---
title: "Will AI Replace Insurance Underwriters? (2026)"
description: "BLS rates insurance underwriters' exposure to AI as very high. What that means, which parts of the work AI touches, and the 2025–2035 outlook (-3.8%…"
canonical: "https://nuecareer.com/careers/will-ai-replace-insurance-underwriters"
last-updated: "2026-10-15T08:19:32.066Z"
---

# Will AI Replace Insurance Underwriters? (2026)

**Short answer:** This is one of the most exposed jobs, and BLS projects it to shrink. Exposure is not a forecast of job loss on its own, but here the two point the same way.

The Bureau of Labor Statistics places insurance underwriters in its very high AI exposure category, among the occupations most exposed to current AI. BLS projects employment to fall by 3.8% over 2025–2035, against 3.5% for all occupations, with about 6,800 openings a year.

| Measure | Value | Source |
| --- | --- | --- |
| BLS relative AI exposure | Very high | BLS Employment Projections, AI exposure categories |
| NueCareer AI-resilience score | 12/100 | NueCareer analysis of O\*NET 31.0 Database |
| Projected employment change | -3.8% | BLS EP 2025–2035 |
| Openings a year | 6,800 | BLS EP 2025–2035 |
| Median annual wage | $81,370 | BLS OEWS May 2025 |

## Exposure categories

| BLS category | Occupations |
| --- | --- |
| Low | 203 |
| Moderate | 194 |
| High | 190 |
| Very high (this job) | 199 |

Exposure means AI could be or has been used to assist with or complete some of the work. BLS says it does not imply job loss, productivity gains, automation probability or wage effects. Method: https://www.bls.gov/emp/publications/ai-exposure-categories.htm

## What holds up

- Face-to-face discussions: 4.4/5 (O\*NET work context)
- Establishing and maintaining interpersonal relationships: 4.3/5 (O\*NET work activity importance)
- Resolving conflicts and negotiating with others: 3.7/5 (O\*NET work activity importance)
- Making decisions and solving problems: 4.6/5 (O\*NET work activity importance)

## What AI reaches first

- Processing information: 4.1/5 (O\*NET work activity importance)
- Time spent sitting: 4.9/5 (O\*NET work context)
- Getting information: 4.8/5 (O\*NET work activity importance)
- Analyzing data or information: 4.4/5 (O\*NET work activity importance)

## O\*NET tasks

- Examine documents to determine degree of risk from factors such as applicant health, financial standing and value, and condition of property.
- Decline excessive risks.
- Write to field representatives, medical personnel, or others to obtain further information, quote rates, or explain company underwriting policies.
- Decrease value of policy when risk is substandard and specify applicable endorsements or apply rating to ensure safe, profitable distribution of risks, using reference materials.
- Evaluate possibility of losses due to catastrophe or excessive insurance.
- Review company records to determine amount of insurance in force on single risk or group of closely related risks.

## Related careers less exposed to AI than insurance underwriters

- [Insurance Appraiser, Auto Damage](https://nuecareer.com/careers/insurance-appraiser-auto-damage): BLS exposure High, median $78,240
- [Fundraiser](https://nuecareer.com/careers/fundraiser): BLS exposure High, median $72,550; [Will AI replace fundraisers?](https://nuecareer.com/careers/will-ai-replace-fundraisers)
- [Labor Relations Specialist](https://nuecareer.com/careers/labor-relations-specialist): BLS exposure High, median $95,420

## FAQ

**Will AI replace insurance underwriters?**

This is one of the most exposed jobs, and BLS projects it to shrink. Exposure is not a forecast of job loss on its own, but here the two point the same way. BLS defines exposure as AI being able to assist with or complete some of the work, and says it does not imply job loss, productivity gains or wage effects. BLS projects employment to fall by 3.8% over 2025–2035, against 3.5% for all occupations, with about 6,800 openings a year.

**How exposed are insurance underwriters to AI?**

BLS sorts 786 occupations into four relative categories: low, moderate, high and very high. Insurance Underwriters are in the very high category, alongside 198 other occupations. The categories combine five public measures of where AI could be or already is used, and they compare occupations with each other rather than giving an absolute risk.

**What parts of an insurance underwriter's job can AI do?**

In O\*NET's description of the work, the parts that overlap most with what AI handles well are processing information, time spent sitting and getting information. The parts that hold up best are face-to-face discussions, establishing and maintaining interpersonal relationships and resolving conflicts and negotiating with others. These come from O\*NET ratings of how important each activity is to the job, read through NueCareer's AI-resilience method.

**Is being an insurance underwriter a safe career from AI?**

On NueCareer's AI-resilience score, built from O\*NET work activities, insurance underwriters score 12 out of 100, higher than 4% of the occupations we score. The job pays a median of $81,370 a year (BLS OEWS, May 2025). No job is fully safe from technological change, but the federal exposure category and the projected openings are the two numbers worth weighing.

**What careers similar to insurance underwriters are less exposed to AI?**

Insurance Appraiser, Auto Damage (high exposure, $78,240 median), Fundraiser (high exposure, $72,550 median), Labor Relations Specialist (high exposure, $95,420 median). Each is in a lower BLS exposure category than insurance underwriters, and most are listed by O\*NET as related to the work.

Full profile: https://nuecareer.com/careers/insurance-underwriter.

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This bank includes information from the O\*NET 31.0 Database by the U.S. Department of Labor, Employment and Training Administration (USDOL/ETA). Used under the CC BY 4.0 license (https://creativecommons.org/licenses/by/4.0/). O\*NET® is a trademark of USDOL/ETA. NueCareer has transformed the data by joining it to BLS wage and projection data and deriving facet, personality-fit and AI-resilience scores. BLS and BEA data are public domain (17 U.S.C. §105).

O\*NET content is used under [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/). How these figures are sourced and derived: https://nuecareer.com/careers/methodology