Generative AI is Changing Claims and Underwriting: Insights from Stan Smith, CEO of Gradient AI

The insurance industry has always about managing risk, predicting outcomes, and providing a safety net for individuals and businesses. But today, insurers face new challenges: rising customer expectations for personalized services, increasing complexity in global risk factors, and a pressing need for innovation. Generative AI offers transformative potential in addressing these challenges, particularly in claims management and underwriting.

In a recent interview on the Analytics Insight podcast, host Priya Dialani sat down with Stan Smith, CEO and founder of Gradient AI, to discuss how Gen AI is empowering insurers to enhance efficiency, improve profitability, and deliver superior customer experiences.

Stan shared his insights in these 4 key areas:

1.   Claims management

2.   Underwriting

3.   Workforce challenges

4.   Gen AI implementation

1. Rethinking Claims Management with Gen AI

Claims management is one of the most resource-intensive functions in insurance. Customers demand swift, fair processing, while insurers must mitigate costs, detect fraud, and ensure regulatory compliance. Balancing these demands often leads to inefficiencies and delays.

Stan’s Insights

From Gradient AI’s early days, clients posed a common question: “Can you help us identify creeping catastrophic claims?” These are claims that initially appear minor but escalate into significant losses.

Stan shared some important dynamics about insurance claims:

• A small percentage of claims drive high costs: Less than 10% of claims can account for over 60-70% of total losses in areas such as workers’ compensation.
• AI models can identify high-risk claims: AI models excel at flagging high-risk claims early, empowering insurers to allocate experienced adjusters where they’re needed most.

However, early identification alone isn’t enough. Clients would then ask, “Now what?” And this is where Gen AI steps in. Beyond predicting risks, it provides actionable recommendations — the "next best action" — tailored to each claim’s specifics. For example:

• Suggesting targeted interventions or treatments to reduce claim costs and duration.
• Offering clear ROI arguments for decisions, such as engaging third-party vendors for high-cost cases.
• Streamlining low risk claims for expedited settlement, improving customer satisfaction.

2. Transforming Underwriting with Gen AI

Underwriting, especially in group health insurance, often relies on analyzing conditions and treatments without adequate context. Is a condition newly diagnosed, actively treated, or in remission? Without this clarity, underwriters might overestimate risks and decline viable groups.

Stan’s Insights

Stan shared that Gen AI bridges these gaps in the traditional approach by:

• Providing contextual insights: For example, a condition and drug combination might signal a $400,000 annual cost. Gen AI contextualizes this, leveraging additional data points to reveal that the patient is in remission, reducing the estimated cost to $10,000 annually.
• Enhancing efficiency: Automating the analysis of complex datasets allows underwriters to make informed decisions faster, reducing reliance on manual reviews by medical experts.

These advancements enable insurers to price policies accurately, mitigate risks, and confidently pursue opportunities previously deemed too uncertain.

3. Addressing Insurance Workforce Challenges

There’s a “perfect storm” happening in the insurance workforce: experienced professionals are retiring, remote work may be hindering mentorship, and there is often high attrition among new hires.

Stan’s Insights
Gen AI can serve as a co-pilot to address these challenges, offering:

• Mentorship for adjusters: By identifying potential actions and providing rationale, Gen AI acts as an on-demand mentor, enhancing decision-making for both seasoned and novice adjusters.
• Insights for underwriters: Gen AI suggests pricing adjustments based on nuanced factors like regional crime rates or industry-specific risks, supporting more accurate underwriting.

4. Key Success Factors for Implementing Gen AI in Insurance Organizations

Change can be difficult in any organization. In order to maximize the ROI of new technology investments, insurers should target operational areas with significant impact on combined ratios, such as claims payouts and underwriting accuracy, as well as leverage historical data to train AI models for better predictions.

Stan’s Insights

For best chance at success, Stan emphasized the importance of:

• Data quality: Inaccurate or incomplete data undermines AI effectiveness.
• Transparency: Providing clear reasoning for AI-driven recommendations builds trust with users and regulators.
• Proactive regulation compliance: Engage with regulators early to address concerns around data use, bias, and security.

Conclusion: Future Trends in Gen AI

Stan predicts increased adoption of Gen AI across the insurance industry to:

• Automate mundane tasks: For example, summarizing documents and streamlining workflows; such automation frees up teams to focus on more strategic activities.
• Enhance customer interactions: Continuously improve recommendations by learning from customer interactions.
• Support dynamic decision-making: Empower insurers to adapt to evolving risks and market conditions in real time.

Generative AI is not just a technological advancement—it’s a game-changer for the insurance industry. And as Stan Smith underscores, the key to success lies in focusing on high-impact solutions, maintaining transparency, and embracing regulatory collaboration. The future of insurance is here, and with Gen AI, it’s brighter than ever.

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