GITNUXSOFTWARE REVIEW
EconomicsTop 10 Best Catastrophe Risk Modeling Software of 2026
Compare catastrophe risk modeling software tools with use-case notes for risk teams, including KatRisk, Verisk Touchstone Re, and Fathom.
How we ranked these tools
Vendors set the topic, but our editors set the inclusion criteria and the evaluation framework before research starts.
Search agents gather the source set and the review record, including transcriptions of video reviews and podcasts.
Framework: Features 40% · Ease 30% · Value 30%
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KatRisk is the best pick for catastrophe modeling teams in insurance that need repeatable end-to-end runs because its run orchestration keeps inputs consistent from exposure mapping through layered financial loss aggregation, while Verisk Touchstone Re fits reinsurance teams thanks to versioned, reinsurance-ready layer loss modeling that supports controlled scenario production.
Editor’s top 3 picks
Three quick recommendations before you dive into the full comparison below — each one leads on a different dimension.
KatRisk
Integrated run orchestration that carries consistent inputs across hazard, loss computation, and financial layer aggregation.
Best for: Fits when modeling teams need repeatable end to end runs from exposure mapping to layered financial loss outputs.
Verisk Touchstone Re
Editor pickReinsurance-ready layer loss modeling driven by versioned model configuration for controlled scenario production.
Best for: Fits when reinsurance modeling teams need repeatable stochastic workflows and controlled model versioning for validation cycles.
Fathom
Editor pickRun orchestration via API ties exposure updates to deterministic model execution and repeatable output sets.
Best for: Fits when teams need recurring catastrophe runs with API automation and controlled access across underwriting and risk.
Comparison Table
KatRisk
vertical specialistSpecialized flood and wind storm surge catastrophe modeling for the insurance sector.
Integrated run orchestration that carries consistent inputs across hazard, loss computation, and financial layer aggregation.
KatRisk is built for teams that need schedule-driven model runs and consistent transformation from exposure records into event loss outputs for reporting. It supports geocoding workflows for location-level exposure, plus occupancy and construction related classification inputs that feed damage calculations. The environment also supports reinsurance layer and policy term modeling so that gross to net loss can be reproduced across layers and assumptions.
A tradeoff appears in governance and reproducibility controls during model change cycles, because organizations often need to define strict run management practices to avoid mixing draft inputs with production-ready runs. KatRisk fits when a mid-sized modeling group must iterate on vulnerability or financial assumptions while keeping hazard and exposure processing reproducible for internal review.
- +Single workflow connects exposure mapping to event loss outputs and financial results
- +Reinsurance layer and policy term handling supports gross to net loss reproduction
- +Run orchestration supports repeatable iterations across modeling assumptions
- +Location-level exposure geocoding supports classification-driven loss calculations
- –Strict run discipline is needed to prevent draft inputs entering production outputs
- –External data preparation effort can be high for complex exposure attributes
- –Advanced automation often depends on integrating KatRisk outputs into surrounding tooling
- –Scenario configuration depth can require specialist review to avoid modeling gaps
Cat modeling teams
Iterate vulnerability and financial assumptions
Faster assumption cycle time
Reinsurance analysts
Translate gross to net recoveries
Consistent layer reporting
Show 1 more scenario
Underwriting risk teams
Scenario analysis for portfolio exposure
Actionable scenario loss signals
Use geocoded location exposure and classification to produce deterministic scenario loss distributions.
Best for: Fits when modeling teams need repeatable end to end runs from exposure mapping to layered financial loss outputs.
Verisk Touchstone Re
enterpriseCatastrophe modeling platform for insurers and reinsurers to assess natural peril exposure.
Reinsurance-ready layer loss modeling driven by versioned model configuration for controlled scenario production.
Verisk Touchstone Re runs probabilistic catastrophe risk modeling workflows for reinsurance analysis using hazard, vulnerability, and financial modeling components that support event-level outputs. The solution connects exposure data preparation and peril modeling with scenario production, which helps teams generate loss distributions for reinsurance layers and rates.
It also supports model governance workflows around versioned modeling inputs and repeatable runs, which helps teams manage catastrophe model validation activities across use cases. Automation is driven through published integrations and configurable processing pipelines that reduce manual steps between data prep and reportable outputs.
- +End-to-end reinsurance-oriented workflow from peril modeling to layer loss outputs
- +Consistent run configuration supports repeatable scenario production across teams
- +Integration options reduce manual handoffs between exposure prep and modeling runs
- +Model governance features track versioned inputs for validation-oriented review cycles
- –Operational setup depends on data normalization and metadata standards
- –Custom workflow automation may require deeper platform expertise than simpler tools
Reinsurance pricing teams
Generate loss distributions for layers
Layer pricing inputs
Cat model validation groups
Reproduce model runs for audits
Consistent audit evidence
Show 1 more scenario
Portfolio risk analysts
Produce scenario outputs for renewals
Renewal scenario comparisons
Connects exposure preparation to scenario production for renewal planning with governed run outputs.
Best for: Fits when reinsurance modeling teams need repeatable stochastic workflows and controlled model versioning for validation cycles.
Fathom
vertical specialistGlobal flood hazard and catastrophe risk data for insurance, banking, and government.
Run orchestration via API ties exposure updates to deterministic model execution and repeatable output sets.
Fathom is a catastrophe modeling solution built for operational use, where exposure updates and model re-runs must stay consistent across teams and time. The workflow maps inputs through hazard intensity footprints, vulnerability-based damage ratios, and an event loss table into downstream financial outputs. Integration depth is driven by its API-driven run lifecycle and data interchange patterns for exposure and job orchestration. This design favors organizations that need recurring modeling batches rather than ad hoc analysis.
A tradeoff appears in how teams must align their exposure preparation and model configuration to Fathom's expected run structure to get predictable throughput. Fathom fits best when there is a steady cadence of submission changes, such as portfolio rebuilds before renewal cycles. It is also suitable when multiple stakeholders need the same computed outputs with controlled access, because RBAC and change traceability reduce review overhead.
- +API-driven job runs reduce manual rerun work
- +Repeatable scenario outputs support audit trails
- +Exposure ingestion supports location-level workflows
- +Controlled access reduces accidental model changes
- –Model configuration requires disciplined setup before automation
- –Some analysis steps depend on pre-shaped input files
- –Heavy modeling batches need careful run scheduling
Underwriting analytics teams
Renewal cycle portfolio re-rating
Faster, repeatable renewal reporting
Enterprise risk modeling
Model governance across stakeholders
Reduced model change disputes
Show 2 more scenarios
Reinsurance commutations teams
Layer-level loss extracts
Consistent layer loss views
Computed event loss outputs feed downstream financial processing for treaty layer views.
Data engineering teams
Pipeline-driven exposure updates
Fewer manual data handling steps
Integration patterns support automated ingestion so portfolio updates trigger modeled loss refresh jobs.
Best for: Fits when teams need recurring catastrophe runs with API automation and controlled access across underwriting and risk.
One Concern
enterpriseCatastrophe resilience and dynamic risk modeling for buildings and infrastructure networks.
Configuration and run history records that support model risk management review without rebuilding documentation per study.
One Concern delivers catastrophe risk modeling workflow software that focuses on turning external event and hazard inputs into usable results for planning and decisioning. The tool is built for probabilistic catastrophe model execution using structured exposure and hazard data, then produces event loss outputs tied to modeled damage and financial terms.
- +Automation-oriented workflows connect hazard inputs to event loss outputs
- +Model governance artifacts support review of configuration and run history
- +Consistent results generation for deterministic and stochastic workflows
- +Extensibility for adding organization-specific data preparation steps
- –Advanced setups depend on experienced modeling and data preparation
- –Exposure data standardization requires upfront mapping discipline
- –Financial modeling depth may lag specialized financial-only toolchains
- –Geocoding and location handling can add iteration cycles during onboarding
Risk planning teams
Convert hazards into planning losses
Actionable loss estimates
Model risk management
Maintain audit trails for configurations
Clear model governance
Show 1 more scenario
Insurance analytics teams
Map exposures to output formats
Reduced manual data prep
Automates connections from exposure feeds through scenario inputs into downstream reporting outputs.
Best for: Fits when teams need repeatable catastrophe modeling runs with governance artifacts and integration-ready outputs.
Oasis Loss Modelling Framework
open-sourceOpen-source catastrophe loss modeling platform supported by the insurance industry.
Configurable run orchestration that turns prepared model components into standardized intermediate and output artifacts.
Oasis Loss Modelling Framework is built to execute end-to-end modeling runs using a staged configuration that maps inputs to computation steps and outputs. The framework supports location-level exposure handling through external exposure preparation, then couples those exposures to hazard intensity footprints and vulnerability functions during run execution. Outputs include event loss tables that can be aggregated for portfolio views and distribution measures used in risk decision workflows. The project also provides tooling to manage run configuration artifacts so the same workflow can be repeated across model versions and datasets.
- +Config-driven workflow orchestration across hazard, vulnerability, and exposure inputs
- +Event loss table generation supports downstream aggregation and scenario comparisons
- +Batch execution suited for repeated reruns across model and data versions
- +Extensibility model components enable custom mappings into standard outputs
- –Operational maturity depends on disciplined configuration and data pipeline integration
- –Complex setups take time for teams without catastrophe data normalization experience
- –Model interpretation and analytics require additional downstream tooling for reporting
- –Debugging mismatched inputs can be time-consuming without strong run artifact tracking
Cat modeling engineering teams
Automate reruns for model updates
Faster, repeatable model publications
Reinsurance analytics groups
Generate event loss outputs
Consistent layer inputs
Show 2 more scenarios
Enterprise risk data teams
Integrate exposures into modeling runs
Reduced manual rework
Connects location-level exposure preparation with event loss execution to support portfolio aggregation.
Model validation program managers
Track outputs across versions
Cleaner model version comparisons
Runs the same modeling workflow against different component versions to compare loss distributions.
Best for: Fits when teams need repeatable catastrophe modeling batch runs with integration control.
Jupiter Intelligence
enterpriseClimate change risk modeling providing forward-looking peril projections for physical assets.
Configuration-driven loss calculation runs that combine deterministic scenario analysis and probabilistic outputs in one workflow.
Jupiter Intelligence provides catastrophe risk modeling workflows that center on integrating hazard, exposure, and financial logic into repeatable loss calculations. The tool is designed for building probabilistic catastrophe model outputs such as loss exceedance curves and summary risk metrics from location-level inputs and risk attributes.
- +Supports location-level exposure workflows tied to loss calculation runs
- +Produces probabilistic model outputs used for exceedance and summary reporting
- +Handles deterministic scenario analysis for fixed event set comparisons
- +Emphasizes configuration-driven execution and repeatable model runs
- –Automation depth is limited when complex approval and governance chains are required
- –Integration work is required to align external exposure data and financial terms formats
- –Scenario and portfolio scaling can require careful setup to avoid run bottlenecks
- –Model validation reporting needs additional process wiring for thorough model risk management
Portfolio underwriting teams
Compute exceedance curves from locations
Underwriting risk curves
Risk modelers
Run fixed scenarios for comparisons
Repeatable scenario deltas
Show 1 more scenario
Model governance reviewers
Review outputs for risk management
Controlled model outputs
Uses configuration-driven runs and output review workflows for model risk documentation.
Best for: Fits when mid-size risk teams need repeatable loss runs across hazard and exposure inputs with scenario testing.
Mitiga Solutions
vertical specialistNatural hazard and climate risk modeling platform for volcanic, seismic, and weather perils.
Run orchestration that preserves configured assumptions and produces traceable outputs across automated scenario executions.
Mitiga Solutions targets catastrophe risk modeling workflows that need repeated scenario runs and managed assumptions across projects. The product emphasizes integration of hazard, exposure, and vulnerability inputs into a calculation pipeline that can produce event-based and portfolio-level loss outputs for downstream financial analysis.
- +Repeatable run definitions reduce drift across iterative scenario studies
- +Integration workflow links hazard, exposure, and vulnerability inputs into one calculation path
- +API and automation options support programmatic job triggers and dataset refreshes
- +Governance-oriented controls help manage who can change modeling configurations
- –Modeling setup requires more configuration work than click-through scenario tools
- –Some advanced portfolio mapping steps take custom data preparation
- –Workflow visibility depends on how teams configure logging and run artifacts
- –Extensibility may need vendor involvement for niche file formats
Insurance program managers
Rerun scenarios with consistent assumptions
Stable scenario comparisons
Actuarial finance teams
Generate event and portfolio losses
Ready-to-use loss tables
Show 1 more scenario
Data engineering teams
Trigger modeling jobs through API
Automated batch processing
Uses an API surface to connect internal datasets and launch modeling jobs without spreadsheet steps.
Best for: Fits when teams need repeatable catastrophe runs with controlled assumptions and programmatic automation support.
Moody's RMS Intelligent Risk Platform
enterpriseCloud-based catastrophe risk management platform for the global insurance industry.
Managed model release operations that couple catastrophe run configuration, validation checks, and governed outputs for audit-ready lifecycle control.
Moody's RMS Intelligent Risk Platform focuses on end-to-end catastrophe modeling workflows that connect hazard, exposure, and financial loss calculations into one governed environment. It supports probabilistic catastrophe model outputs like occurrence exceedance probability and loss exceedance curves, plus deterministic scenario analysis for structured disaster planning.
- +Integrated hazard, exposure, and financial modules for consistent loss outputs
- +Policy and reinsurance layer handling supports gross-to-net calculations
- +Model uncertainty and validation-oriented operations reduce release friction
- +Automation-friendly job orchestration supports repeatable catastrophe runs
- –Workflow setup requires disciplined governance and standardized inputs
- –UI-driven configuration can feel slow for large exposure updates
- –Extensibility depends on RMS-grade interfaces and supported data flows
- –Debugging mismatched inputs often requires cross-team subject-matter support
Cat underwriting teams
Produce gross and net layer views
Layer-adjusted loss results
Disaster planning teams
Run deterministic scenarios for response plans
Fixed-event loss estimates
Show 1 more scenario
Model risk governance teams
Validate uncertainty across releases
Governed validation workflows
Provides configuration controls and validation-oriented operations to manage uncertainty across model releases.
Best for: Fits when enterprise risk teams need governed probabilistic and scenario outputs with strong model risk controls.
Karen Clark & Company RiskInsight
enterpriseCatastrophe loss modeling software providing open, transparent peril models for insurers.
Model risk management and publishing workflows that preserve traceability across probabilistic model revisions.
Karen Clark & Company RiskInsight focuses on building probabilistic catastrophe model outputs and turning them into decision-ready risk metrics, with workflow controls that support model governance and repeatable studies. The software is designed around hazard computation plus loss modeling across exposure detail, including location-level inputs, occupancy mapping, and construction attributes where available.
- +Workflow controls support repeatable catastrophe studies and revision comparisons
- +Supports location-level exposure inputs tied to occupancy and construction characteristics
- +Produces loss results compatible with exceedance and layer-oriented financial review
- +Model risk management workflows fit teams that run frequent model updates
- –Study setup often requires detailed input mapping and disciplined configuration
- –Extensibility and automation typically depend on existing data pipelines and internal standards
Reinsurance buyers
Translate outputs into exceedance metrics
Better program decisions
Exposure data owners
Run models with construction attributes
More granular loss modeling
Show 1 more scenario
Portfolio analysts
Rerun standardized studies and compare revisions
Clear revision comparisons
Publishes controlled recurring studies so standardized scenarios can be rerun and compared across model versions.
Best for: Fits when model risk governance and repeatable catastrophe study runs matter more than rapid ad hoc analysis.
EigenRisk EigenPrism
enterpriseReal-time catastrophe risk analytics platform for portfolio exposure management.
Integrated probabilistic and scenario run orchestration that standardizes configuration-to-loss output generation for iterative portfolio studies.
EigenRisk EigenPrism is a catastrophe risk modeling software used to run end-to-end probabilistic catastrophe model workflows for location-level portfolios. The product connects exposure inputs and hazard representations into loss calculations, then produces outputs like loss exceedance curves and return-period views for underwriting and risk review.
- +Workflow orchestration supports repeatable batch runs across exposures and model settings
- +Loss outputs include exceedance curve style reporting used in underwriting and risk committees
- +Strong configuration support for deterministic scenario and probabilistic model runs
- +Model validation support supports model uncertainty tracking and risk model governance
- –Advanced modeling requires setup discipline across hazard, exposure, and financial configuration
- –Custom data preparation steps can be required before exposure imports behave predictably
- –Iteration cycles can slow when large exposures require repeated re-runs for parameter changes
- –API automation depth can lag specialized integration stacks for external data systems
Underwriting risk reviewers
Review return periods for portfolios
Return-period risk visibility
Reinsurance analysts
Compare modeled outcomes across iterations
Consistent iteration tracking
Show 1 more scenario
Operations teams
Batch process exposures with automated jobs
Less manual batch work
Executes automated job runs for batch exposure processing and results publication.
Best for: Fits when mid-size modeling teams need repeatable catastrophe workflows with controlled configuration and batch execution.
Conclusion
After evaluating 10 catastrophe risk modeling software, KatRisk stands out as our overall top pick — it scored highest across our combined criteria of features, ease of use, and value, which is why it sits at #1 in the rankings above.
Use the comparison table and detailed reviews above to validate the fit against your own requirements before committing to a tool.
Frequently Asked Questions About Catastrophe Risk Modeling Software
How do Oasis Loss Modelling Framework and Fathom handle deterministic scenario analysis versus probabilistic event-set processing?
Which tools provide API automation that ties exposure updates to repeatable model execution?
When teams need reinsurance-ready layer loss modeling, how do Verisk Touchstone Re and One Concern differ in workflow emphasis?
What breaks if model versioning and configuration history are missing in a catastrophe model workflow?
How do KatRisk and EigenRisk EigenPrism support iterative changes without rebuilding downstream export pipelines?
How does SSO and RBAC typically show up across enterprise users, and which platforms include governed access controls?
Which integration approach reduces manual steps between hazard, exposure preparation, and reportable results?
Where does governance control fall short when choosing between Karen Clark & Company RiskInsight and One Concern?
What technical requirement is most likely to derail automation in Mitiga Solutions and Oasis Loss Modelling Framework pipelines?
Tools reviewed
Primary sources checked during evaluation.
Referenced in the comparison table and product reviews above.
How to Choose the Right Catastrophe Risk Modeling Software
We separate options by how they handle catastrophe-specific inputs and model outputs end-to-end, not by generic “platform” claims. Focus on data model and API automation depth, then check how model uncertainty reporting and validation artifacts show up in outputs.
Exposure and hazard input data model
Why it matters
A consistent exposure and hazard input data model reduces rework and prevents mismatched assumptions when you run scenario work. If the product forces you into awkward formats, results can silently drift between runs.
How to check
Ask for a sample import: an exposure file plus a hazard file, with two locations and different asset types. Watch whether it keeps units/identifiers consistent and whether it surfaces mapping issues before a run.
API automation for runs and exports
Why it matters
API automation matters because catastrophe risk work is iterative, with frequent re-runs and standardized exports for review. Deeper automation lets analysts plug into existing workflows without manual click paths.
How to check
On a demo, request a workflow that submits a run, then exports results for review in one repeatable call chain. Confirm whether runs, parameters, and outputs are addressable via the API and not only via the UI.
Validation artifacts and audit trail
Why it matters
Validation artifacts and an audit trail are how teams verify which model versions and assumptions produced a loss output. Without them, it’s hard to defend outcomes when stakeholders challenge assumptions or timeframes.
How to check
Request a sample report package for a completed run, including model version identifiers and configuration snapshots. Ask where the audit trail records who changed configuration, and whether it preserves prior states.
Model uncertainty and rerun behavior
Why it matters
Model uncertainty reporting affects whether your catastrophe outputs are decision-ready, especially when comparing scenarios. You also want to know how reruns behave when randomness or sampling is involved.
How to check
Run the same scenario twice in the sandbox and compare uncertainty/interval outputs for stability. Ask the product to show where uncertainty is represented in results and what toggles control it.
Event and loss output detail
Why it matters
Event and loss output detail determines whether you can trace results to drivers, not just consume a headline number. If outputs are too aggregated, analysts can’t debug scenario logic or explain changes across runs.
How to check
Ask for exports that include both event-level loss and aggregated summaries for a small test portfolio. Verify the report includes the inputs used for event loss construction and supports filters by scenario run.
Configuration control and RBAC
Why it matters
Configuration control and RBAC reduce accidental changes to assumptions during scenario production. When multiple analysts contribute, permissions and audit log clarity prevent conflicting edits from contaminating outputs.
How to check
In a demo, create two roles with different permissions and attempt to change a run configuration. Then check the audit log for the exact parameter changes and whether exports still reference the correct configuration.
Red flags
- Exports hide assumptions or version identifiers, making outputs hard to defend later
- API automation only reproduces the UI clicks, with limited control over parameters and exports
- Event-level loss output is missing or not traceable back to the scenario inputs
Fit by situation
- If you run scenarios weekly with repeated exports, prioritize API automation for runs and exports.
- If multiple analysts change assumptions before review, prioritize configuration control and RBAC.
- If you need to explain why a change happened between two scenarios, prioritize event and loss output detail.
Funding and competing interests
This study was co-funded by several of the vendors assessed in it. No single vendor funded it. Funders could propose the market studied.
They had no role in selecting vendors, setting or weighting criteria, analysing data or drawing conclusions, and they received the same factual-accuracy check as every other vendor assessed.
Market definition, inclusion rules and criteria weights were fixed by an editor before research began and did not change afterwards. Editorial process →
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