Actuary
Catastrophe Modeling
What You Do Today
Run cat models (RMS, AIR, CoreLogic) to estimate exposure to hurricanes, earthquakes, wildfires, and cyber events. You're interpreting model output, adjusting for your specific portfolio, and presenting results to leadership and reinsurers.
AI That Applies
AI-enhanced catastrophe models that incorporate real-time data — satellite imagery, IoT sensor data, climate projections — into exposure estimates. ML that calibrates model parameters to your specific loss experience.
Technologies
How It Works
For catastrophe modeling, the system draws on the relevant operational data and applies the appropriate analytical models. Machine learning models identify the patterns in historical data that most strongly predict the target outcome, then apply those patterns to score new inputs. The results integrate into the practitioner's existing workflow — presenting recommendations, flags, or automated outputs alongside their normal working context. The interpretation and business decision.
What Changes
Cat models update dynamically instead of annually. The AI integrates real-time wildfire satellite data with your exposure database to give you a loss estimate during the event, not after.
What Stays
The interpretation and business decision. The model says the 250-year PML is $500M — but whether to buy that much reinsurance is a business judgment about risk appetite, capital, and market conditions.
What To Do Next
This section won't tell you what your numbers should be. It will show you how to find them yourself. Every instruction below produces a real, verifiable result in your organization. No benchmarks, no projections — just the steps to build your own evidence.
Establish Your Baseline
Know where you are before you move
Before adopting AI tools for catastrophe modeling, understand your current state.
Without a baseline, you can't measure whether AI actually improved anything. You'll adopt tools without knowing if they're working.
Define Your Measures
What to track and how to calculate it
Time per cycle
How to calculate
Measure how long catastrophe modeling takes end-to-end today, then after AI adoption.
Why it matters
The most visible improvement is speed. If AI doesn't save time, question whether it's adding value.
Quality of output
How to calculate
Track error rates, rework frequency, or stakeholder satisfaction scores before and after.
Why it matters
Speed without quality is just faster mistakes. Measure both.
Start These Conversations
Who to talk to and what to ask
your chief actuary
“What data do we already have that could improve how we handle catastrophe modeling?”
They set the standards for model validation and governance
your data science or analytics lead
“Who on our team has the deepest experience with catastrophe modeling, and what tools are they already using?”
They build complementary models and share the same data infrastructure
your regulatory filing lead
“If we brought in AI tools for catastrophe modeling, what would we measure before and after to know it actually helped?”
AI-assisted rate filings need to meet regulatory standards
Check Your Prerequisites
Confirm readiness before you invest
Check items as you confirm them.