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Financial Services InsuranceTop 10 Best Insurance Modeling Software of 2026
Compare leading insurance modeling software options for actuarial and risk teams, including Moody’s AXIS, to match use cases and tradeoffs.
How we ranked these tools
Vendors set the topic, but our editors set the inclusion criteria and the evaluation framework before research starts.
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Framework: Features 40% · Ease 30% · Value 30%
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Moody’s AXIS is the best choice for actuarial teams that need repeatable stochastic study runs with strong assumption control, since it preserves traceable links from configured inputs to model outputs. FIS Prophet is the alternative for frequent portfolio recalculations, thanks to its controlled, versioned run traceability.
Editor’s top 3 picks
Three quick recommendations before you dive into the full comparison below — each one leads on a different dimension.
Moody's AXIS
Assumption and version governance that maintains traceable links from configured inputs to model outputs.
Best for: Fits when actuarial teams need repeatable stochastic study runs with strong assumption control and traceability.
FIS Prophet
Editor pickModel run traceability links scenario results to executed configuration and release history for controlled comparisons across versions.
Best for: Fits when actuarial teams run frequent portfolio recalculations and need controlled, repeatable stochastic and deterministic outputs.
Milliman MG-ALFA
Editor pickAssumption-driven study configuration and results management designed for repeatable insurance modeling cycles, not just one-off calculations.
Best for: Fits when actuarial teams need controlled study runs across scenarios for pricing or reserving governance.
Comparison Table
Moody's AXIS
enterpriseActuarial modeling software for life insurance, annuity, and health portfolios.
Assumption and version governance that maintains traceable links from configured inputs to model outputs.
Moody's AXIS supports stochastic modeling workflows where loss outputs are generated from modeled event, frequency, and severity behavior, then carried into aggregate results for downstream decisioning. The software is built around configuration-driven study runs, which helps standardize how the same inputs and assumptions produce consistent model outputs across teams. Model change control features such as version tracking and assumption management reduce the friction of re-running comparable studies for validation and committee review.
A key tradeoff is that AXIS is governance and workflow oriented, so teams must invest in clean input mappings and disciplined study configurations to avoid rework when model structures change. A strong usage situation is reserving analysis that requires repeated model runs under multiple assumptions and consistent output reproducibility for audit and validation.
- +Workflow-based study runs that keep model configurations repeatable
- +Assumption versioning and model traceability for validation cycles
- +Stochastic run support for aggregate outputs used in decisioning
- +Cross-team governance patterns that reduce uncontrolled assumption drift
- –Input mapping discipline is required to prevent costly rework
- –Some advanced custom modeling patterns depend on structured study setup
- –Governance controls add operational overhead for small teams
- –Modeling depth can increase configuration time for new programs
Actuarial modeling teams
Run stochastic pricing studies
Consistent scenario results
Reserving analytics teams
Govern reserving assumption changes
Reduced revalidation effort
Show 2 more scenarios
Capital modeling teams
Produce solvency capital inputs
More defensible capital runs
Aggregate loss outputs feed capital-oriented risk views across modeled scenarios.
Risk analytics governance
Maintain model audit trails
Clear change accountability
Study configuration tracking ties changes to outputs across multiple model versions.
Best for: Fits when actuarial teams need repeatable stochastic study runs with strong assumption control and traceability.
FIS Prophet
enterpriseActuarial modeling platform for life, health, and general insurance businesses.
Model run traceability links scenario results to executed configuration and release history for controlled comparisons across versions.
Model preparation in FIS Prophet emphasizes repeatable inputs and controlled execution, which supports deterministic modeling when teams need fixed assumption sets for pricing or reserving updates. Stochastic modeling workflows and Monte Carlo simulation can generate distribution outputs for aggregate risk views used in exposure-based analysis and scenario testing. Governance is reinforced by run traceability and configuration controls around model execution so teams can compare outputs across model versions without losing the link to assumptions and inputs.
A key tradeoff is that Prophet’s modeling workflow is most effective inside established FIS-centric data and operational processes. Teams with highly bespoke data pipelines may need extra engineering work to map policy, exposure, and claims feeds into Prophet-ready inputs before they can run repeatably. Prophet is a strong choice for periodic portfolio updates when the organization needs consistent automation for scenario analysis and distribution outputs across underwriting, reserving, and capital perspectives.
- +Strong control over model runs and releases for repeatability
- +Supports deterministic and stochastic modeling workflows with scenario outputs
- +Automation-oriented execution for scheduled portfolio recalculations
- +Audit-ready run history links outputs to executed configuration
- –Best results require tighter coupling to FIS operational processes
- –Upfront mapping effort for policy and exposure feeds
- –Workflow complexity can slow early adoption for new teams
- –Advanced configurations need governance discipline to avoid drift
Pricing and analytics teams
Monthly rate changes with scenarios
Consistent pricing updates and comparisons
Actuarial reserving teams
Loss development updates with governance
Faster version-controlled reserving refresh
Show 2 more scenarios
Capital and risk modeling teams
Stochastic loss distributions for capital
Capital-ready distribution outputs
Generate simulated loss distributions for aggregate risk views to support scenario analysis and sensitivity testing.
Reinsurance analytics teams
Treaty scenarios and aggregated results
Repeatable reinsurance scenario studies
Automate scenario runs to quantify treaty impacts across exposure and portfolio groupings.
Best for: Fits when actuarial teams run frequent portfolio recalculations and need controlled, repeatable stochastic and deterministic outputs.
Milliman MG-ALFA
vertical specialistLife insurance actuarial modeling software for product, valuation, and risk analysis.
Assumption-driven study configuration and results management designed for repeatable insurance modeling cycles, not just one-off calculations.
MG-ALFA is built for insurance risk modeling work where assumptions and exposure or policy inputs must be versioned alongside model runs for auditable consistency. It supports end-to-end study cycles that combine model parameterization, scenario analysis, and aggregation of outputs into report-ready structures.
A key tradeoff is that the workflow depth favors disciplined actuarial processes over ad hoc analytics, so teams must invest in run configuration standards for consistent throughput. It fits best when reserving, pricing, or capital studies require repeatable run management across multiple model assumptions and stakeholder review points.
- +Strong run management for assumptions and study configurations
- +Automates repeatable model runs across scenarios
- +Produces structured outputs for actuarial deliverables
- +Supports deterministic and stochastic modeling workflows
- –Requires disciplined setup of run standards for consistent outputs
- –Less suited to lightweight exploratory analysis tasks
- –Integration depth depends on external data pipelines
- –Model authoring flexibility can be limited versus custom-code stacks
Actuarial reserving teams
Run credibility and parameter sensitivity studies
Faster iteration with consistent outputs
Pricing analytics teams
Evaluate portfolio-level pricing scenarios
More stable scenario comparisons
Show 1 more scenario
Capital modeling groups
Simulate adverse risk scenarios
Clearer drivers of changes
Aggregates scenario outputs into structured results for capital and solvency sensitivity reviews.
Best for: Fits when actuarial teams need controlled study runs across scenarios for pricing or reserving governance.
Aon PathWise
enterpriseInsurance financial modeling software for asset, liability, and capital analysis.
Assumption and model-run orchestration supports repeatable scenario production tied to actuarial governance workflows.
Aon PathWise is an insurance modeling solution focused on reserving analysis and capital modeling workflows tied to Aon actuarial services. It supports assumption configuration, scenario output, and model execution orchestration used for insurance risk modeling tasks.
- +Reserving and capital modeling workflows are built around actuarial run cycles
- +Scenario output management supports repeatable sensitivity and what-if comparisons
- +Assumption configuration reduces ad hoc edits between successive model runs
- +Operational controls fit governance needs for multi-team actuarial workstreams
- –Model customization depth can require specialist configuration support
- –Automation options depend on integration patterns with upstream exposure and policy feeds
- –Run traceability details can be harder to surface without disciplined conventions
- –Works best when processes align with Aon actuarial delivery expectations
Best for: Fits when insurance teams need managed reserving and capital scenario runs with governance controls.
Verisk Touchstone
vertical specialistCatastrophe risk modeling software for property insurers and reinsurers.
Touchstone’s environment-aware model management and results handoff supports consistent reuse of modeling assumptions across enterprise workflows.
Verisk Touchstone focuses on enterprise insurance risk modeling with an integrated workflow for building models from actuarial and exposure inputs to outputs used in pricing, underwriting, and capital decisions. Its differentiation comes from Verisk-native content and interoperability with Verisk ecosystems, which reduces friction when the same exposure and peril assumptions must be reused across teams.
- +Strong Verisk content and workflow reuse across pricing, underwriting, and capital use cases
- +Stochastic simulation support for scenario testing and frequency-severity style analyses
- +Model output packaging designed for consistent downstream consumption by analysis teams
- +Governance-oriented controls for managing model changes across environments
- –Model authoring requires more structured setup than script-first modeling tools
- –Integration depth depends on aligning data feeds to Touchstone input conventions
- –Complex workflows can slow iteration without disciplined configuration management
- –Advanced automation often needs API-aware engineering by the consuming team
Best for: Fits when insurers need enterprise-governed insurance risk modeling reused across multiple teams and decision processes.
Oasis Loss Modelling Framework
API-firstOpen insurance catastrophe modeling framework for peril models, exposure data, and loss calculations.
OasisLmf’s modular loss modeling workflow separates configuration, data preparation, and calculation orchestration for repeatable scenario runs.
Oasis Loss Modelling Framework turns loss modeling workflows into a governed pipeline for catastrophe and portfolio risk analysis. It supports exposure ingestion, vulnerability and hazard-driven loss calculations, and stochastic scenario runs that produce occurrence and aggregate loss outputs.
- +Configurable orchestration for hazard and vulnerability driven loss runs
- +Stochastic scenario execution supports distribution outputs for risk reporting
- +Workflow separation helps keep model configuration distinct from compute steps
- +Exportable loss measures support downstream aggregation and studies
- –Requires strong setup discipline across data mappings and configuration
- –Model building tasks can be script and configuration heavy
- –UI coverage for end-to-end analysis is limited versus full application suites
- –Integration work may be needed to align with in-house exposure and portfolio formats
Best for: Fits when teams need catastrophe loss calculations with controlled scenario orchestration and scalable portfolio studies.
RiskAgility FM
enterpriseFinancial modeling software for insurance enterprise risk management.
Assumption and model-run governance that ties inputs to execution history for repeatable scenario testing.
RiskAgility FM supports insurance risk modeling workflows that connect exposure and policy context to scenario-based loss outputs. It emphasizes configurable model runs, assumptions tracking, and operational controls so actuarial teams can repeat analyses with consistent governance.
- +Model run configuration supports repeatable actuarial scenario testing
- +Assumption management improves audit trails for model inputs and changes
- +Integration options reduce manual handoffs between data prep and modeling
- +Governance controls help standardize model execution across teams
- –Higher configuration effort than tools focused on reserving-only workflows
- –Scenario modeling depth can require more setup discipline than expected
- –API surface may feel incomplete for highly custom actuarial pipelines
- –Complex portfolio structures can increase model run troubleshooting time
Best for: Fits when actuarial teams need controlled, repeatable scenario runs across portfolios and integrations.
ALGo
enterpriseActuarial and risk modeling software for insurance companies.
Scenario-driven execution with API-triggered runs that maintain tight traceability from inputs to produced outputs.
ALGo converts insurance risk inputs into modeled loss outcomes with an emphasis on scenario execution and governance-ready outputs. The workflow centers on building exposure and assumptions for stochastic and deterministic runs, then producing model results suitable for underwriting, reserving support, and capital-style reporting.
- +API-driven model runs support repeatable execution from external tools
- +Scenario configuration reduces manual rework when assumptions change
- +Run traceability ties outputs back to input sets and settings
- +Deterministic and stochastic modeling workflows cover common insurance use
- –Model setup depends on consistent input formatting across sources
- –Governance features are stronger for run traceability than for full model authoring
- –Limited visible support for complex reserving workflows in the core experience
- –Automation depth can require custom orchestration for end-to-end pipelines
Best for: Fits when actuarial teams need API-triggered scenario modeling and controlled outputs for risk decisions.
KatRisk
vertical specialistCatastrophe risk modeling software for natural peril exposure and loss analysis.
Exposure-to-peril mapping with scenario output generation aimed at portfolio aggregation for catastrophe risk decisions.
KatRisk turns catastrophe risk and insurance exposure data into modeled loss estimates for portfolio planning and scenario analysis. It focuses on linking exposure attributes to peril-specific hazard assumptions and distributional loss outputs, then running repeatable scenario sets across policy groups.
- +Cat-risk workflow connects exposure records to peril loss outputs
- +Scenario sets support repeatable analysis across policy groupings
- +Exported loss outputs fit common aggregation and reporting needs
- +Models maintain consistent mapping between exposure attributes and hazards
- –Exposure file preparation is a prerequisite for usable results
- –Advanced custom scenario logic needs structured configuration work
- –Less suited for life and health actuarial stacks versus cat-first needs
- –Deep model diagnostics require more domain discipline than basic runs
Best for: Fits when property insurers need repeatable catastrophe scenario losses tied to exposure data and grouping rules.
SAS Viya
enterpriseAnalytics platform supporting insurance pricing, claims modeling, forecasting, and risk analysis.
SAS Viya’s model deployment and scoring workflow connects analytics execution to controlled operations with programmatic job access.
SAS Viya supports insurance modeling teams that need a governed analytics environment for end-to-end risk and actuarial workflows. It combines model development, optimization, and batch scoring with deployment controls for running scenarios and producing repeatable outputs.
- +Integrated workflow from data prep through analytics and scoring jobs
- +Strong automation for repeatable scenario runs and controlled deployments
- +Good governance hooks for access control, lineage, and operational auditing
- +Extensibility supports custom modeling logic beyond built-in procedures
- –Requires SAS-centric tooling patterns to get consistent governance outcomes
- –Provisioning and environment management can be heavy for small teams
- –Model performance tuning may take dedicated work for large simulations
- –Limited native catastrophe-specific workflow compared with specialist tools
Best for: Fits when insurance analytics teams need governed, repeatable modeling runs across environments.
Conclusion
After evaluating 10 insurance modeling software, Moody's AXIS 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 Insurance Modeling Software
How do Moody’s AXIS and FIS Prophet differ in model governance for stochastic runs?
Which tools provide API-first or programmatic execution for insurance modeling workflows?
When teams need deterministic modeling updates, how does KatRisk compare with Milliman MG-ALFA?
What data-migration steps usually break integrations between exposure data sources and these tools?
How do Oasis Loss Modelling Framework and Aon PathWise handle orchestration across multiple scenario runs?
Where do admin controls and RBAC tend to matter most for enterprise model change governance?
What tradeoff appears when a workflow is deeper in reserving and study management versus more focused on catastrophe peril modeling?
How do model configuration standards affect throughput for large portfolio studies in tools like Oasis and RiskAgility FM?
When SSO and security controls are mandatory, which platforms typically fit tighter enterprise access patterns?
Tools reviewed
Primary sources checked during evaluation.
Referenced in the comparison table and product reviews above.
How to Choose the Right Insurance Modeling Software
The differences show up in how tightly each option controls inputs and outputs. We look at integration depth, API automation, data model control, RBAC, and audit log coverage so your insurance modeling work stays consistent from build to reporting.
Data flow integration with core systems
Why it matters
When data flow integration is good, insurance modeling software can reuse your existing sources and keep calculations aligned with operational feeds. When it’s weak, teams rebuild the same mappings for each model run.
How to check
Ask for a demo that pulls the same dataset into a modeling run and then writes results back to your downstream tools. Confirm whether it uses configurable mappings and repeatable workflows rather than manual re-keying each time.
API and automation surface for model runs
Why it matters
An insurance modeling workflow often needs repeatable run controls for schedules and re-runs. A stronger API and automation surface reduces operator variability around the same model version.
How to check
Request an example API call or job workflow that triggers a run, passes parameters, and retrieves outputs. During the demo, test changing one control setting and confirm the run lineage and outputs update accordingly.
Configurable data model and schema mapping
Why it matters
Data model and schema mapping determines whether your insurance modeling inputs match the tool’s expected structure. Good schema mapping helps prevent silent misalignment between model inputs and reporting outputs.
How to check
Bring a sample of your current input layout and ask the demo to map it into the tool’s internal structure. Look for explicit field definitions, validation messages, and a way to version those mappings over time.
RBAC, audit log, and change history
Why it matters
Insurance models change with assumptions, and teams need accountability. RBAC plus audit log and change history make it clear who changed what and which results those changes produced.
How to check
In the demo, create at least two roles (viewer and editor) and verify they see different actions. Then change a configuration and check that the audit log records the change details and links to the affected run or output set.
Run validation and controlled outputs
Why it matters
Run validation and controlled outputs reduce the chance that an insurance modeling result reflects incomplete inputs or a wrong configuration. This is especially important when multiple model versions coexist.
How to check
Ask how the product validates required inputs before execution and whether it blocks runs with missing or inconsistent fields. Then check whether outputs are labeled with configuration/version metadata and whether you can reproduce the same outputs later.
Red flags
- Outputs lack clear versioning or configuration metadata for each run.
- Reruns require manual copying of inputs or rebuilding mappings each time.
- Role permissions and audit history don’t show who changed model settings.
Fit by situation
- If we must run scheduled insurance modeling and re-run on demand, prioritize API and automation surface.
- If our biggest pain is input-to-output misalignment across teams, prioritize configurable data model and schema mapping.
- If governance is central to our insurance modeling process, prioritize RBAC, audit log, and change history.
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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