Practical Cat Model Workflow

The Probabilistic Framework

While the details vary between organisations and software platforms, all commercial catastrophe modelling workflows follow the same underlying probabilistic framework. At its core, the model simulates a very large number of potential events — typically representing 10,000 to 100,000 years of synthetic history — and estimates the loss to a portfolio for each event. The results are combined to produce a probability distribution of losses.

The Standard Cat Model Workflow

Step 1 — Exposure Data Collection and Quality Review

The workflow begins with collecting exposure data from the client or underwriter — property locations, construction types, occupancies, values, and policy terms. The first task is a thorough data quality review: identifying missing geocodes, unknown construction types, unreasonable values, or incomplete policy information. Data quality issues are documented and, where possible, corrected or supplemented using industry databases and assumptions.

Step 2 — Geocoding and Schema Mapping

Properties must be geocoded to geographic coordinates that the model can use. The exposure data must also be mapped to the model's schema — its classification system for construction types, occupancies, and coverage types. This mapping process requires judgment: a "brick veneer" house in the client's data must be correctly classified into the model's masonry or wood-frame categories based on how it is structurally behaving.

Step 3 — Model Execution

The geocoded, mapped exposure is run through the cat model — sequentially through the hazard, vulnerability, and financial modules — for each event in the stochastic catalog. This produces a loss estimate for each of the tens of thousands of simulated events. Modern cat models can process large portfolios in minutes to hours on standard hardware.

Step 4 — Results Review and Validation

Raw model output must be reviewed before being communicated to stakeholders. Key checks include: comparing results to prior model runs or benchmarks, assessing whether the loss distribution shape looks reasonable, checking for outliers or unexpected concentrations of loss, and comparing outputs across multiple models if available. Unexplained changes from prior results must be investigated.

Step 5 — Reporting and Communication

The final step is distilling the model output into a form useful to decision-makers. This typically means producing EP curves, AAL summaries, PML metrics at specified return periods, geographic loss maps, and comparisons across perils and business lines. Communicating uncertainty — including the range of results across models and the sensitivity to key assumptions — is as important as the point estimates themselves.

Key Metrics in a Standard Cat Report

• Annual Average Loss (AAL) and loss cost
• OEP and AEP curves — probability distributions of occurrence and aggregate annual losses
• Return period losses — 1-in-10, 1-in-50, 1-in-100, 1-in-200, 1-in-250 year
• Top events — the simulated events generating the largest losses to the portfolio
• Geographic concentration analysis — where is risk concentrated?
• Multi-model comparison — how do results vary across vendor models?

The Human Element

It is important to emphasise that running a cat model is not a mechanical process. Every step requires expert judgment: assessing data quality, making schema mapping decisions, interpreting unusual results, and communicating uncertainty to non-technical audiences. The most valuable cat modellers are those who combine technical proficiency with the ability to explain complex probabilistic concepts clearly to underwriters, executives, and regulators.