Catastrophe Model Output

There are no catastrophe model outputs. The underwriter assesses natural disaster exposure by looking at a map and guessing. Accumulation risk is unknown — nobody can quantify how much exposure the carrier has in a hurricane-prone county or earthquake zone.

None — AI cannot assess catastrophe risk because no modeled loss estimates exist.

Catastrophe model runs happen annually at the portfolio level. The actuary sends a geocoded exposure file to the cat modeling vendor, waits weeks for results, and receives a summary report with aggregate probable maximum loss estimates. Individual account-level cat scores do not exist — the model produces portfolio-level curves only. Underwriters have no cat model output to reference when evaluating individual submissions.

AI can reference portfolio-level cat model aggregates but cannot assess individual account catastrophe risk because account-level cat model outputs do not exist.

Cat model outputs exist at the individual account level. Each property location has a modeled probable maximum loss, average annual loss, and peril-specific exposure scores. The underwriter can see 'this building has a 250-year PML of $3.2M for hurricane and $800K for earthquake.' But the outputs are static — generated during the annual model run and not refreshed between cycles. Outputs are stored as flat files not linked to the underwriting workbench.

AI can incorporate individual account cat scores into risk assessment. Cannot perform real-time accumulation analysis or scenario modeling because the outputs are static annual snapshots.

Cat model outputs are structured records integrated into the underwriting workbench. Each property location has modeled PML, AAL, and loss exceedance curves linked to the application record. On-demand re-scoring allows underwriters to model 'what if' scenarios (adding a location, changing coverage limits). An underwriting manager can query 'show me all accounts where the hurricane PML exceeds our per-risk tolerance for this territory' and get an instant answer.

AI can perform automated cat risk assessment — incorporating modeled loss estimates into underwriting decisions, running accumulation checks against cat model output, and flagging accounts that exceed risk tolerance thresholds.

Cat model outputs are schema-driven with formal entity relationships. Modeled losses link to peril definitions, vulnerability functions, exposure data, and secondary uncertainty parameters. An AI agent can query 'what is the marginal impact on our Florida hurricane PML if we bind this $10M coastal account, considering our current portfolio composition and the secondary uncertainty range?' and get a computed, multi-scenario answer.

AI can perform fully autonomous catastrophe risk management — portfolio optimization, marginal impact analysis, and reinsurance treaty evaluation using the complete cat model output structure.

Cat model outputs update continuously. New policy bindings, cancellations, and endorsements trigger real-time portfolio remodeling. Model vendor updates propagate immediately. Live weather event tracking produces real-time loss estimates as events unfold. The cat model output is a living, breathing view of catastrophe risk that evolves continuously.

Fully autonomous catastrophe risk management. AI maintains continuously current cat risk positions and responds in real-time to portfolio changes and emerging events.

Ceiling of the CMC framework for this dimension.