Artificial Intelligence in Insurance Training Course

Participants can assess whether insurance data is suitable, permitted and sufficiently reliable for AI use.

• Insurance data domains mapped across policy, claims, billing, telematics and documents
• Data lineage traced from source systems to feature and decision
• Missingness, outliers and duplicates treated using documented data-quality rules
• Feature engineering for tenure, coverage, exposure, claims and payment behaviour
• Sampling bias and proxy discrimination tested before model development
• Privacy-by-design controls applied to personal, special-category and behavioural data
• Data dictionaries specify definitions, owners, refresh cycles and permitted uses

Participants can interpret model performance measures and connect them to insurance operating decisions.

• CRISP-DM stages adapted for regulated insurance model-development lifecycles
• Training, validation and test sets separated by time and policy cohort
• Cross-validation selected for sparse outcomes and unstable claim populations
• ROC-AUC, precision-recall and calibration compared against business decision thresholds
• MAE, RMSE and quantile loss applied to severity predictions
• Confusion matrices translated into referral rates, missed fraud and workload
• Benchmark rules and champion-challenger models establish measurable incremental value

Participants can specify governance, validation and fairness controls for insurance AI models.

• Model inventory fields capture purpose, owner, data, version and approval
• NIST AI RMF functions applied to insurance model governance decisions
• ISO/IEC 42001 controls interpreted for AI management-system responsibilities
• Fairness metrics tested across protected groups and relevant proxies
• Explainability methods distinguish global drivers from individual decision reasons
• Adverse-action explanations aligned with underwriting and claims communication requirements
• Independent validation tests conceptual soundness, outcomes, stability and use controls
• Incident escalation criteria cover material harm, drift, security and conduct

Participants can design controlled AI applications for submission handling, risk selection and underwriting triage.

• Risk appetite translated into underwriting triage, referral and straight-through rules
• Submission intake uses OCR, entity extraction and document classification
• Propensity and appetite models rank submissions without replacing underwriter authority
• Geospatial, peril and exposure features support property risk assessment
• Underwriter workbenches present evidence, confidence and override reason codes
• Portfolio accumulation monitored when automated decisions alter risk selection
• Quote turnaround, bind rate and loss outcomes measure underwriting impact

Participants can assess AI methods for claims prioritisation, fraud detection and recovery management.

• Claims triage combines severity, litigation propensity and customer vulnerability signals
• Computer vision estimates vehicle damage with confidence-based human review
• Fraud detection combines anomaly scores, network analysis and investigation rules
• Special investigation unit queues prioritised by expected recoveries and harm
• Claims automation preserves audit trails, handoffs and claimant communication records
• Subrogation opportunities identified through entity resolution and liability indicators
• Leakage measurement separates legitimate claim variation from process-control failures

Participants can evaluate AI-supported pricing and reserving methods within actuarial and conduct controls.

• Frequency-severity modelling connects exposure, risk characteristics and expected loss
• Generalised linear models compared with gradient boosting for pricing governance
• Rating-factor constraints prevent prohibited or poorly justified differentiation
• Demand elasticity models quantify conversion effects of premium changes
• Loss reserving forecasts combine development triangles, assumptions and uncertainty ranges
• Scenario testing assesses catastrophe, inflation and portfolio-mix sensitivity
• Actuarial sign-off requirements retained for material pricing and reserving decisions

Participants can assess and control generative AI applications for insurance knowledge, documents and communications.

• Retrieval-augmented generation grounds responses in approved policy and procedure content
• Prompt patterns extract policy terms, summarise files and draft correspondence
• Hallucination, prompt injection and data-exfiltration risks tested before deployment
• Human review thresholds set for coverage interpretation and customer-facing messages
• Knowledge-base governance assigns source owners, review dates and access permissions
• Generative AI use cases separated from predictive-model decision use cases
• Output logging supports quality assurance, complaints handling and audit evidence

Participants can establish accountabilities and third-party controls for an insurance AI operating model.

• Target operating model assigns accountabilities across business, risk, data and technology
• Use-case prioritisation scores value, feasibility, risk, data readiness and time-to-benefit
• Business cases quantify benefits, implementation costs, controls and residual risk
• Vendor due diligence examines training data, security, explainability and exit rights
• Service-level agreements specify availability, incident response, support and change notification
• Third-party model documentation tested against internal validation and audit requirements
• Change-management plans address role redesign, training, adoption and escalation routes

Participants can create a monitored implementation plan for a controlled insurance AI deployment.

• Production monitoring tracks performance, drift, fairness, latency and override patterns
• Population stability index flags material shifts in input distributions
• Outcome back-testing compares predicted risk with emerging claims experience
• Model recalibration triggers distinguish routine maintenance from material model change
• Control dashboards report exceptions, approvals, incidents and remediation ownership
• 90-day implementation plan sequences pilots, controls, dependencies and decision gates
• Executive reporting links AI outcomes to customer, risk and financial measures