Top 10 Best Catastrophe Modeling Software of 2026

Fathom Global is designed for teams that run both deterministic scenario analysis and probabilistic catastrophe model studies, then publish outputs into an event-loss workflow. Exposure handling centers on location-level data preparation such as geocoding, data cleaning, and mapping into the model’s location and attribute requirements. The results center on event loss outputs that can feed financial model steps like aggregation and exceedance-oriented reporting. Automation is oriented around repeatable run configuration so model users can standardize per-peril and per-scenario execution.

A key tradeoff is that Fathom Global’s strongest value comes when teams accept its workflow conventions for exposure preparation and output structure rather than building fully custom pipelines. It fits best when an insurer or reinsurer needs consistent reruns across portfolios and scenarios, especially when correlation assumptions and per-peril configuration must stay controlled. It is less suitable for teams that require every output format to match legacy file layouts without transformation steps.

Governance is handled through controlled configuration of model runs and publishing, with audit-friendly traceability of which inputs produced which outputs. This reduces ambiguity during model maintenance cycles when exposure refreshes and assumption changes must be tied to specific output sets. The fit is strongest for model teams that need repeatable operations across multiple jurisdictions or business units.

Moody's RMS Intelligent Risk Platform is built around production-grade catastrophe modeling runs that generate event loss outputs and distribution results used in pricing, portfolio risk, and reinsurance loss analysis. The platform supports geospatial ingestion patterns that map exposure records to model grids and locations, then applies vulnerability and hazard relationships within the modeling workflow. Model execution control is oriented around repeatability, including run configuration governance and the ability to regenerate results when exposure or parameters change.

A tradeoff appears for teams that require heavy customization outside Moody's ecosystem, since deep changes to modeling logic typically depend on available model configurations rather than open-ended authoring. A common usage situation is portfolio quarterly risk refresh where exposure updates and selected peril assumptions must produce consistent event loss tables and summary risk metrics for treaty reporting and internal limits management.

KatRisk Modeling Platform fits teams that need to convert geocoded exposure records into modeled losses, then manage the chain from hazard and vulnerability definitions to event loss table outputs. The platform’s workflow emphasis supports recurring production cycles where model inputs change, and loss outputs must be regenerated with traceable configuration. Integration depth is strongest when exposures, mappings, and model settings are standardized for repeated runs, because the value depends on feeding consistent location data into the modeling process.

A tradeoff appears in governance-heavy environments where multiple model releases, custom mapping rules, or bespoke data transforms require careful configuration discipline. KatRisk works best when a single modeling process is executed often, such as quarterly portfolio modeling and per-event portfolio impact reporting, where throughput and consistency matter more than ad hoc experimentation.

Verisk Extreme Event Solutions delivers insurer-focused catastrophe modeling built around Verisk peril and event libraries and scenario generation workflows. It supports end-to-end modeling outputs that feed event loss tables and downstream financial model runs.

The product emphasizes integration with exposure and policy data pipelines through geospatial processing, model configuration, and repeatable batch execution. Extensibility shows up through configurable modeling jobs and model output handling suited for regular portfolio runs.

Oasis Loss Modelling Framework (oasislmf.org) generates probabilistic catastrophe loss outputs by orchestrating exposure, hazard, and vulnerability inputs through a defined workflow. It supports event loss table and aggregated portfolio metrics like annual average loss and exceedance probability outputs.

Oasis LMF also places strong emphasis on deterministic scenario runs and repeatable model configurations across runs. The framework’s core value is automation around loss calculation and output publishing for insurance and reinsurance analysis.

Aon Impact Forecasting is a catastrophe modeling solution used by insurers and risk teams to build and run probabilistic catastrophe models and deterministic scenario analyses across perils and geographies. Its core workflow focuses on preparing exposure and model inputs, executing event loss computations, and managing model outputs for downstream financial and portfolio views. The product is distinct for how it fits into insurer operating models through established integrations and controlled governance around model runs and result sets.

CLIMADA is an open-source catastrophe modeling codebase that focuses on hazard, exposure, vulnerability, and loss computation workflows that can be scripted end to end. It provides a Python-first modeling stack that generates event loss outputs from hazard event sets and exposure data mapped to geographies.

CLIMADA also includes utilities for model configuration, scenario generation, and validation-style checks around inputs and model outputs. For teams that need model transparency and repeatable automation, CLIMADA’s integration hinges on its code-level extensibility rather than a GUI-centric modeling environment.

RiskScape is catastrophe modeling software used to produce event and annual loss outputs for insurance and risk teams with New Zealand focused hazard and exposure workflows. It provides a geography driven workflow that links geocoded exposures to event loss tables and portfolio summaries for deterministic scenario analysis and probabilistic output.

The tool emphasizes configuration around modeled hazards, exposure types, and vulnerability functions rather than custom code for everyday runs. Governance and operations are handled through project structure that supports repeatable reruns and controlled model input management across teams.

Jupiter Intelligence is a catastrophe modeling workflow that turns hazard and risk inputs into insurer-ready outputs. It focuses on configurable model orchestration, including ingestion of geospatial and exposure inputs and generation of event loss outputs for downstream financial analysis.

Jupiter Intelligence also emphasizes repeatable runs for scenario work, with controls for model versioning and output management across perils and regions. The product is typically evaluated on how well its automation and integration surface fits into an insurer or reinsurance risk stack.

Verisk Touchstone Re is a catastrophe modeling solution that supports underwriting and reinsurance analytics with an integrated workflow from model inputs to model outputs. It is designed around Verisk’s hazard, vulnerability, and financial modeling components so users can produce event loss and summary metrics for exposure locations.

Automation is supported through model runs, repeatable configuration, and output packaging that downstream systems can ingest for reporting and portfolio actions. The product is also used in governance-heavy environments where model results must be traceable to specific inputs and assumptions.