GITNUXSOFTWARE ADVICE
Data Science AnalyticsTop 10 Best Dynamic Financial Analysis Software of 2026
Ranking and comparison of dynamic financial analysis software for insurers, featuring tools like Pigment, Modano, and Palantir Foundry.
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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Pigment is the best pick for insurers that need governed, repeatable scenario runs with testing logic shared across teams, whereas Datarails fits when you want Excel-based dynamic balance sheet and cash flow projections under controlled planning, and Alteryx is the budget slot choice if you need automated scenario runs that combine prep, modeling, and standardized reporting.
Editor’s top 3 picks
Three quick recommendations before you dive into the full comparison below — each one leads on a different dimension.
Pigment
Scenario versioning with linked calculations enables controlled, auditable comparisons between assumption sets.
Built for fits when insurers need governed scenario runs and repeatable balance sheet and cash testing logic..
Modano
Editor pickWorkflow-driven scenario execution that ties Monte Carlo iteration runs to governed batch projection outputs.
Built for fits when insurers need governable DFA runs across many scenarios with repeatable execution..
Palantir Foundry
Editor pickFoundry deployment configuration and governance tooling support production promotion with traceable lineage across scenario runs.
Built for fits when insurers need governed scenario workflows with auditability across model iterations..
Related reading
Comparison Table
Pigment
enterpriseCollaborative planning platform for dynamic financial analysis and business modeling.
Scenario versioning with linked calculations enables controlled, auditable comparisons between assumption sets.
Pigment modeling supports multi-step calculation graphs that update downstream measures when drivers change. The product provides structured inputs, configurable views, and scenario comparisons that help finance and underwriting teams evaluate alternative assumptions using the same underlying logic. Integration depth is strongest when insurers standardize data feeds into model inputs and then use automation to regenerate planning outputs on a schedule or on-demand.
A key tradeoff is that complex stochastic simulation engines and loss distribution math are not Pigment’s core runtime, so teams often pair Pigment with external actuarial engines and import summarized results. Pigment fits best when a governing planning model needs frequent recalculation, board-ready reporting, and controlled scenario management around DFA-style outputs produced elsewhere.
- +Versioned scenarios keep assumptions and outputs comparable across runs
- +Calculation dependency graph updates downstream KPIs automatically
- +Workflow and role controls support governed model publishing
- +Automation hooks support scheduled rebuilds for planning cycles
- –Stochastic simulation runtime for Monte Carlo iterations is not native
- –External model imports require disciplined mapping for consistency
- –Performance tuning can be necessary for very high-dimensional inputs
- –Actuarial-specific constructs like copula calibration need external tooling
Finance planning teams
Run quarterly capital and earnings scenarios
Faster scenario turnaround
Actuarial and finance liaisons
Import external DFA outputs into planning
Reduced rework
Show 2 more scenarios
Underwriting management
Stress underwriting cycle assumptions
Clear decision traceability
Managers compare alternative lapse, risk, and pricing driver sets using the same calculation graph.
Enterprise reporting teams
Standardize board-ready outputs from scenarios
More consistent reporting
Configured views pull from model outputs so scenario comparisons publish consistently.
Best for: Fits when insurers need governed scenario runs and repeatable balance sheet and cash testing logic.
More related reading
Modano
enterpriseFinancial modeling platform enabling dynamic financial analysis through modular Excel models.
Workflow-driven scenario execution that ties Monte Carlo iteration runs to governed batch projection outputs.
Modano fits teams that run frequent capital and reserve scenario testing because it connects scenario generation to a repeatable projection workflow. It supports Monte Carlo iteration driven model execution and produces outputs suitable for economic capital model reporting and capital adequacy testing discussions.
A key tradeoff is that deeper automation depends on model configuration discipline and clean upstream assumptions, because governance around changes is central to repeatability. Modano is a strong fit when an insurer needs batch runs across many scenarios for underwriting cycle modeling and capital planning rather than interactive one-model-at-a-time work.
- +Scenario-driven execution designed for repeated stochastic runs
- +Batch outputs support capital adequacy testing and capital planning workflows
- +Configuration supports repeatability across many assumption sets
- +Workflow orientation reduces manual orchestration for projection runs
- –Advanced automation requires disciplined model configuration management
- –Less suitable for exploratory spreadsheet-style iteration without run governance
- –Integration effort can be nontrivial when data must be normalized
- –Model changes can require more review cycles than ad hoc tools
Actuarial modeling teams
Run DFA capital scenarios in batches
Faster production of scenario sets
Capital planning analysts
Stress test capital with consistent assumptions
Consistent outputs across scenarios
Show 2 more scenarios
Finance and risk governance
Control change across model inputs
Reduced audit friction for runs
Uses governed execution so updated parameters remain traceable across repeated runs.
Portfolio and ALM teams
Compare balance sheet projections across scenarios
Clear scenario-to-scenario comparisons
Applies configurable projection settings to support cash flow testing and balance sheet projection comparisons.
Best for: Fits when insurers need governable DFA runs across many scenarios with repeatable execution.
Palantir Foundry
enterpriseOperating system for enterprise data integration and dynamic financial analytics at scale.
Foundry deployment configuration and governance tooling support production promotion with traceable lineage across scenario runs.
Palantir Foundry fits dynamic financial analysis work when insurers need governed data movement across systems like policy administration, claims, reinsurance, and general ledger. The environment supports repeatable dataset preparation, configurable modeling runs, and traceability of outputs back to inputs. RBAC-style access control and audit log visibility help teams manage who can view datasets and promote outputs into reporting.
A common tradeoff is that teams typically need stronger internal process discipline to design the data-to-model interfaces and promotion steps between sandbox and production workspaces. Foundry works best when dynamic testing must be re-run frequently, such as underwriting cycle modeling and regulatory capital framework iterations driven by changing assumptions.
- +Governed pipelines connect policy, claims, and finance datasets to model inputs
- +Workflow automation supports controlled promotion from experimentation to reporting
- +Fine-grained user permissions align model access with responsibilities
- +API-first integrations support orchestration with external actuarial components
- –Setup time rises when mapping source systems into reusable modeling datasets
- –Some actuarial processes require custom integration effort outside Foundry
Actuarial analytics teams
Re-run scenario sets with traceable inputs
Faster iteration cycles
Enterprise data teams
Standardize reinsurance and finance inputs
Fewer data reconciliation issues
Show 2 more scenarios
Risk governance and compliance
Control access to capital testing outputs
Reduced governance risk
RBAC-style permissions and audit visibility restrict who can view or promote scenario outputs.
Model operations teams
Automate Monte Carlo iteration orchestration
Higher run throughput
APIs and job automation connect external simulation engines to scheduled reporting workflows.
Best for: Fits when insurers need governed scenario workflows with auditability across model iterations.
Datarails
SMBAI-powered FP&A platform for dynamic financial analysis built on Excel.
Assumption-to-output orchestration that updates dependent calculations and reporting across versioned scenario runs.
Datarails provides dynamic financial analysis in an insurer-friendly modeling workflow that connects assumptions, drivers, and reporting into versioned runs.
It supports scenario generation for balance sheet projection and cash flow testing with automated recalculation when inputs change.
The product emphasizes integration depth through its data ingestion options and an API surface for model orchestration and downstream consumption.
It is best used when model governance, repeatable runs, and structured outputs for capital adequacy testing matter more than ad hoc spreadsheet work.
- +Scenario-driven DFA runs with traceable input changes and repeatable outputs
- +Strong integration options for feeding models and pushing results to other systems
- +Automation focus for recalculation and publishing across frequent assumption updates
- +Versioned modeling workflow supports controlled iterations for forecasting cycles
- –Model build and governance require upfront configuration discipline
- –Complex underwriting and reinsurance logic can demand careful mapping of inputs
- –High scenario counts can pressure runtime throughput without tuning
- –Advanced model extensibility may depend on technical support for edge cases
Best for: Fits when insurers need repeatable, scenario-based balance sheet and cash flow projections under controlled governance.
Jedox
enterpriseIntegrated planning platform for dynamic financial and operational analysis.
Jedox rule-driven model execution that updates multidimensional reports from parameterized scenario inputs.
Jedox generates dynamic financial analysis models by combining spreadsheet-style authoring with multidimensional reporting. The solution supports scenario comparison for balance sheet projection, cash flow testing, and capital adequacy style planning through configurable rule logic.
Jedox’s integration focus shows up in its connectivity for data import and its application programming access for model automation. Governance relies on role-based access controls, model permissions, and audit trails for controlled changes.
- +Spreadsheet-like modeling with multidimensional reporting for scenario finance work
- +Model automation via an application programming interface for repeatable runs
- +Role-based access controls and change history for managed planning cycles
- +Rule and calculation logic supports spreadsheet fidelity with business constraints
- –Scenario modeling gets complex when large hierarchies and many assumptions interact
- –Advanced actuarial workflows may require external integration for specialized data
- –Governance depends on disciplined model permissions and publishing practices
- –Performance tuning can be needed for high-throughput Monte Carlo iteration workloads
Best for: Fits when insurers need controlled planning models with strong scenario management and automation hooks.
Vena
SMBComplete planning platform for dynamic financial analysis and budgeting.
Managed model workflows with controlled approvals and permissions for insurer planning artifacts.
Vena is a dynamic financial analysis system designed for insurer finance teams that need model governance around structured planning, forecasting, and consolidation. It centers on managed Excel-driven models with workflow, versioning, and role-based access so actuarial and finance inputs stay traceable during scenario runs.
Integrations support pulling data from enterprise systems and pushing standardized outputs to downstream reporting. Automation is geared toward repeating planning cycles with controlled approvals and consistent delivery of balance sheet and cash flow projections.
- +Workflow and approval controls for repeatable planning cycles
- +Role-based access keeps shared insurer models segmented
- +Excel-based model management reduces rework during iterations
- +Integration patterns fit enterprise data extraction and standardized output
- –Model design discipline is required to avoid brittle spreadsheet logic
- –Advanced stochastic scenario generation depends on the model build
- –High automation throughput can require careful performance tuning
- –API coverage is not as uniform as specialist modeling tools
Best for: Fits when insurer finance needs governed, repeatable forecasting around Excel-based actuarial and financial models.
Synario
enterpriseFinancial modeling platform for dynamic scenario analysis and strategic decision-making.
Configuration-driven insurer workflow ties assumption updates to scenario runs and the reporting layout in one governed model context.
Synario focuses on insurer-friendly dynamic financial analysis workflows that connect assumptions, model logic, and reporting in one place. It supports scenario generation and iterative projections used for capital adequacy testing and planning cycles.
Users configure cash flow testing inputs, run risk aggregation, and review outputs without exporting to a separate DFM build environment. The solution also provides an integration and automation surface for pulling data and pushing outputs into downstream planning and governance processes.
- +Insurer-oriented workflow builder links assumptions to projection outputs
- +Scenario generation supports repeated runs for comparison across planning cycles
- +Risk aggregation and reporting are driven from the same run configuration
- +Integration and automation options reduce manual export steps
- –Complex models need careful configuration to keep dependencies consistent
- –Deep customization beyond the core modeling workflow may require engineering effort
- –Large scenario batches can demand more attention to throughput planning
- –Governance features may lag teams that need granular enterprise RBAC
Best for: Fits when insurers need scenario-based DFA runs with repeatable configurations and controlled reporting outputs.
Alteryx
enterpriseCode-free analytics automation platform for dynamic financial modeling and forecasting.
Server-scheduled analytics workflows that run the same scenario logic end to end for consistent outputs.
Alteryx delivers dynamic financial analysis through repeatable analytics workflows that connect data prep, modeling logic, and reporting under one runbook. Its standout strength is workflow automation for scenario generation and risk calculations built from chained data transforms and calculation steps.
Alteryx also supports extensibility via custom tools and scripts so DFA-style analyses can reuse standardized components across departments. Governance and administration center on workbook access control, scheduled runs, and environment configuration for consistent output across teams.
- +Workflow automation ties data preparation to scenario outputs in one execution path
- +Custom tools and scripts let teams package reusable modeling logic
- +Batch execution supports scheduled runs for recurring forecasting cycles
- +Integration options fit common insurer data sources and reporting targets
- –Complex dependency logic becomes harder to trace across long workflows
- –Advanced governance depends on disciplined configuration and access management
- –High-throughput Monte Carlo iterations can strain local or shared execution environments
- –Versioning workbook logic requires formal change control to avoid drift
Best for: Fits when insurers need automated scenario runs that combine data prep, modeling, and standardized reporting.
Anaplan
enterpriseConnected planning platform for dynamic financial modeling across business units.
Dedicated planning model automation with RBAC-governed change control across model environments, plus API-first data integration.
Anaplan supports dynamic financial analysis through its planning model workspace for insurers that need repeatable balance sheet and cash flow projections. It provides a spreadsheet-like modeling experience with controlled dimensional planning, versioned scenarios, and view-level calculations that can drive scenario stress testing and capital adequacy testing workflows.
Anaplan also offers an automation and API surface for loading model data, orchestrating runs, and integrating outputs with adjacent actuarial, risk, and finance systems. Strong governance features like RBAC and environment controls help teams manage model changes across development and production iterations.
- +Modeling workflow supports scenario comparisons with controlled calculation chaining
- +APIs and scheduled jobs enable repeatable data loads and orchestration
- +RBAC and environment separation support governance across model development stages
- +Centralized model views improve consistency across finance and risk users
- –Model performance depends on disciplined sizing of dimensionality and calculation logic
- –Advanced actuarial constructs often require careful mapping to Anaplan data structures
- –Automation design takes build-time effort to avoid brittle run dependencies
- –Complex insurer-specific structures can increase model maintenance overhead
Best for: Fits when insurers need governed, scenario-driven planning runs integrated with upstream data systems.
CCH Tagetik
enterpriseCorporate performance management software for planning, forecasting, consolidation, and reporting.
Calculation chain configuration that ties allocation logic and reporting outputs to scenario-managed runs inside the same execution workflow.
CCH Tagetik is dynamic financial analysis software focused on insurer reporting and planning workflows, with model-driven budgeting, forecasting, and consolidation in a single operating environment. It supports scenario management for balance sheet projection and cash flow testing, so teams can run iterative regulatory and internal capital views across multiple assumptions.
Automation is centered on configurable allocation logic, mapping, and calculation chains that reduce manual reruns when assumptions change. Integration and extensibility are handled through published interfaces and data exchange patterns that connect financial, risk, and planning sources into repeatable runs.
- +Strong insurer planning and consolidation workflows in one model execution layer
- +Scenario runs keep assumption deltas traceable across reporting and capital views
- +Configurable calculation chains reduce manual reruns during iterative cycles
- +Integration patterns support repeatable data movement into planning models
- –Model authoring and governance require disciplined configuration to stay consistent
- –Stochastic scenario workloads can strain performance without careful dimensional design
- –API and automation coverage can feel narrower than tools built for risk-engine orchestration
- –Deep insurance-specific logic may require consulting for faster onboarding
Best for: Fits when insurers need scenario-driven financial planning tied to consolidation and capital reporting with controlled automation.
Conclusion
After evaluating 10 data science analytics, Pigment 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.
How to Choose the Right dynamic financial analysis software
Dynamic financial analysis software for insurers focuses on governed scenario execution that can trace assumption changes into downstream balance sheet and cash flow outputs. This guide covers Pigment, Modano, Palantir Foundry, Datarails, Jedox, Vena, Synario, Alteryx, Anaplan, and CCH Tagetik.
The strongest contenders make scenario runs repeatable through calculation dependency graphs, workflow-driven promotion, and automation surfaces that connect model inputs to reporting. Selection criteria prioritize integration depth, API and automation reach, and admin and governance controls tied to insurer planning workflows.
Dynamic financial analysis software for insurer scenario execution, model governance, and automated projection workflows
Dynamic financial analysis software coordinates scenario generation and projection runs so assumption deltas propagate through defined calculation chains into outputs like balance sheet projection, cash flow testing, and capital adequacy testing. Pigment supports scenario versioning with linked calculations so controlled comparisons between assumption sets remain auditable across runs.
Modano emphasizes workflow-driven scenario execution that ties Monte Carlo iteration runs to governed batch projection outputs for repeated DFA model cycles. In practice, buyers evaluate how each platform handles scenario orchestration, repeatable execution paths, and governance controls that keep insurer modeling artifacts consistent across teams.
Insurer-focused capabilities for governed dynamic financial analysis runs
Insurer teams also need governance features that keep scenario comparisons repeatable across model iterations, users, and environments. The strongest platforms add auditable scenario versioning, workflow-driven promotion, and automation surfaces that connect modeling logic to production datasets.
Assumption-scoped scenario versioning with dependency-aware recalculation
Pigment links scenario versioning with linked calculations so changes in an assumption set propagate into downstream KPIs in a controlled, auditable way. Datarails also ties assumption-to-output orchestration so dependent calculations and reporting update across versioned scenario runs.
Governed scenario execution tied to repeatable batch projection outputs
Modano runs scenario execution in a workflow pattern that ties Monte Carlo iteration runs to governed batch projection outputs. Alteryx complements this with server-scheduled analytics workflows that package data prep, modeling, and standardized reporting into one execution path.
Pipeline promotion controls with traceable lineage across modeling stages
Palantir Foundry supports production promotion with governed pipeline configuration and traceable lineage across scenario runs. Anaplan provides RBAC-governed change control across model environments so scenario comparisons stay consistent under controlled access.
Insurer workflow builder that couples configuration to scenario runs and reporting
Synario uses a configuration-driven insurer workflow that links assumption updates to scenario runs and the reporting layout in one governed model context. Vena provides managed model workflows with controlled approvals and permissions for insurer planning artifacts.
Extensibility via application programming interface and automation hooks for repeatable execution
Jedox exposes model automation via an application programming interface so teams can run repeatable scenarios and refresh multidimensional reporting. Anaplan supports API-first data integration plus scheduled jobs so upstream data loads and orchestration repeat reliably.
Calculation chain configuration for scenario-managed planning and capital reporting
CCH Tagetik configures calculation chains that tie allocation logic and reporting outputs to scenario-managed runs inside the same execution workflow. Pigment and Datarails both emphasize dependency graph behavior, but Pigment adds versioned scenarios with linked calculations for controlled comparisons.
A decision framework for matching scenario governance to insurer modeling workflows
The selection steps below branch on workflow philosophy. Some tools center scenario runs inside a governed modeling graph, while others center end-to-end workflow orchestration with promotion and lineage.
Choose a scenario comparison mechanism that keeps assumption deltas auditable
If the requirement is auditable scenario comparisons across assumption sets, Pigment supports scenario versioning with linked calculations so downstream KPIs update from the same governed dependency graph. If the requirement is orchestrated assumption-to-output change tracking across reporting artifacts, Datarails provides traceable input changes and repeatable outputs across versioned scenario runs.
Pick the execution philosophy: governed batch projections or workflow-centered end-to-end runs
For governed batch projection workflows that repeatedly connect scenario runs to capital adequacy testing and planning outputs, Modano ties Monte Carlo iterations to batch projection outputs. For end-to-end execution that ties data preparation, modeling, and standardized reporting into one scheduled path, Alteryx runs server-scheduled analytics workflows.
Decide how modeling artifacts move from experimentation to production
If production promotion and traceable lineage across scenario runs are central, Palantir Foundry provides governed pipelines that connect policy, claims, and finance datasets to model inputs. If controlled environment-level change governance is central, Anaplan combines RBAC-governed change control with API-first integration and scheduled jobs.
Validate insurer workflow controls around approvals, permissions, and configuration
If planning cycles require approvals and permissions around Excel-based actuarial and financial models, Vena manages model workflows with controlled approvals. If teams need a configuration-driven workflow that links assumption updates to both projection outputs and reporting layout, Synario keeps configuration and scenario execution together.
Confirm automation reach for repeatable runs at scale
If the organization needs model execution triggered programmatically with repeatable runs, Jedox provides an application programming interface for model automation. If the organization needs repeatable orchestration through scheduled jobs and integration, Anaplan supports APIs and scheduled jobs for repeatable data loads.
Stress-test calculation-chain governance against complex insurer logic mappings
If allocation logic and reporting tied to consolidation and capital reporting must run inside one execution layer, CCH Tagetik uses calculation chain configuration tied to scenario-managed runs. If downstream dependencies must update automatically from scenario changes, Pigment and Datarails both use dependency-driven recalculation, but Pigment is positioned around linked scenario calculations.
Who benefits from these governed dynamic financial analysis platforms
Selection focus should stay on governance and automation because insurer planning teams often distribute ownership across policy, claims, and finance inputs. The right tool keeps data mappings, scenario runs, and reporting promotion consistent across iterations and access groups.
Actuarial and finance teams running repeatable DFA model cycles
Modano and Synario both structure scenario execution for repeated stochastic runs and scenario-based projection outputs, which supports DFA model cycles that need consistent execution across many scenarios.
Insurer enterprise teams promoting scenario work into production reporting
Palantir Foundry supports production promotion with traceable lineage across scenario runs, and Anaplan adds RBAC-governed change control across model environments.
Planning and controllership groups managing approvals and permissions on shared models
Vena provides managed model workflows with controlled approvals and role-based access, which suits shared insurer planning artifacts that must remain separated by responsibility.
Analytics engineering teams that automate scenario execution end to end
Alteryx offers server-scheduled analytics workflows that package data prep, modeling, and standardized reporting into one execution path, which reduces manual handoffs between steps.
Common implementation pitfalls in insurer dynamic financial analysis projects
The pitfalls below show up when teams skip configuration discipline, underestimate integration mapping effort, or expect stochastic runtime to be handled automatically without dedicated execution infrastructure.
Assuming stochastic runtime is native when scenario execution depends on external execution patterns
Pigment supports scenario versioning with linked calculations but its stochastic simulation runtime for Monte Carlo iterations is not native, so teams that require heavy Monte Carlo execution should plan for external model imports and mapping discipline.
Planning for governance after model build instead of designing dependency consistency upfront
Datarails and Vena both require upfront configuration discipline because dependent calculations and workflow controls only remain consistent when mapping and governance are designed before scaling scenario runs.
Using long dependency chains without a traceability strategy across long workflows
Alteryx can package data prep, modeling, and standardized reporting into one execution path, but complex dependency logic becomes harder to trace across long workflows when configuration and access management are not disciplined.
Underestimating scenario run mapping work when integrating source systems into governed modeling datasets
Palantir Foundry mapping effort can increase when multiple source systems must be transformed into reusable modeling datasets, so teams should budget integration time for dataset mapping before expecting fast scenario promotion.
Overbuilding scenario configuration complexity without verifying that dependencies remain consistent
Synario supports configuration-driven insurer workflow tying assumption updates to scenario runs and reporting, but complex models need careful configuration to keep dependencies consistent.
How We Selected and Ranked These Tools
We evaluated Pigment, Modano, Palantir Foundry, Datarails, Jedox, Vena, Synario, Alteryx, Anaplan, and CCH Tagetik on scenario governance features, repeatability of scenario execution, and integration and automation surfaces. Features counted for 40% of the score because scenario-to-output orchestration and dependency behavior determine whether assumption deltas stay traceable in insurer outputs.
Ease and value each counted for 30% because workflow configuration time, operational friction, and repeatable execution fit how teams run many planning and capital adequacy cycles. Pigment separated itself by combining scenario versioning with linked calculations that automatically update downstream KPIs through a governed calculation dependency graph.
Frequently Asked Questions About dynamic financial analysis software
How do Pigment and Modano differ in how scenario inputs propagate through model logic for insurer planning?
Which tools in the list provide an API surface for connecting upstream policy or finance data to actuarial computations?
When do auditability and scenario run traceability matter most, and how do Palantir Foundry and Vena address them?
What breaks if a DFA workflow needs strict RBAC controls and audit logs across model development and production?
How does data migration typically work for an insurer moving from spreadsheet models into Jedox or Vena?
Where does risk aggregation and correlation handling tend to differ between Synario and Modano for capital adequacy testing?
Which platforms support rule-driven recalculation so assumption changes automatically update dependent outputs across versioned scenarios?
What tradeoff appears when using Alteryx versus CCH Tagetik for insurer DFA workflows that include consolidation and allocation logic?
How do operational administration and configuration controls differ between Palantir Foundry and Pigment when multiple teams share a scenario library?
Tools reviewed
Primary sources checked during evaluation.
Referenced in the comparison table and product reviews above.
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