager
- Map of current AI use (by function) with risk tiers and owners.
- Top 5 risks, current controls, and gaps.
- 90-day remediation plan with budget and milestones.
- Customer impact metrics and complaint trends.
Useful reference frameworks
Suggested 90-day timeline
- Days 0-30: inventory all AI, freeze new high-risk deployments, set policy and risk tiers.
- Days 31-60: document model cards, run bias and explainability tests, fix obvious gaps.
- Days 61-90: implement monitoring, complete vendor reviews, brief the board, and schedule annual validations.
Upskill your team
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Regulatory attention is here. Treat AI like any other model risk, prove fair outcomes, and keep a clean audit trail. Do that, and oversight becomes routine.
