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
Key terms
Modules
Covers core data techniques applied to actuarial pricing, telematics, fraud, and model fairness.
Covers mapping, scoring, and benchmarking insurance data quality, lineage, and maturity.
Covers privacy rules, consent chains, governance councils, and audits for insurers.
Latest articles
Recent articles from the blog that apply to Insurance.
- Data governance in 2026: why compliance alone is no longer enoughRegulatory pressure on data has never been higher, but CDOs who treat governance purely as a compliance function are already falling behind. The organizations pulling ahead are the ones treating governance as a business capability with measurable commercial value.
- Data governance is not a compliance exercise, it's your most underutilized competitive weaponMost organizations treat data governance as a defensive posture, a checkbox for regulators and auditors. The CDOs who are pulling ahead understand it as an offensive capability that accelerates decision-making, unlocks AI readiness, and builds institutional trust at scale.