AI in insurance
AI in insurance: risk pricing, claims automation and fraud, underwriting assistance, and the fairness/regulatory constraints on models.
AI is reshaping insurance across underwriting, pricing, claims and distribution, but the sector's regulatory density and actuarial traditions make adoption distinct from other industries. This block builds fluency in how AI concepts map onto insurance workflows: risk selection, fraud detection, claims automation and customer engagement. You will examine where AI genuinely creates value across the insurance value chain, how to evaluate vendor solutions and build realistic ROI cases, and the governance frameworks specific to this sector, including model risk management, algorithmic bias in underwriting, and regulatory expectations from bodies overseeing solvency and fair treatment of policyholders. The goal is practical fluency for insurance professionals working alongside data science teams, vendors and regulators, not technical depth in building models yourself.
Ce que vous allez maîtriser
- Explain core AI concepts (machine learning, NLP, computer vision, generative AI) using insurance-specific examples like claims triage and underwriting
- Identify high-value AI use cases across underwriting, claims, pricing, distribution and fraud detection, and distinguish hype from realistic deployment
- Evaluate an AI vendor solution or internal business case using sector-appropriate ROI and adoption criteria
- Assess model risk, bias and regulatory exposure in an AI system before deployment, applying insurance-specific governance checklists