3 Ways Insurance Underwriters Can Gain Insights from Generative AI

Generative AI (GenAI) is poised to revolutionize the insurance industry, offering underwriters unprecedented insights into risk controls, building and location details, and insured operations. By leveraging this technology, underwriters can identify more value in the submission process, make higher quality underwriting decisions, and achieve more accurate pricing with reduced premium leakage. In this post, we’ll delve into the opportunities, capabilities, and potential impact of GenAI in insurance underwriting.

1) Risk Control Insights: Zoning in on Material Data

Generative AI enhances risk control analysis by highlighting loss prevention measures and assessing their effectiveness in reducing loss potential. This is crucial for informed underwriting decisions, especially in addressing areas often overlooked in data gathering. Underwriters typically face high volumes of submissions from disparate sources, making it challenging to review each one thoroughly. GenAI steps in to analyze the completeness and quality of all submissions at scale, enabling a comprehensive comparison against similar risks, underwriting guidelines, and the current book of business.

What GenAI Can Do:

• Generate comprehensive risk narratives: Align risks with carriers’ appetites and current portfolios.
• Flag and source missing data: Identify and source any missing material data required for thorough risk assessment.
• Manage data lineage: Track and update data efficiently.
• Enrich submissions with third-party data: Integrate auxiliary sources like TPAs or publicly listed products/services related to the insured’s operations.
• Validate submission data: Use additional sources, such as geospatial data, to validate details like vegetation management or proximity to building and roof construction materials.

By synthesizing submission packages with third-party data, GenAI presents information in a meaningful, easy-to-consume way that aids decision-making. This results in faster, improved pricing and risk mitigation recommendations. It also reduces the lengthy back-and-forth between underwriters and brokers by providing instant, prioritized insights across the entire portfolio, allowing underwriters to address control gaps and deficiencies promptly. These improvements enable more risks to be written without excessive premiums, turning potential “no” into a confident “yes.”

2) Building & Location Details: Enhancing Risk Exposure Accuracy

Consider a restaurant chain with multiple properties in a CAT-prone region like Tampa, Florida. Accurate building detail insights can significantly enhance the underwriter’s ability to predict risk exposure. Tampa’s high-risk hazards, according to FEMA’s National Risk Index, include hurricanes, lightning, and tornadoes. An insurance carrier might initially classify a restaurant as medium risk due to a past safety inspection failure, lack of hurricane protection units, and a potential link between maintenance failures and loss events.

Mitigation Measures Included in the Submission:

• Mandatory hurricane training: All employees undergo hurricane preparedness training.
• Metal storm shutters: Windows are equipped with storm shutters.
• Secured outdoor items: Furniture, signage, and other loose items are secured to prevent damage in high winds.

These measures demonstrate the restaurant’s commitment to reducing risk, which GenAI can highlight to give underwriters a fuller picture of risk exposure. Building detail insights uncover what is being insured, while location detail insights provide context, such as proximity to fire stations or historical loss data. This allows underwriters to drill down for additional context and recommend effective risk mitigation actions, transforming how risk is assessed and managed.

3) Operations Insights: Informing Additional Risk Controls

GenAI synthesizes information from broker submissions, financial statements, and other sources to provide a detailed view of insured operations. This includes hazard grades, SIC codes, and loss history, giving underwriters immediate visibility into top loss-driving locations and overall exposure.

Example:

A restaurant chain might initially be rated as medium risk, but further analysis of its catering operations reveals additional hazards:

• High occupancy: Located in a busy shopping complex with a maximum occupancy of 1000 persons.
• Claims history: Multiple claims over the last decade with high average claim amounts, indicating potential for accidents, property damage, and liability issues.

Even with existing risk controls like OSHA training and hurricane/fire drills, GenAI might recommend further measures, such as specific risk controls for catering operations and additional fire safety measures. This detailed operational insight ensures accurate risk attribution and helps underwriters make more informed decisions.

Beyond Profitable Underwriting Decisions

The benefits of GenAI extend beyond more profitable underwriting decisions. These insights:

• Accelerate new underwriter training: Reduce the time needed for new underwriters to understand data, guidelines, and risk insights.
• Improve analytics and rating accuracy: Ensure complete, accurate submission data is used in CAT Models.
• Reduce churn between teams: Minimize the back-and-forth between actuary, pricing, and underwriting teams on risk information.

Generative AI provides insurance underwriters with powerful tools to enhance risk assessment, improve decision-making, and drive profitability. By leveraging GenAI, underwriters can navigate the complexities of modern risk landscapes with greater precision and confidence.

For more insights on how generative AI can transform your underwriting process, connect with DOXA today.