AI and Insurance: Use Cases, Legal Risks, and Governance Essentials

AI and Insurance: A Practical Guide to Adoption and Risk

AI can streamline pricing, underwriting and claims. It can lower loss ratios, reduce leakage and speed up service. To get those gains, insurers need clear controls for legal, regulatory and reputational risk-plus a plan to counter AI-enabled fraud.

Where AI Adds Value Across the Insurance Lifecycle

• Pricing and underwriting: risk scoring, propensity models, and straight-through processing on low-complexity risks.
• Claims and benefits: triage, document extraction, severity prediction, subrogation and recoveries.
• Product and distribution: demand sensing, coverage design, next-best-offer and agent assist.
• Customer service and policy admin: chatbots, email bots, workflow automation and self-serve.
• Fraud detection: anomaly detection, network analysis and media forensics.

Used well, these tools shorten cycle times, trim operating costs and improve loss outcomes.

Key Risks to Watch

Litigation risk

• Bias and discrimination: Biased data or features can skew outcomes. In Huskey v. State Farm, plaintiffs alleged algorithmic claims handling produced slower processing and less coverage for Black homeowners.
• Misrepresentation by chatbots: Generative systems can be wrong with confidence. In Moffatt v. Air Canada, a tribunal required the airline to honor a chatbot's erroneous policy statement.
• Breach of contract and good faith: Over-reliance on models can invite claims of unfair treatment. In The Estate of Gene B. Lokken v. UnitedHealth Group, plaintiffs allege AI-based denials for medically necessary care.
• Privacy and data misuse: Recording and analyzing calls with AI without proper consent can trigger claims, as raised in Michelle Gills v. Patagonia Inc.

Regulatory risk

Canada has no federal AI statute yet. Expectations are forming through privacy, human rights, IP, contract and common law, with provincial updates in Ontario, Québec and Alberta. Financial regulators are signaling specific standards, including model risk expectations for FRFIs and draft AI guidance from Québec's AMF.

• OSFI Guideline E-23 (Model Risk Management) applies to models, including those using machine learning, and takes effect May 2027. See the guideline from OSFI for scope and controls. OSFI Guideline E-23
• Global rules like the EU AI Act and various U.S. state laws influence best practices for governance, testing and transparency-even for Canada-based insurers with cross-border operations.

Reputational risk

Any of the above issues can erode trust. Customers are sensitive to AI decisions on approvals or denials and to automated service replacing human help.

AI-enabled fraud

Expect higher volumes and sophistication: fabricated invoices, altered medical documents, deepfake images or videos of damage and injuries, and spoofed identities.

Practical Steps to Mitigate Risk

1) Stand up AI governance and clear accountability

• Define ownership for each model: business, risk, compliance and IT. Keep a living model inventory with risk ratings and use cases.
• Require human oversight for high-impact decisions (e.g., claim denials, coverage rescissions, pricing exceptions). Provide an appeals path.
• Set policy for data sourcing, consent, privacy, retention and secure storage. Log all model inputs/outputs for auditability.

2) Build model risk controls

• Pre-deployment: document purpose, features, data lineage and limits; run performance, stability and explainability tests; perform fairness and sensitivity checks on protected attributes and proxies.
• Post-deployment: monitor drift, error rates, overrides, and complaint patterns; schedule periodic re-validation; maintain kill-switches and rollbacks.
• Use shadow reviews for critical use cases before full automation.

3) Secure customer-facing AI

• Keep chatbots from making binding coverage or claims determinations. Add guardrails, disclaimers, escalation rules and rate-limit sensitive responses.
• Log interactions; red-team for prompt injection, data leakage and harmful advice; retrain on verified knowledge, not open internet.

4) Strengthen fraud defenses

• Use media forensics: metadata checks, noise patterns, lighting inconsistencies and content provenance (watermarks, C2PA signals).
• Triaging: flag claims with AI-generated artifacts, unusual submission patterns or linked networks; require extra verification on high-risk cases.
• Authenticate identities with liveness detection and document verification; monitor device and IP risk signals.

5) Manage vendors and contracts

• Demand transparency on training data, evaluation metrics and known limits; require audit rights and incident reporting.
• Set SLAs, bias and performance thresholds, data-use restrictions, and indemnities for IP, privacy and security failures.

6) Keep pace with guidance

• Track updates from OSFI, FCAC, the Competition Bureau, OPC and the Ontario Human Rights Commission, plus international rules for cross-border operations.
• Document how your controls align to emerging expectations; update policies as standards mature.

7) Prepare your people

• Train underwriters, claims, SIU, product and compliance teams on AI limits, bias awareness, fraud signals and escalation paths.
• Run tabletop exercises for model failure, privacy incidents and deepfake surges.

Operational Checklist for Insurance Leaders

• Model inventory with risk tiers and owners
• Human-in-the-loop for high-impact decisions
• Bias, explainability and performance testing before launch
• Monitoring for drift, overrides, complaints and fraud patterns
• Data governance: consent, minimization, retention and encryption
• Vendor transparency, SLAs and audit rights
• Clear customer communication and recourse
• Incident response runbooks for AI misfires and privacy events

What to Do Next

Pick two to three high-ROI use cases with clear guardrails. Stand up governance in parallel and capture metrics on cycle time, loss impact, leakage and customer effort. Scale only after the controls prove themselves.

If you need structured upskilling for your team, explore role-based programs here: AI courses by job. For tools relevant to finance and insurance teams: AI tools for finance.

For regulatory expectations on model risk in Canada, review OSFI's guidance: Guideline E-23.

Disclaimer: This article provides general information and is not legal advice. For advice on your specific circumstances, consult qualified counsel.