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3 Policyholder Benefits Offered by AI-Powered Strategies

Predictive modeling can offer insurers a competitive advantage, but the benefits are also passed down to the insured. We review how this technology is being leveraged to better serve policyholders.

January 28, 2025

For insurers, artificial intelligence (AI) models can quickly identify market changes through captured, collected, and analyzed forecasting data. AI can enhance fairness, efficiency, and customization for an insured, leading to better product offerings and faster claims resolutions.

“By augmenting our data and preparing for changing expectations, insurance carriers can offer tailored risk solutions that benefit insureds such as accident prevention technology,” said Bill Wilkins, Senior Vice President, Advanced Analytics and Practical Applications at Safety National. “From the claims perspective, this data can also quickly identify high-risk claims, enabling a faster diagnosis for an injured worker and a more effective treatment plan.”

These three AI benefits can improve an insured’s experience with their carrier, from workers’ compensation to auto and general liability and beyond.

1. More Accurately Identifying Risk Factors

Policy pricing and rating plans were once based purely on human belief and that decided what insurers used for rating factors. Now, with AI and data capture improvements, insurance carriers get a clear holistic view of an organization’s actual risks instead of relying on assumptions. Specific data like claims histories, weather patterns, and driver behaviors can help determine emerging risk factors. With more precise risk assessments, insurers can offer more accurate and competitive pricing.

2. Avoiding Discrimination in Risk Scoring

Many of the variables tied to loss for an insurer are weighted based on machine learning data, which can help prevent unfair bias in an insured’s risk assessment. For example, in an auto liability policy, gender is often a highly overrated variable in determining potential risk since it can be linked to multiple other factors like a driver’s occupation and radius of driving distance. The radius of driving distance is often the most critical component for determining auto risks, which is not tied to one specific gender. Insurers should be collecting enough data to not just digest these variables but also to understand their true risk, enabling them to adjust based on an insured’s changing risk environment.

3. Creating Tailored Risk Management Solutions

The best preventative solutions are developed using data to identify what areas of an organization’s operations would benefit most. Cameras can be used in a manufacturing plant to track near misses or in a healthcare system to monitor where accidents are more frequently occurring. Technological opportunities, like telematics, can track driver routes to see if specific locations create more exposure based on previously occurring accidents. For a policyholder, implementing these solutions should lead to lower premiums not only because fewer accidents are occurring but also because they are actively engaging in preventative measures.