Property and Casualty Underwriters analyze insurance applications for homes, vehicles, and commercial properties to determine whether to accept the risk and at what price. They review data such as credit history, property inspections, claims history, and geographic risk factors like flood or wildfire zones to calculate premiums that balance competitiveness with profitability. Underwriters work closely with agents and brokers, often negotiating terms and coverage limits, and rely increasingly on predictive analytics and automated underwriting software to speed decision-making.
| Entry level | $52,000 |
| Median | $78,000 |
| Senior | $105,000 |
| Top 10% | $140,000 |
| Job growth | +3% |
| Professionals in the USA | 0.1 million |
| Typical hours/week | 40 hrs |
| Remote work share | 55% |
| Annual job openings | 13,900/yr |
| Demand | Moderate |
AI and machine learning models are increasingly capable of analyzing risk data, pricing policies, and flagging applications for standard coverage, automating much of the routine underwriting workflow. Insurers are deploying algorithmic underwriting for simple and mid-tier risks, shrinking demand for manual review of straightforward cases. Complex, high-value, or novel risks still require human judgment, but overall headcount growth is projected to slow.
Automation exposure: Data collection, risk scoring, policy pricing calculations, application triage, and routine approval/denial decisions for standard personal and small commercial lines are highly automatable using predictive analytics and AI underwriting engines.
The human edge: Underwriters bring contextual judgment for unusual or emerging risks, negotiation skills with brokers and agents, ethical decision-making in gray-area cases, and relationship management that builds trust with clients and producers—areas where AI still struggles.
Figures are estimates for exploration — verify current data with BLS.gov.