Not assessed
unknown
sources: BLS OES (approx) · O*NET
0 sources
7 core tasks shown
Insurance Underwriters High exposure: 4 of 7 core tasks show high task automation potential : exposure 50/100 (not assessed).
No model has published evidence mapped to this occupation yet - the score comes from generic task assessment.
Score breakdown
Why this score?
Every component is stored with the score row and can be recomputed from the evidence table.
Methodology - · score - · input hash -
Strength of current models on the capabilities this job's tasks require.
Task-level exposure, weighted by task importance. A component of the headline, not the headline itself.
Evidence that AI is actually being used for this work.
Observed changes in postings, employment or earnings.
Quantity and source quality of the evidence behind the score.
0 evidence item(s) contribute. Calculated -.
Overview
Review individual applications for insurance to evaluate degree of risk involved and determine acceptance of applications.
Employment
-
approx. BLS OES
Median wage
-
approx. BLS OES
Tasks tracked
7
linked to this occupation
Employment and wage values are approximate reference figures from the BLS Occupational Employment and Wage Statistics programme, stored with an approximation flag. Verify against the O*NET record and BLS before quoting them.
Tasks
Task-level automation exposure
Core tasks only. Exposure describes how much of the task output current AI can produce or substantially assist - not whether the task disappears. Secondary shared-baseline edges are hidden until per-occupation curation is complete.
core task · importance 4.5/5
core task · importance 4.4/5
core task · importance 4.3/5
core task · importance 4.2/5
core task · importance 4/5
core task · importance 4/5
core task · importance 3.9/5
Evidence
Evidence trail
0 attributed item(s). Confidence reflects study design: randomised and production deployments score higher than platform observations, which score higher than projections.
No occupation-specific evidence has been mapped yet. This occupation is scored from generic task assessment only - a genuinely weaker basis, and the reason its score is marked low-confidence.
Historical trend
Exposure over time
Backcast series: recomputed at quarterly checkpoints using only the models and evidence published by each date.
No series yet
This chart fills in once the first snapshot is recorded.
Reconstruction, not measurement. 0 observed daily snapshot(s) exist for this occupation; the earlier part of the curve is derived by applying the current methodology to the model releases and published evidence available at each past date.
Recent events
Signals affecting this occupation
No events match this filter yet. New verified events appear here as analysis completes.
Related
Related Occupations
Occupations sharing major SOC group (13) with Insurance Underwriters.
faq
Frequently Asked Questions
Common questions regarding Insurance Underwriters and AI exposure.
Will AI replace Insurance Underwriters?
Current AI models show an exposure score of 50/100 for Insurance Underwriters. This measures technical capability to assist or automate specific tasks rather than predicted immediate job loss. Moderate exposure reflects assistance in routine information-handling while core responsibilities require human judgment.
Which tasks in Insurance Underwriters have the highest AI exposure?
The most exposed tasks for Insurance Underwriters include "Review company records to determine amount of insurance in force on single risk or group of closely related risks." (80% exposure) and "Write to field representatives, medical personnel, or others to obtain further information, quote rates, or explain com…" (78% exposure).
What aspects of Insurance Underwriters can AI not do yet?
Tasks with the lowest AI exposure for Insurance Underwriters include "Authorize reinsurance of policy when risk is high." (35% exposure) and "Decrease value of policy when risk is substandard and specify applicable endorsements or apply rating to ensure safe, p…" (35% exposure). These responsibilities typically require physical dexterity, licensed human accountability, or complex interpersonal navigation.
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Notice: All ratings shown are AI-estimated technical exposure scores measuring technical capabilities across O*NET task statements, not predicted job loss. Contains information from O*NET 31.0 by the U.S. Department of Labor, ETA, used under CC BY 4.0.