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The AI Advantage: Solving 6 Challenges in Workers' Comp Underwriting

P&C INSIGHTS BLOG | March 20, 2025

The Gradient AI Team

The AI Advantage: Solving 6 Challenges in Workers' Comp Underwriting

Workers' compensation underwriting can be a balancing act — insurers must accurately assess risk, ensure competitive pricing, and maintain profitability while keeping policies accessible for businesses of all sizes. Traditional underwriting processes, often reliant on manual data entry and outdated models, can struggle to keep pace with evolving workplace risks, regulatory changes, and the vast amount of data available today.

Artificial intelligence models are changing the workers’ comp underwriting paradigm by taking on these challenges directly. AI-based solutions allow insurers to increase productivity, make better judgment calls regarding risk, and improve decision-making for the benefit of both the carrier and the policyholder. Work comp underwriting powered by AI gives better outcomes through:

Enhanced risk assessment – AI models analyze vast datasets, identifying patterns that underwriters may overlook, assigning a risk score that provides a more precise risk assessment.

Automated data processing – AI streamlines policy underwriting by extracting insights from unstructured data sources, such as claims history, safety records, and payroll data.

Operational efficiency gains – Automating routine underwriting tasks allows underwriters to focus on high-value decision-making, reducing turnaround times.

In this blog post, we’ll explore how AI is reshaping workers' compensation underwriting, addressing 6 critical challenges, and unlocking new opportunities for insurers to drive profitability while delivering better coverage.

6 Challenges Currently Facing Workers’ Comp Insurers and How AI Addresses these Challenges

Do the challenges below sound familiar? Gradient AI’s Work Comp clients tell us that these are the biggest challenges they face today.

These issues certainly underscore the need for innovative solutions, like AI-driven analytics, to mitigate their impact, and ultimately to enhance underwriting accuracy and efficiency in Workers' Comp.

1. Shifting workforce demographics

An aging workforce may lead to longer recovery times and higher claim severity. Additionally, hybrid and remote work introduce complexities in determining whether an injury occurred during employment.¹

How AI Helps
AI-powered predictive analytics can assess demographic trends and their impact on risk. Predictive modeling can estimate injury probabilities based on job roles, age distribution, and remote work environments. Underwriters can use AI-driven risk scoring to adjust pricing models dynamically, ensuring that policies accurately reflect emerging workforce risks.

2. Medical inflation

Medical inflation refers to rising costs for medical resources (e.g., physician services, health care facilities and supplies, pharmaceuticals, etc.). These costs comprise the largest share (60%) of workers’ comp expenses, according to the National Library of Medicine. The Centers for Medicare and Medicaid predict healthcare spending will rise by 5.4% each year through 2028, presenting ongoing medical inflation concerns.²

How AI Helps
AI-driven cost projection models integrate historical claims data, regional medical inflation rates, and provider pricing trends to enhance pricing accuracy. Machine learning algorithms can help underwriters assess potential cost escalation and adjust premium structures accordingly. AI also improves medical provider benchmarking, allowing insurers to tailor underwriting decisions based on provider efficiency and cost-effectiveness.

3. Catastrophic claims and litigation

Catastrophic claims that cost more than $2 million represent less than 0.1% of workers’ compensation claims but account for up to 2% of all costs to insurers, according to a report by the National Council on Compensation Insurance.³ Related to this, in the Healthe Systems 2025 Workers’ Comp Industry Insights Survey, the insurance professionals surveyed listed Workers’ Comp litigation as the number one challenge in the industry right now.⁴ How does all of this impact underwriting? High-cost claims add complexity to the underwriting process, as insurers must carefully assess and price the risk of such claims occurring.⁵

How AI Helps:
AI models analyze historical claims data to identify early indicators of potential catastrophic claims, enabling underwriters to refine risk selection and adjust pricing strategies. Predictive analytics can assess litigation probability by evaluating claimant characteristics, case histories, and legal trends. This allows underwriters to price policies with greater precision and apply exclusions or risk-adjusted premiums where necessary.

4. Pricing pressure

The rise in catastrophic claims and out-sized litigation is creating upward pressure on premiums. However, underwriters must balance this with competitive market forces. The workers' compensation market has seen years of declining rates due to fierce competition, making it challenging to adjust prices adequately to reflect increased risk.⁶

How AI Helps
AI-enhanced risk modeling helps insurers refine their pricing strategies by incorporating real-time industry data, economic trends, and external risk factors into underwriting decisions, to adjust rates in response to evolving risks while maintaining market competitiveness. AI-powered market intelligence tools provide insights into competitor pricing, helping underwriters fine-tune their pricing models for optimal profitability.

5. Reserve strength and profitability concerns

The increase in catastrophic claims is also contributing to a gradual decline in reserve strength for insurers. This trend, coupled with medical and wage inflation, is putting pressure on underwriting profitability.⁷

How AI Helps
AI-powered actuarial models improve reserve forecasting by analyzing claims trends, economic conditions, and real-time portfolio risk. Insurers can leverage AI-driven simulations to predict future claims volatility, enabling better financial planning and reserve allocation. AI also enhances underwriting profitability analysis by pinpointing high-risk policy segments and optimizing risk-adjusted pricing.

6. Need for increased operational and technical efficiency

Legacy systems are often ill-equipped to handle the volume and complexities of modern claims data, leading to inefficiencies, delays, and increased administrative costs.⁸

How AI Helps
AI-driven automation enhances underwriting efficiency by streamlining data ingestion, risk analysis, and decision-making. NLP tools extract insights from vast amounts of structured and unstructured data, improving the speed and accuracy of underwriting assessments. AI can provide underwriters with real-time recommendations, ensuring consistent and data-driven risk evaluation.

Conclusion: AI is Transforming Workers' Comp Underwriting by Enhancing Risk Assessment, Improving Pricing Accuracy, and Driving Operational Efficiency

As insurers face growing challenges — from medical inflation to shifting workforce trends — AI provides a strategic advantage to navigate uncertainty and maintain profitability. By leveraging AI-powered solutions, underwriters can make more informed decisions, optimize pricing strategies, and position their organizations for long-term success in an increasingly complex market.