Insurance team's work during natural disaster events Role play scenario how Insurance team's work

Scenario: Underwriting Natural Disaster Risk in Hurricane-Prone Regions

Key Players:

1. Sophia (Underwriting Manager): Oversees policy design and premium determination.

2. Ethan (Actuarial Analyst): Specializes in catastrophe modeling and predictive analytics.

3. Amara (Risk Assessment Lead): Focuses on environmental risk and regional vulnerability analysis.

4. Liam (Claims Analyst): Provides historical claims data and disaster impact insights.

5. Maya (GenAI Specialist): Leverages AI-driven tools for data processing and scenario simulations.

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Step 1: Initial Meeting

Sophia:

"Team, we’re planning to underwrite policies in a hurricane-prone region. I need a detailed analysis of potential risks and pricing recommendations."

Ethan:

"I’ll gather data on past hurricanes and run catastrophe models to estimate potential losses."

Amara:

"I’ll analyze the geographical and environmental factors contributing to the region's risk."

Maya:

"I’ll set up AI-driven tools to process satellite imagery, weather data, and social media feeds to highlight risk hotspots."

Liam:

"I’ll provide a breakdown of historical claims data, focusing on damage patterns and recovery costs."

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Step 2: Data Analysis

Ethan:

"Using CAT models, I estimate a 25% probability of Category 4 hurricanes in this area over the next decade. Loss projections for such events are $500M on average."

Amara:

"My analysis shows that properties within 5 miles of the coastline are 70% more likely to sustain severe damage due to wind and flooding."

Maya:

"AI simulations confirm Ethan’s findings and identify critical risk zones. I also found that social media activity during past hurricanes highlights high flood risks in poorly drained areas."

Liam:

"In the past three hurricanes, 80% of claims involved roof damage and basement flooding. Average claim payouts were $25,000 per property."

Sophia:

"This is excellent data. Let’s use these findings to structure our policies."

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Step 3: Policy Design

Sophia:

"Based on Ethan’s loss projections, we’ll price premiums at $1,500 annually for low-risk zones and $3,000 for high-risk zones."

Ethan:

"That aligns with our predictive models. I recommend a 10% surcharge for properties within the highest-risk zones."

Amara:

"We should also consider exclusions for properties with outdated building codes."

Maya:

"I can use AI tools to flag properties in high-risk zones that don’t meet current safety standards."

Liam:

"We might also consider including higher deductibles for hurricane-related claims to reduce risk exposure."

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Step 4: Real-time Collaboration During a Hurricane

Sophia:

"Hurricane Isabel is approaching. How are we handling it?"

Maya:

"Our AI tools show a 90% probability of high-impact flooding in Zone C. We’ve alerted the claims team to prepare for increased activity there."

Amara:

"Emergency data confirms that Zone C has inadequate drainage. We need to issue warnings to policyholders now."

Sophia:

"I’ll authorize temporary policy adjustments for customers in Zone C, such as deferred premium payments."

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Step 5: Post-Event Debrief

Sophia:

"What are the key takeaways from Hurricane Isabel?"

Ethan:

"Actual losses were 10% higher than predicted, indicating that our model needs updating with this new data."

Amara:

"The properties that sustained the most damage were in areas flagged by our AI tools as high-risk."

Maya:

"AI simulations were accurate, but we need better integration of live weather data for faster predictions."

Liam:

"Claims are pouring in for roof and flood damage. We should consider adding mandatory flood insurance in high-risk zones."

Sophia:

"Great work, team. Let’s refine our models and policy structures for the next season."

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Conclusion:

This role-play showcases the collaboration between underwriting, actuarial, risk, and AI specialists, resulting in data-driven, actionable strategies to handle natural disaster risks effectively.