Insurance Technical Literacy

Insurance technical literacy is the source’s view that actuaries, underwriters, and risk managers need enough programming and data-tool understanding to evaluate, build, or question analytical work without becoming full-time software engineers. In EP 11: Growing Technology Footprints in Insurance Sector, Nick Blamer recommends comfort with R, Python, SQL, and basic programming concepts, while Sam adds that business specialists should be able to perform analysis or due diligence themselves.

The concept is adjacent to Actuarial Self-Study Career Path but broader than actuarial exams. It covers the practical baseline needed to work with low-code tools, spreadsheet-to-API systems, BI platforms, and AI-assisted workflows. The episode’s point is not that every insurance professional must become an engineer, but that technical literacy reduces blind dependence on vendors, IT teams, or AI outputs.

Key Claims

• Insurance professionals need enough technical skill to understand analytical workflows and model claims.
• R, Python, SQL, and basic programming concepts remain useful even when low-code or no-code tools are available.
• Technical literacy helps business users evaluate spreadsheet-to-API outputs, BI reports, and generative AI assistance.
• The skill boundary is due diligence and practical analysis, not necessarily full software-engineering ownership.
• AI Worker Literacy becomes higher-stakes in insurance because model outputs may affect regulated pricing, underwriting, risk assessment, or reporting.

Connections

• Nick Blamer and Sam (Data Science With Sam) - source voices.
• Actuarial Science, Actuarial Self-Study Career Path, and Actuary Data Scientist Partnership - professional and team context.
• Spreadsheet to API Governance, Business Logic APIs, and Insurance Technology Modernization - tool and infrastructure context.
• AI Worker Literacy, Human Judgment Under AI, and Insurance Model Regulatory Constraint - AI and validation boundary.