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Emergency DisasterTop 10 Best Catastrophe Modelling Services of 2026
Compare the top Catastrophe Modelling Services providers with a ranked list of picks and key strengths. Explore options now.
How we ranked these tools
Core product claims cross-referenced against official documentation, changelogs, and independent technical reviews.
Analyzed video reviews and hundreds of written evaluations to capture real-world user experiences with each tool.
AI persona simulations modeled how different user types would experience each tool across common use cases and workflows.
Final rankings reviewed and approved by our editorial team with authority to override AI-generated scores based on domain expertise.
Score: Features 40% · Ease 30% · Value 30%
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Editor’s top 3 picks
Three quick recommendations before you dive into the full comparison below — each one leads on a different dimension.
Aon
End-to-end catastrophe modelling that feeds underwriting, capital, and risk management workflows
Built for insurers and reinsurers needing integrated catastrophe modelling and portfolio analytics.
KPMG
Editor pickEnd-to-end catastrophe modelling governance that links hazard assumptions to validated loss outputs
Built for insurers and public agencies needing governed catastrophe modelling for reporting.
AECOM
Editor pickScenario-driven loss estimation combining hazard, exposure, and vulnerability with engineering interpretation
Built for enterprise teams needing end-to-end catastrophe modelling with engineering-grade advisory inputs.
Related reading
Comparison Table
This comparison table maps catastrophe modelling service providers across capability areas, including hazard and risk analytics, modelling methodologies, data sources, and disaster risk advisory delivery. It highlights how firms such as Aon, KPMG, AECOM, ERM, and S&P Global Commodity Insights position their catastrophe and disaster risk advisory work for insurers, reinsurers, and critical infrastructure stakeholders. The table also supports side-by-side evaluation of engagement scope, technical outputs, and typical use cases for catastrophe decision-making.
Aon
enterprise_vendorOffers catastrophe and disaster risk modeling and advisory through its reinsurance and risk analytics teams for emergency disaster exposure.
End-to-end catastrophe modelling that feeds underwriting, capital, and risk management workflows
Aon stands out for delivering enterprise-grade catastrophe modelling backed by underwriting, risk consulting, and risk engineering services. Core capabilities include hazard and exposure modelling, portfolio analytics, and scenario and stress testing for insurance and reinsurance. The offering supports event-level outputs used for pricing, capital assessment, and coverage decisioning. Delivery typically integrates modelling results into broader risk management workflows rather than standalone modelling outputs.
- +Produces event-level catastrophe outputs tied to underwriting and portfolio analytics
- +Integrates modelling with risk consulting and risk engineering for actionable decisions
- +Supports scenario and stress testing for reinsurance and insurance portfolio planning
- –Enterprise delivery focus can add complexity for small teams
- –Modelling outcomes require careful governance to match policy and exposure assumptions
- –Consultative integration can extend timelines for purely technical model requests
Best for: Insurers and reinsurers needing integrated catastrophe modelling and portfolio analytics
More related reading
KPMG
enterprise_vendorProvides risk and catastrophe modeling advisory as part of its insurance and risk transformation engagements for large emergency-disaster programs.
End-to-end catastrophe modelling governance that links hazard assumptions to validated loss outputs
KPMG stands out for delivering catastrophe modelling work that integrates financial, operational, and risk governance needs across insurance and public-sector stakeholders. The firm supports end-to-end modelling services that connect hazard and vulnerability inputs to catastrophe loss outputs and scenario analytics. KPMG’s delivery emphasis on controls, documentation, and model validation aligns well with audit-ready reporting for catastrophe risk management. Engagements can extend into portfolio exposure analysis, model implementation oversight, and regulatory reporting support.
- +Strong model governance and documentation for audit-ready catastrophe risk outputs
- +Integrates catastrophe loss modelling with financial and operational risk reporting needs
- +Supports portfolio exposure analysis across complex data sources and structures
- +Experienced validation and challenge processes for hazard and vulnerability assumptions
- –Less suitable for quick, one-off modelling sprints without governance requirements
- –Delivery depth can require sustained data-quality and stakeholder availability
- –Scenarios focused on enterprise governance may limit rapid exploratory iteration
- –Implementation support may be slower when legacy systems lack structured data
Best for: Insurers and public agencies needing governed catastrophe modelling for reporting
AECOM
enterprise_vendorPerforms hazard, vulnerability, and risk assessment services that can be modeled to support emergency disaster preparedness and mitigation.
Scenario-driven loss estimation combining hazard, exposure, and vulnerability with engineering interpretation
AECOM stands out for delivering catastrophe modelling work alongside engineering and advisory services across the full disaster lifecycle. The provider supports risk quantification for natural hazards by linking hazard data, exposure, and vulnerability to estimate losses and impacts. It also integrates resilience planning inputs with scenario design, technical documentation, and stakeholder-ready reporting. This combination suits complex, multi-discipline projects that require both model credibility and implementable recommendations.
- +Integrates hazard, exposure, and vulnerability to quantify disaster losses
- +Delivers scenario-based analyses for planning, underwriting support, and resilience
- +Uses multidisciplinary engineering context to improve interpretation of model outputs
- +Produces documentation suited for technical review and governance processes
- –Projects often require strong client data governance and clear modelling assumptions
- –Standardised self-serve workflows are not the primary delivery style
- –Modelling timelines depend on data availability and validation scope
Best for: Enterprise teams needing end-to-end catastrophe modelling with engineering-grade advisory inputs
ERM
enterprise_vendorDelivers enterprise risk management and hazard risk modeling advisory that supports disaster risk governance and emergency planning.
Expert model validation and defensible assumption translation into risk governance deliverables
ERM stands out for delivering end-to-end catastrophe modelling and risk analytics across hazards, assets, and business decisions. The service capability covers model development, hazard and exposure assessments, and scenario analysis to support underwriting, portfolio risk, and resilience planning. ERM’s delivery approach emphasizes operational integration so outputs map to decision workflows such as capital, claims, and risk governance. The team also supports expert validation by translating modelling assumptions into defensible risk narratives.
- +End-to-end catastrophe modelling for hazard, exposure, and scenario analysis
- +Decision-focused outputs for underwriting, portfolio risk, and resilience planning
- +Defensible assumption documentation for model governance and validation
- +Operational integration of risk analytics into existing decision processes
- –Implementation scope can be heavy for teams lacking internal data ownership
- –Needs clear hazard and exposure definitions to avoid modelling rework
- –Stakeholder alignment is required for consistent assumptions across scenarios
Best for: Organizations needing catastrophe modelling integrated into underwriting and risk governance
S&P Global Commodity Insights catastrophe and disaster risk advisory (S&P Global Intelligence segment)
enterprise_vendorProvides risk and catastrophe-focused analytics and advisory services used to inform disaster risk assessments and emergency exposure planning.
Decision-focused catastrophe and disaster risk advisory blending loss modelling with commodity impact interpretation
S&P Global Commodity Insights catastrophe and disaster risk advisory stands out through its coupling of commodity market expertise with catastrophe modelling for disaster risk decision support. The service supports hazard, exposure, and vulnerability analysis tied to natural catastrophe events and loss estimation workflows. It provides advisory outputs that translate model results into risk insights for stakeholders tracking disaster impacts across portfolios and supply chains. Engagements are delivered by an S&P Global Intelligence segment team focused on practical risk interpretation rather than standalone modelling tools.
- +Commodity-aware disaster risk insights for exposure tied to economic and supply variables
- +Structured hazard, exposure, and vulnerability analysis aligned to loss estimation needs
- +Advisory outputs emphasize decision-ready interpretation of model results
- –Advisory-heavy delivery can limit hands-on model customization for internal teams
- –Commodity-focused context may be less optimal for purely insurance-centric use cases
- –Complex datasets and modelling workflows can extend onboarding time
Best for: Enterprises linking catastrophe risk to commodity, asset, or supply exposure decisions
RiskAware
specialistDelivers catastrophe and disaster risk modeling advisory focused on model governance, exposure assessment, and decision support for risk teams.
Decision-focused loss reporting that translates catastrophe assumptions into underwriting actions.
RiskAware stands out for catastrophe modelling delivery that pairs statistical and scenario-driven analytics with clear decision-ready outputs. The service supports hazard and loss modelling workflows used for underwriting, portfolio exposure, and risk transfer analysis. Engagements typically use data processing, model calibration, and impact reporting to translate catastrophe assumptions into measurable financial consequences. Strong emphasis is placed on reproducible methodology and stakeholder communication across technical and non-technical teams.
- +Scenario and statistical catastrophe outputs tied to measurable financial loss metrics
- +Methodology and assumptions documented to support review and governance
- +Data processing and model calibration to improve alignment with available exposure data
- +Clear impact reporting for underwriting and portfolio decision meetings
- –Modelling scope can be constrained by the quality and completeness of provided exposure data
- –Stakeholder communication relies on structured inputs and defined decision criteria
- –Advanced custom workflows may require detailed upfront specification of assumptions
Best for: Insurers and reinsurers needing decision-ready catastrophe modelling for portfolios.
CIMA+ (CIMA Group)
specialistDelivers catastrophe and disaster risk consulting services that translate modeled hazards into actionable emergency planning and resilience outcomes.
Scenario-based catastrophe modelling that ties probabilistic losses to resilience and strategic decisions
CIMA+ stands out for combining catastrophe modelling with advisory work that links hazard analysis to business and risk decisions. The service supports portfolio exposure analysis, probabilistic loss modelling, and scenario design tailored to insurance and reinsurance use cases. It also delivers ongoing risk and resilience inputs such as climate and extreme weather considerations integrated into practical modelling outputs. This makes the provider suitable when modelling results must translate into underwriting, accumulation management, and strategic risk planning.
- +Connects catastrophe modelling outputs to underwriting and risk decision workflows
- +Delivers portfolio exposure analysis aligned to catastrophe modelling structures
- +Supports scenario development for insurance and reinsurance planning needs
- +Integrates resilience and climate-relevant considerations into modelling outputs
- –Strong advisory emphasis can slow purely technical, model-only engagements
- –Project timelines depend heavily on data quality and exposure granularity
- –Less suitable for teams seeking turnkey self-serve modelling tools
Best for: Insurers needing catastrophe modelling linked to risk and resilience decision support
Kytom
specialistDelivers catastrophe risk analytics and emergency disaster risk assessment services using model-based hazard, exposure, and vulnerability methods.
Scenario and portfolio impact analysis built from integrated hazard, exposure, and vulnerability inputs
Kytom stands out by delivering catastrophe modelling workflows that connect hazard, exposure, and vulnerability into decision-ready outputs for insurance and risk teams. Its core capabilities focus on modelling and assessment across natural catastrophe perils and producing scenario and portfolio insights. Delivery is oriented around practical risk reporting, including outputs suitable for underwriting, pricing support, and capital discussions. Engagements typically emphasize structured assumptions, model governance, and traceable results for stakeholders who need audit-friendly outputs.
- +Links hazard, exposure, and vulnerability into decision-ready catastrophe outputs
- +Supports scenario and portfolio impact analysis for risk and underwriting use
- +Provides structured assumptions that improve result traceability
- +Outputs align to stakeholder reporting needs for risk governance
- –Less suitable for teams needing fully bespoke, end-to-end custom model development
- –Catastrophe focus may require separate capability coverage for broader enterprise risk modelling
- –Relies on clear data inputs, making data preparation a critical dependency
Best for: Insurance and risk teams needing governance-focused catastrophe modelling outputs
Fathom
specialistSupports catastrophe modeling and emergency preparedness planning through data-led risk evaluation and scenario analytics delivered as services.
Scenario development tied to loss estimation outputs for underwriting and portfolio decisions
Fathom stands out by delivering catastrophe modelling that combines model-based hazard assessment with practical decision support for risk teams. Core capabilities include hazard and exposure assessment, scenario development, and loss estimation workflows aligned to catastrophe modelling requirements. The service supports multi-risk analysis and prepares outputs usable for underwriting, portfolio risk reviews, and resilience planning. Engagement quality emphasizes repeatable modelling steps rather than one-off analyses that cannot be operationalized.
- +Scenario-driven modelling for clear loss exposure narratives
- +Operationally usable outputs for portfolio and underwriting workflows
- +Multi-risk framing supports broader catastrophe decision making
- +Structured modelling steps improve repeatability across iterations
- –Focus on modelling outputs may limit deeper strategy facilitation
- –Complex scope can require strong client-side data readiness
- –Output customization depends on agreed workflow specifications
Best for: Risk and underwriting teams needing scenario-based catastrophe loss modelling support
RPS
enterprise_vendorProvides engineering-led hazard and disaster risk studies that incorporate catastrophe-style scenario modeling for emergency planning and resilience.
Model governance with documented assumptions and repeatable re-run workflows
RPS provides catastrophe modelling services focused on producing and using risk outputs for insurance and risk management decisions. The provider supports model governance through version control, assumptions documentation, and repeatable workflows that keep results consistent across reporting cycles. Engagements commonly cover exposure preparation, hazard and vulnerability workflows, and portfolio impact analysis using industry-standard modelling approaches. RPS also supports validation and quality checks to ensure outputs align with underwriting and operational risk needs.
- +Structured model governance improves traceability of catastrophe assumptions and results
- +Exposure preparation and portfolio analytics support underwriting and risk decision workflows
- +Validation checks reduce modelling inconsistencies across reporting cycles
- +Repeatable processes support reliable re-runs for changing exposure data
- –Complex engagements require strong data readiness from the client
- –Output interpretation still depends on internal decision processes
- –Model customization can be slower when requirements diverge from standard workflows
Best for: Insurance and enterprise risk teams needing governed catastrophe modelling delivery
How to Choose the Right Catastrophe Modelling Services
This buyer’s guide explains how to evaluate catastrophe modelling services using practical strengths from Aon, KPMG, AECOM, ERM, S&P Global Commodity Insights catastrophe and disaster risk advisory, RiskAware, CIMA+, Kytom, Fathom, and RPS. It covers what these providers deliver, which capabilities matter most for insurers, reinsurers, and public agencies, and how to avoid common implementation pitfalls. The guide ends with a concrete selection framework and provider-specific FAQ answers.
What Is Catastrophe Modelling Services?
Catastrophe modelling services produce loss estimates by linking hazards, exposure, and vulnerability to scenario and stress testing outputs for disaster risk decisions. These services solve the problem of turning event-level disaster exposure into decision-ready results for underwriting, capital assessment, and risk governance. Providers like Aon deliver enterprise-grade catastrophe modelling tied to underwriting and portfolio analytics. Providers like KPMG focus on end-to-end catastrophe modelling governance that connects hazard assumptions to validated loss outputs for audit-ready reporting.
Key Capabilities to Look For
Catastrophe modelling providers need the right technical workflows and decision integration so outputs remain defensible, usable, and repeatable.
Event-level catastrophe outputs tied to underwriting and portfolio analytics
Aon produces event-level catastrophe outputs that feed underwriting, capital, and risk management workflows. This matters when portfolios require event-level decisions rather than only aggregated loss views.
End-to-end model governance with validation, documentation, and challenge processes
KPMG delivers audit-ready catastrophe modelling with strong model governance and validation so hazard and vulnerability assumptions link to validated loss outputs. RPS also emphasizes documented assumptions, version control, and quality checks to support repeatable re-runs across reporting cycles.
Hazard, exposure, and vulnerability loss estimation workflow
AECOM combines hazard, exposure, and vulnerability to quantify disaster losses with engineering-grade interpretation. Kytom similarly links hazard, exposure, and vulnerability into decision-ready catastrophe outputs with traceable, stakeholder-friendly results.
Scenario and stress testing for insurance, reinsurance, and resilience planning
Aon supports scenario and stress testing for reinsurance and insurance portfolio planning with decision-ready outputs. CIMA+ delivers scenario-based catastrophe modelling that ties probabilistic losses to resilience and strategic decisions.
Defensible assumption translation into risk governance and underwriting decisions
ERM provides expert model validation and translates modelling assumptions into defensible risk narratives for governance and decision workflows. RiskAware also focuses on documented methodology and assumptions that translate catastrophe assumptions into measurable financial loss metrics for underwriting actions.
Multidisciplinary engineering advisory to improve interpretation and implementability
AECOM adds multidisciplinary engineering context to improve interpretation of model outputs and support implementable recommendations. This capability is valuable when stakeholders need technical credibility beyond loss numbers for emergency disaster planning.
How to Choose the Right Catastrophe Modelling Services
A practical selection framework maps the organization’s decision needs to the modelling workflow strengths offered by specific providers.
Start with the decision workflow that the modelling must feed
Choose Aon when the goal is integrated catastrophe modelling that feeds underwriting, capital assessment, and broader risk management workflows using event-level outputs. Choose ERM when the goal is operational integration into capital, claims, and risk governance decision processes with defensible assumption documentation.
Set governance and documentation requirements up front
Select KPMG when audit-ready catastrophe modelling governance is required to connect hazard assumptions to validated loss outputs with controls, documentation, and model validation. Select RPS when the priority is model governance through documented assumptions, version control, and repeatable workflows that keep results consistent across re-runs.
Confirm the hazard to loss methodology matches the data reality
Validate that the provider can execute a hazard, exposure, and vulnerability loss estimation workflow with data processing and model calibration. RiskAware emphasizes methodology documentation and calibration aligned to available exposure data, while Kytom relies on clear data inputs that make data preparation a dependency.
Match scenario coverage to the scenarios stakeholders actually need
Choose Aon when scenario and stress testing are needed for reinsurance and portfolio planning with decision-ready interpretations. Choose CIMA+ when scenario-based probabilistic losses must tie directly to resilience and strategic decisions for underwriting and accumulation management.
Align advisory depth with customization expectations
Choose AECOM when engineering-grade advisory is required alongside scenario-driven loss estimation that combines hazard, exposure, and vulnerability. Choose Fathom when the priority is operationally usable scenario development with repeatable modelling steps that support underwriting and portfolio reviews rather than deep strategy facilitation.
Who Needs Catastrophe Modelling Services?
Different catastrophe modelling providers fit different operational contexts, from insurer underwriting teams to public agencies needing governed reporting and traceable validation.
Insurers and reinsurers needing integrated catastrophe modelling and portfolio analytics
Aon is a strong match for teams needing end-to-end catastrophe modelling that feeds underwriting, capital, and risk management workflows with event-level outputs. RiskAware also fits insurers and reinsurers needing decision-ready catastrophe modelling for portfolios with clear impact reporting that translates catastrophe assumptions into underwriting actions.
Insurers and public agencies needing governed catastrophe modelling for reporting
KPMG fits insurers and public agencies that require controls, documentation, and validation to produce audit-ready catastrophe risk outputs. Kytom also aligns with governance-focused catastrophe modelling outputs that deliver traceable results for stakeholder reporting needs.
Enterprise teams needing end-to-end catastrophe modelling with engineering-grade advisory inputs
AECOM fits enterprise teams that need scenario-based loss estimation combining hazard, exposure, and vulnerability with engineering interpretation and stakeholder-ready documentation. This combination supports multi-discipline projects that require both credible modelling and implementable recommendations.
Enterprises linking catastrophe risk to commodity, asset, or supply exposure decisions
S&P Global Commodity Insights catastrophe and disaster risk advisory fits enterprises that need catastrophe and disaster risk advisory blending loss modelling with commodity impact interpretation. This is valuable when exposure decisions depend on economic and supply variables as well as loss outcomes.
Common Mistakes to Avoid
Misalignment between governance needs, data readiness, and customization expectations can slow delivery or reduce decision usability across catastrophe modelling providers.
Requesting purely technical outputs without governance and governance artifacts
KPMG is built around model governance, documentation, and validation, so skipping governance alignment increases stakeholder friction for audit-ready reporting needs. RPS also relies on documented assumptions and repeatable workflows, so governance expectations should be clarified early to avoid rework.
Underestimating exposure data quality and completeness requirements
RiskAware limits modelling scope when provided exposure data quality and completeness are insufficient, which can constrain results and increase calibration iterations. Kytom and RPS also depend on clear data inputs and strong client-side data readiness, so weak exposure governance can delay repeatable re-runs.
Treating scenario outputs as interchangeable with decision workflows
Aon’s strength is tying event-level outputs to underwriting and portfolio analytics, so outputs need deliberate mapping to decision criteria. ERM similarly emphasizes operational integration, so a mismatch between scenario outputs and decision workflows like capital, claims, and risk governance can reduce usability.
Choosing an advisory-heavy provider when rapid exploratory iteration is the main goal
KPMG and CIMA+ emphasize governed, stakeholder-focused delivery, which can slow one-off technical sprints that require rapid exploratory iteration. Aon and Fathom fit better when repeatable modelling steps and portfolio decision narratives must be operationalized within an agreed workflow.
How We Selected and Ranked These Providers
we evaluated every service provider on three sub-dimensions. The first sub-dimension is capabilities with weight 0.4. The second sub-dimension is ease of use with weight 0.3. The third sub-dimension is value with weight 0.3. The overall rating is the weighted average with overall = 0.40 × features + 0.30 × ease of use + 0.30 × value. Aon separated from lower-ranked providers because it combines high-scoring capabilities with enterprise integration, including event-level catastrophe outputs tied to underwriting, capital assessment, and risk management workflows.
Frequently Asked Questions About Catastrophe Modelling Services
How do Aon and ERM differ in delivering catastrophe modelling outputs for underwriting and portfolio decisions?
Which provider best fits audit-ready catastrophe modelling governance and documentation requirements?
What distinguishes AECOM’s approach when catastrophe modelling must inform engineering-grade resilience planning?
How do RiskAware and Kytom handle decision-ready reporting for underwriting and risk transfer discussions?
Which provider is most suitable for combining catastrophe risk modelling with commodity, asset, or supply-chain decisioning?
What onboarding or implementation activities should teams expect from providers that deliver model implementation oversight?
What technical inputs are usually required across catastrophe modelling workflows for providers like Fathom and CIMA+?
How do ERM and RPS differ in model validation and quality checks across reporting cycles?
What common failure modes can occur if a team engages catastrophe modelling services without strong governance, and how do providers mitigate them?
Conclusion
After evaluating 10 emergency disaster, Aon 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.
Tools reviewed
Primary sources checked during evaluation.
Referenced in the comparison table and product reviews above.
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