AI for Insurance Operations Training Course

Participants can define the data requirements and controls needed for reliable insurance AI services.

• Policy, claims, billing and contact-centre data lineage documented
• Data-quality rules test completeness, timeliness, validity, consistency and uniqueness
• Entity resolution joins claimant, policyholder, vehicle and provider records
• Feature definitions prevent training-serving skew in operational scoring
• PII classification and retention schedules govern training-data access
• Data contracts specify ownership, refresh frequency, fields and acceptable quality

Participants can interpret predictive model outputs and set thresholds for insurance operations.

• Supervised learning selects target variables for claim severity and lapse
• Train-validation-test splits prevent leakage between policy and claim records
• Precision, recall, lift and calibration guide operational threshold selection
• Confusion matrices translate false positives into workload and customer consequences
• SHAP explanations identify influential variables for underwriting and claims decisions
• Champion-challenger testing compares models against existing business rules

Participants can design AI-supported underwriting intake, triage and referral workflows.

• Risk triage routes submissions by appetite, complexity and predicted loss
• Submission ingestion extracts fields from broker emails and proposal forms
• Document classification separates schedules, endorsements, loss runs and supporting evidence
• Referral rules combine model scores with underwriting authority limits
• Quote turnaround analysis locates bottlenecks, rework and abandonment
• Underwriter feedback labels exceptions and improves future decision quality

Participants can apply AI methods to claims routing, reserving support and customer treatment.

• FNOL triage prioritises severity, vulnerability, fraud signals and service commitments
• Computer vision estimates vehicle damage within defined confidence thresholds
• Claims segmentation assigns straight-through, assisted and specialist handling paths
• Next-best-action rules trigger reserve review, outreach or investigation
• Reserve adequacy monitoring compares predicted development against adjuster estimates
• Escalation protocols protect vulnerable customers and contested coverage decisions

Participants can define controlled generative AI and document-intelligence applications for insurance service teams.

• Intent classification routes policy changes, billing queries and claim status requests
• Retrieval-augmented generation grounds responses in approved policy and procedure content
• Knowledge-base governance assigns owners, review dates and source authority
• Prompt templates constrain tone, disclosures, escalation and prohibited advice
• Conversation analytics detects repeat contacts, sentiment shifts and unresolved demand
• Human handoff criteria preserve service quality for complex customer situations

Participants can design AI-supported fraud detection and investigation workflows with appropriate safeguards.

• Fraud typologies distinguish opportunistic, organised, application and provider fraud
• Network analysis links claimants, vehicles, addresses, phones and repairers
• Anomaly detection flags unusual frequency, timing, amount and relationship patterns
• Investigation queues rank alerts by expected value and evidential strength
• Feedback loops capture confirmed outcomes without contaminating fraud labels
• Financial-crime escalation aligns suspicious activity procedures with local obligations

Participants can establish proportionate governance controls for insurance AI systems and vendors.

• NIST AI RMF functions structure governance, measurement and response activities
• ISO/IEC 42001 controls assign AI management-system accountabilities and records
• Model inventory records purpose, owner, data, decisions and dependencies
• Bias testing compares error rates across protected or vulnerable groups
• Explainability requirements match adverse decisions, complaints and regulatory scrutiny
• Incident response covers model drift, harmful outputs and control failures
• Third-party due diligence assesses vendor data, security, audit rights and exit

Participants can define accountabilities, monitoring and change controls for production AI services.

• Target operating model defines business, data, technology and risk accountabilities
• RACI matrices allocate approval, challenge, monitoring and incident responsibilities
• MLOps pipelines version data, code, prompts, models and documentation
• Service-level measures track latency, accuracy, availability, cost and rework
• Drift monitoring distinguishes input change, performance decay and process change
• Change-control gates approve retraining, prompt revisions and threshold adjustments
• Vendor scorecards review performance, security, resilience and contractual obligations

Participants can prepare a controlled implementation roadmap for a selected insurance AI use case.

• 90-day roadmap sequences pilot, validation, release and scale decisions
• Pilot charters define scope, owner, baseline, controls and exit criteria
• Benefit cases quantify expense reduction, loss avoidance and service improvements
• Adoption plans address role redesign, training, communications and supervisor support
• Dashboard specifications connect model measures with operational and customer outcomes
• Application plan commits each participant to a governed workplace use case