Data in insurance
insurance data: actuarial and claims data, telematics and new risk signals, fraud detection, and the governance and fairness of pricing models.
This block builds data fluency specific to insurance, where underwriting, pricing, claims and reserving all depend on data quality and structure. You will learn how core data concepts (structuring, lineage, integration) apply to policy, claims and actuarial data, then map the sector's key data sources, from policy administration systems to telematics and third-party bureaus, along with the quality metrics and analytics benchmarks used to judge them. Finally, you will examine the privacy rules, governance frameworks and audit practices that keep insurance data compliant and trustworthy, including consent management for sensitive health and behavioral data. The goal is practical fluency: knowing what data exists, how to judge it, and how it is controlled.
What you'll master
- Map the core data types and flows across underwriting, claims, pricing and reserving processes
- Identify and evaluate key insurance data sources including PAS, claims systems, telematics and external bureaus
- Apply data-quality metrics and analytics benchmarks to assess datasets used in actuarial and underwriting models
- Design or review a basic data governance and audit checklist aligned with insurance privacy regulations