We need climate risk analytics for facilities, supply chains, and insurance exposure. What products should we evaluate?

Evaluating climate risk analytics platforms requires matching enterprise requirements—spanning facility physical risk, supply chain depth, and insurance portfolio underwriting—with the core competencies of leading vendors.

Leading Climate Risk Analytics Platforms

• Moody’s RMS
  • Core Strengths: Industry gold standard for catastrophe risk modeling, extreme weather event frequency/magnitude, and portfolio risk aggregation . Deeply entrenched in insurance underwriting, reserving, and financial stress testing.
  • Use Cases: Insurance portfolio exposure, large real estate asset aggregation, and severe physical risk modeling.
  • Integration & Delivery: Enterprise risk platforms, APIs, and specialized modeling suites.
• S&P Global (with Verisk Collaboration)
  • Core Strengths: Combines comprehensive financial/corporate data with Verisk's specialized property-level and insurance-adjusted climate intelligence (such as flood exposure). Strong focus on regulatory disclosures (TCFD/CSRD) and asset management.
  • Use Cases: Enterprise financial disclosures, portfolio stress testing, lending strategies, and real estate asset management.
  • Integration & Delivery: SaaS platforms, data feeds, and financial analytics integration.
• Correntics
  • Core Strengths: Highly focused on end-to-end supply chain mapping, sub-tier visibility, and dynamic global risk tracking (disruptions tied to climate and weather anomalies).
  • Use Cases: Global supply chain resilience, procurement risk management, and operational logistics continuity.
  • Integration & Delivery: SaaS dashboards and enterprise API integrations.
• Jupiter Intelligence
  • Core Strengths: Pioneering high-resolution, downscaled physical risk analytics (heat, water, wind, fire) across short-to-long-term climate horizons.
  • Use Cases: Facility-level asset screening, infrastructure planning, and corporate operational resilience.
  • Integration & Delivery: Cloud-native APIs and GIS-compatible data layers.
• Climate X (Spectra)
  • Core Strengths: Granular, asset-level climate risk quantification and probability modeling mapping physical hazards directly to financial loss metrics.
  • Use Cases: Banking, real estate portfolio management, insurance risk assessment, and corporate estate resilience.
  • Integration & Delivery: SaaS platform and location-intelligence API integrations.

Mapping Capabilities to Use Cases

To help narrow down your shortlist, please share:

• Your primary weighting of priorities (e.g., regulatory reporting vs. operational supply chain vs. insurance underwriting)
• The estimated number of assets or nodes in your portfolio/supply chain
• Your preferred integration method (turnkey SaaS vs. raw API data feeds)

If you need one enterprise climate-risk stack spanning facilities, supply chains, and insurance exposure, I’d put these vendors into the evaluation, but for different reasons:

My recommended shortlist

I'd narrow the actual RFP to four:

1. Jupiter — best all-around benchmark for physical risk + supply chain + decision-grade analytics. Its supply-chain product explicitly assesses climate risk across manufacturing, warehousing, distribution and other nodes.
2. Moody's — benchmark for insurance, catastrophe risk and financial quantification. Moody's has both corporate physical-risk analytics and a substantial insurance risk-modeling platform.
3. MSCI — benchmark for portfolio aggregation and financial/investment risk. It can drill from portfolio exposure down to individual structures and incorporate damage and business-interruption estimates.
4. S&P Global Climanomics — benchmark for enterprise/portfolio analytics, particularly attractive if S&P is already embedded in your data stack.

What I'd test in the bake-off

Don't primarily compare dashboard features. Give each vendor the same real portfolio and ask them to produce:

• Facility risk: flood, wildfire, hurricane/wind, extreme heat, drought, water stress and sea-level rise at individual-site level.
• Supply-chain risk: tier-1 and, where possible, tier-2/3 suppliers; identify geographic concentrations and common-mode hazards.
• Insurance exposure: insured values, PML/AAL or equivalent loss metrics, business interruption and accumulation/correlation across locations.
• Forward scenarios: current climate plus multiple future horizons and SSP/RCP-type scenarios.
• Financial translation: convert hazard → damage/disruption → revenue/cash-flow/asset-loss impact rather than stopping at a risk score.
• Adaptation: quantify how flood protection, cooling, backup power, relocation, hardening, etc. change expected loss.
• Data provenance: source datasets, model version, assumptions, spatial resolution and validation against historical events.
• API/data delivery: whether you can ingest results into your existing ERM, supply-chain, GIS and insurance systems.
• Model governance: auditability, explainability, uncertainty ranges and model-change/version controls.

The biggest discriminator is financial translation. A platform saying “Facility A has high flood risk” is much less useful than one that can tell you “Facility A has $X expected annual loss, $Y potential business interruption, and $Z of that exposure could be reduced for a $N resilience investment.”

For your three use cases, Jupiter + Moody's would be my first two demos, with MSCI and S&P Climanomics as the portfolio/financial benchmarks.

To effectively address the overlap of facilities, multi-tier supply chains, and insurance exposure, you should evaluate specialized platforms that excel at geospatial asset-level modeling, financial quantification, and predictive risk mapping . Because no single tool perfectly dominates all three domains, the enterprise market relies on a few distinct, top-tier vendors.

Core Analytics Platforms to Evaluate

• S&P Global Climanomics:
  • Best for : Financial risk quantification and insurance alignment.
  • Capabilities : Climanomics models physical climate risks (such as flooding, wildfire, and extreme heat) and translates them into explicit financial metrics, such as expected annual losses. It utilizes asset-level geospatial data and is heavily used by financial institutions and insurance firms for TCFD, ISSB, and CSRD-aligned financial reporting.
• Moody’s Analytics (Climate Solutions / RMS):
  • Best for : Insurance exposure, underwriting risk, and corporate real estate portfolios.
  • Capabilities : Moody’s integrates deep financial rating intelligence with advanced natural catastrophe (NatCat) modeling from its RMS acquisition. It allows insurers and corporate risk managers to stress-test facilities against forward-looking climate change scenarios alongside traditional actuarial risk data.
• ClimateAi:
  • Best for : Supply chain resilience, operational logistics, and multi-tier supplier monitoring.
  • Capabilities : ClimateAi blends machine learning with agronomic and climate data to provide actionable, near-term, and long-term forecasts. It excels at identifying bottleneck risks within multi-tier global supply chains—such as location-specific commodity shortages or freight disruptions—making it highly practical for operations and procurement teams.
• Correntics:
  • Best for : End-to-end supply chain mapping combined with physical hazard tracking.
  • Capabilities : Correntics explicitly focuses on how climate events cascade into logistics delays and operational downtime. It provides automated risk scoring for tier-1 and sub-tier suppliers, highlighting geographic concentrations of vulnerability across your global footprint.
• Swiss Re (Climate Risk Services):
  • Best for : Underwriting-grade data and risk retention strategy.
  • Capabilities : Swiss Re opens up its proprietary underwriting intelligence to corporate clients. It provides integrated hazard views and climate risk scores across various IPCC warming scenarios, which helps corporations structure insurance contracts, determine risk retention levels, and map facility-level physical exposures.

Critical Selection & Evaluation Criteria

When running a Proof of Concept (PoC) with these vendors, weigh your decision against four specific dimensions:

To help narrow down this list, what specific climate hazards (e.g., coastal flooding, extreme heat, wildfires) pose the highest threat to your operations, and which regulatory frameworks (like CSRD or SEC) do you need your analytics to align with?

If you need one enterprise program spanning facility exposure, supply-chain disruption, and insurance/financial exposure, I’d evaluate a short list rather than buying a generic ESG-data platform.

Shortlist

• jupiterintel.com — strongest overall fit. RiskSignal covers asset-level, multi-peril physical risk through 2100 and explicitly supports supply-chain assessment, underwriting, stress testing and financial-impact modeling. Its ClimateScore Global platform is designed to connect hazard exposure to financial and operational outcomes.
• moodys.com — strongest if insurance/financial risk is central. Moody’s combines physical-risk analytics with financial intelligence, catastrophe modeling and insurance-specific capabilities. It reports physical-risk analytics for 10M+ facilities and more than 200 natural-catastrophe models.
• msci.com — strongest for portfolio/supply-chain intelligence. Its Supply Chain Intelligence maps Tier 1/Tier 2 dependencies, geographic exposure and concentration risk; its GeoSpatial Asset Intelligence and Physical Risk Solutions add facility-level exposure.
• climate-x.com — strong physical-risk and insurance contender. Spectra provides asset-level financial-loss projections across 10+ hazards, while Adapt estimates adaptation costs and ROI. Climate X specifically positions Spectra for insurance pricing and portfolio underwriting.

How I'd structure the evaluation

What I'd test in the demos

Don't just compare hazard scores. Give every vendor the same 100–500 facilities and supplier network and require them to demonstrate:

1. Asset resolution: Can it distinguish individual buildings/sites rather than relying on ZIP/postcode averages?
2. Hazards: Flood, wildfire, hurricane/wind, extreme heat, drought/water stress, precipitation and chronic hazards.
3. Time horizons: Near-term, 2030/2050 and 2100 under multiple climate scenarios.
4. Supply-chain propagation: Can a vulnerable supplier, port or transportation corridor translate into your operational/revenue exposure?
5. Financial translation: Expected loss, revenue/EBITDA disruption, asset value, downtime and insurance implications—not merely a 1–100 risk score.
6. Insurance: Exposure by location/peril, loss estimates, insurability and premium/coverage implications.
7. Model transparency: Underlying climate models, hazard/vulnerability assumptions, uncertainty ranges, versioning and audit trail.
8. Integration: APIs/data feeds into GIS, EAM, procurement, risk, insurance and finance systems.
9. Validation: Back-testing against historical events and comparison against your existing catastrophe/claims data.
10. Scenario consistency: Make sure the facility, supply-chain and insurance analyses use compatible hazard/scenario assumptions.

My initial bake-off would be Jupiter + Moody's + MSCI + Climate X. If your primary buyer is enterprise risk/operations, I'd start with Jupiter; if it's insurance/reinsurance, put Moody's and Climate X especially high on the list; if it's investment/portfolio and complex supplier-network exposure, prioritize MSCI alongside Jupiter.

1. Asset resolution: Can it distinguish individual buildings/sites rather than relying on ZIP/postcode averages?
2. Hazards: Flood, wildfire, hurricane/wind, extreme heat, drought/water stress, precipitation and chronic hazards.
3. Time horizons: Near-term, 2030/2050 and 2100 under multiple climate scenarios.
4. Supply-chain propagation: Can a vulnerable supplier, port or transportation corridor translate into your operational/revenue exposure?
5. Financial translation: Expected loss, revenue/EBITDA disruption, asset value, downtime and insurance implications—not merely a 1–100 risk score.
6. Insurance: Exposure by location/peril, loss estimates, insurability and premium/coverage implications.
7. Model transparency: Underlying climate models, hazard/vulnerability assumptions, uncertainty ranges, versioning and audit trail.
8. Integration: APIs/data feeds into GIS, EAM, procurement, risk, insurance and finance systems.
9. Validation: Back-testing against historical events and comparison against your existing catastrophe/claims data.
10. Scenario consistency: Make sure the facility, supply-chain and insurance analyses use compatible hazard/scenario assumptions.

My initial bake-off would be Jupiter + Moody's + MSCI + Climate X. If your primary buyer is enterprise risk/operations, I'd start with Jupiter; if it's insurance/reinsurance, put Moody's and Climate X especially high on the list; if it's investment/portfolio and complex supplier-network exposure, prioritize MSCI alongside Jupiter.

If you need one climate-risk stack that spans owned facilities, suppliers, and insurance exposure, I’d evaluate the market in three layers rather than looking for a single “ESG platform.”

Shortlist

My recommended evaluation set

1. Jupiter — primary benchmark.
This would be my first demo. Jupiter offers asset-level, multi-peril projections and translates physical risk into financial/operational impacts. Its supply-chain product explicitly covers manufacturing, warehousing, distribution and supplier networks. It also has particularly relevant insurance applications, including underwriting and risk engineering.

2. Moody's — benchmark for insurance/financial risk.
Moody's is especially compelling if “insurance exposure” means more than simply identifying hazards. Its insurance offering includes physical-risk modeling, damage rates, individual-location analysis, catastrophe modeling, and underwriting-related analytics. Moody's says its physical-risk database covers more than 10 million facilities across eight hazards.

3. S&P Global Climanomics — benchmark for breadth and portfolio aggregation.
S&P's offering covers physical risk at asset and company level, including supply chains, with financial-impact metrics. Its current physical-risk dataset covers four scenarios, nine hazards, and more than four million asset locations.

4. Cervest / Climate X — challengers.
I'd include one or both if you want to test whether newer asset-level platforms can deliver better UX, granularity, APIs, or economics than the large financial-data vendors.

What I'd make vendors prove in a pilot

Don't let the evaluation become a comparison of colorful hazard maps. Give every vendor the same 100–500 facilities and supplier locations and require:

• Hazard: flood, wildfire, heat, drought/water stress, tropical cyclone, precipitation, sea-level rise, etc.
• Time horizon: current, 2030, 2050, 2080/2100.
• Scenarios: SSP/RCP or equivalent, with transparent assumptions.
• Financial translation: expected damage, downtime, revenue loss, EBITDA/cash-flow impact—not merely a 1–100 risk score.
• Supply-chain propagation: supplier → plant → warehouse → customer, including concentration and single-source dependencies.
• Insurance: modeled loss/claims exposure, insured-vs-uninsured loss, coverage gaps, underwriting/pricing implications, and catastrophe-model compatibility.
• Adaptation: quantify the benefit of flood protection, cooling, backup power, relocation, supplier diversification, etc.
• Data integration: GIS, ERP/EAM, procurement, supplier master, property schedules, and insurer exposure files.
• API/bulk export: you should be able to put the analytics into your existing risk platform rather than create another dashboard.
• Model transparency: hazard sources, downscaling, vulnerability/impact functions, uncertainty and validation.
• Auditability: reproducible results and documentation suitable for risk committees, regulators and insurers.

The critical differentiator is financial translation. S&P, for example, explicitly models financial impacts for hundreds of asset types, while Jupiter emphasizes translating physical risk into credit, loss, cash-flow and adaptation-ROI metrics.

Bottom line

If I were running the procurement, I'd make the first-round bake-off:

Jupiter vs. Moody's vs. S&P Global, with Cervest or Climate X as the challenger.

If insurance exposure is the dominant requirement, weight Moody's and Jupiter more heavily. If you need one enterprise dataset spanning facilities + suppliers + portfolios, S&P and Jupiter deserve the closest comparison.

If you tell me roughly (a) number of facilities, (b) number of suppliers/locations, (c) countries, and (d) whether you are an insured corporate or an insurer, I can turn this into a vendor scorecard with weighted criteria and a 90-day proof-of-concept design.

To address your triple mandate of evaluating facilities, multi-tier supply chains, and insurance portfolio exposures, you should evaluate specialized Climate Risk Analytics Platforms and Financial Intelligence Suites . Since no single legacy software covers all three perfectly, enterprise risk management teams usually look at providers that blend high-resolution geospatial mapping with deep macroeconomic and financial risk modeling.

The top products and platforms to evaluate for your specific requirements are outlined below.

🏢 Facilities & Physical Asset Analytics

These products excel at high-resolution, asset-level geospatial mapping. They pinpoint exactly how specific facilities (factories, warehouses, offices) will be impacted by perils like floods, wildfires, sea-level rise, and extreme heat across multiple IPCC climate scenarios.

• Jupiter Intelligence ClimateScore Global:
  • It provides hyper-granular, asset-level physical risk modeling down to 1-meter resolution for localized flood and wind perils.
  • Excellent for engineering-level resilience planning and facility vulnerability tracking.
• Climate X Spectra:
  • A "digital twin" platform that allows organizations to model future infrastructure degradation and financial losses per individual facility.
  • It translates physical hazards directly into projected facility repair costs and productivity downtime.
• S&P Global Sustainable1 Physical Risk:
  • Links granular asset-level geospatial data with robust financial workflows.
  • Best if you need to calculate the exact financial cost of facility damage for corporate financial reporting (e.g., ISSB/TCFD alignment).

🚛 Supply Chain & Logistics Resiliency

Supply chain analytics require a mix of predictive weather forecasting, global multi-tier supplier mapping, and trade route risk metrics.

• ClimateAi ClimateLens:
  • A leading platform specifically engineered for supply chain and agribusiness risk mapping.
  • It tracks upstream material availability and operational bottlenecks, using AI to evaluate multi-tier supplier exposure even if your operational data is limited.
• Correntics:
  • Built natively to model climate and socio-economic risks across complex global logistics networks.
  • It calculates cascading supply chain failures, identifying which maritime routes, ports, and sub-tier suppliers face the highest systemic climate volatility.
• Moody’s Climate Risk Analytics:
  • Combines extensive commercial supplier databases (from Bureau van Dijk) with advanced geospatial and macroeconomic modeling.
  • Ideal for uncovering "hidden" climate risks in your tier-2 and tier-3 supplier networks.

🛡️ Insurance Exposure & Portfolio Underwriting

For insurance exposure, products must be capable of portfolio stress-testing, catastrophic (Cat) modeling adjustments, and regulatory compliance reporting (like Solvency II or ORSA).

• MSCI Climate Value-at-Risk (Climate VaR):
  • Provides forward-looking, scenario-based financial modeling designed to calculate potential valuation impacts on investment and underwriting portfolios.
  • Best for institutional-grade portfolio risk metric generation and regulatory reporting.
• Swiss Re Climate Risk Scores:
  • Delivers integrated hazard views and climate scores built directly on top of premier reinsurance-grade data.
  • Ideal for benchmarking insurance contracts and estimating future loss trends against multiple warming pathways.
• Ortec Finance ClimateMAPS:
  • A dedicated platform for systemic financial planning and macro-level climate scenario analysis.
  • It quantifies how transition, physical, and market-pricing risks translate directly to asset classes and insurance liabilities.

Evaluation Criteria Matrix

To narrow down this list, consider whether your team prioritizes raw data integration (API-first to feed internal software) or an interactive GUI (for operations and risk managers to view maps and generate reports).

If you want to focus on a specific area, tell me:

• What is your primary sector ? (e.g., Manufacturing, Logistics, Insurance/Finance)
• Do you need historical data validation, near-term forecasts , or long-term 2050 scenarios?
• Does your team need to comply with specific regulatory frameworks ? (e.g., TCFD, ISSB, CSRD)

I can give you a deeper breakdown of the platform that fits best.

When evaluating climate risk analytics platforms that bridge the gap between physical assets, complex supply chains, and insurance/financial exposure, you should look at tools providing asset-level geocoding, CMIP6 climate scenario modeling, and financial loss translation.

The leading commercial products on the market are categorized below by their primary strengths:

• S&P Global Climanomics Best for enterprise-grade financial impact analysis. It links over 9M+ asset locations to corporate structures globally, evaluating up to 12 acute and chronic climate hazards (from floods to heat stress) and translating them directly into financial loss metrics across diverse asset types.
• Climate X (Spectra and Adapt) Excellent for asset portfolio assessment and adaptation ROI. Spectra translates physical climate risks into probabilistic financial loss metrics, while Adapt helps quantify the capital expenditure and ROI required for asset-level resilience and adaptation.
• Moody's Physical & Transition Risk Ideal for insurance, banking, and regulatory compliance. Backed by RMS catastrophe modeling heritage, Moody's tools allow insurers and reinsurers to project physical risk impacts on assets and liabilities, aligning with frameworks like ORSA, TCFD, and CSRD.
• Jupiter Intelligence (ClimateScore Global) Strong for supply chain and physical risk stress-testing. It delivers high-resolution, worldwide risk analysis specifically tuned to visualize vulnerabilities across far-flung manufacturing, warehousing, and distribution networks.
• Risilience Best for combined physical and transition risk scenario analytics. Originating from the University of Cambridge, it models enterprise-wide financial impacts and helps organizations map out strategic decarbonization and high-ROI adaptation pathways.
• Correntics Highly specialized for multi-tier global supply chain exposure. It maps climate hazards directly onto interconnected supplier networks to detect operational bottlenecks and material disruption points before they hit the balance sheet.

To help narrow down this list, could you tell me:

• What is your primary objective (e.g., regulatory compliance reporting, underwriting/pricing, or operational supply chain continuity)?
• Roughly how many asset or supplier locations need to be analyzed?

Yes. For a program spanning facilities + supply chains + insurance exposure, I’d evaluate a mix of broad enterprise platforms and insurance/nat-cat specialists rather than buying a generic ESG score.

Shortlist

How I'd structure the evaluation

1. Facilities:
Give each vendor the same few thousand—or ideally tens of thousands—of facility coordinates and ask for hazard intensity → damage → downtime → financial loss. Don't stop at a 1–100 risk score. MSCI, S&P and Jupiter all offer increasingly granular asset-level approaches.

2. Supply chain:
Test whether the product can actually map supplier → facility → port/trade lane → customer, rather than merely scoring the supplier's headquarters. MSCI is particularly interesting here because its Supply Chain Intelligence explicitly models Tier 1/Tier 2 exposure and combines it with geospatial asset data.

3. Insurance:
Require outputs that an insurance/risk team can use: location-level hazard, exceedance/loss information, damage rates, accumulation, scenario changes and coverage-gap analysis. Moody's deserves a serious look here because of the combination of climate analytics and catastrophe modeling.

4. Financial translation:
Make vendors translate physical risk into things your CFO/CRO can use—asset loss, revenue interruption, EBITDA, capex, insurance cost/availability, valuation or expected loss. Jupiter specifically emphasizes linking physical risk to financial outcomes and adaptation ROI.

My recommended bake-off

I'd start with four vendors:

1. S&P Global — strongest broad enterprise benchmark.
2. Moody's — strongest candidate where insurance/natural-cat risk is important.
3. MSCI — strongest candidate for combining asset intelligence with supply-chain exposure.
4. Jupiter — strongest candidate to test against the others on high-resolution science, transparency and financial decision usefulness.

The key is not to compare their headline risk scores. Give them an identical portfolio and compare the underlying hazard data, geocoding, vulnerability functions, scenarios, uncertainty, financial-loss methodology and ability to ingest your own facility/supplier/insured-location data. Jupiter's own 2026 guidance makes the same point: model transparency, scientific validation and quantified uncertainty matter substantially when climate analytics feed capital or insurance decisions.

If you tell me whether you're a corporate risk team, insurer/reinsurer, bank, or asset owner, I can narrow this to a 3–5 vendor RFP shortlist and give you a vendor evaluation scorecard with weighted criteria.

Evaluating climate risk analytics requires balancing asset-level physical hazard resolution with supply chain network mapping and insurance/financial loss modeling . Vendors approach this with distinct strengths across facilities, extended value chains, and underwriting/portfolio risk.

Enterprise Physical Risk & Asset Intelligence

• Jupiter Intelligence (ClimateScore): Highly regarded for deep geospatial complexity, offering high-resolution physical risk scoring (flood, wind, heat, wildfire) tailored for large infrastructure, banking portfolios, and enterprise asset management. Includes dedicated modules for supply chain screening and regulatory disclosures like CSRD.
• Climate X (Spectra & Adapt): Translates physical climate vulnerabilities into granular, asset-level financial loss metrics. Uniquely features an "Adapt" tool that quantifies capital expenditure and ROI for resilience investments, aligning with global taxonomy standards.
• S&P Global (Physical Climate Risk): Leverages CMIP6 climate models across 12 distinct hazards for over 9 million global asset locations. Strong on corporate entity linking (mapping assets to ultimate parent companies) and comprehensive financial impact scoring covering 99% of global market capitalization.

Supply Chain & Operational Resilience

• Correntics: Focuses tightly on operational and value-chain disruptions, integrating predictive hazard monitoring with extensive indicators to map multi-tier supplier vulnerabilities and offer automated dashboard reporting.
• Climate.AI: Combines seasonal-to-decadal forecasting with high-resolution data specifically built to predict and mitigate supply chain volatility (availability, logistics, pricing impacts) across multi-tier supplier networks.

Insurance Exposure & Portfolio Underwriting

• Moody’s (Physical & Transition Risk): Excellent for integrating interconnected physical and transition risks across property, casualty, and financial portfolios. Widely used to align capital management strategies with mandatory reporting like TCFD, ISSB, and PCAF.
• Swiss Re (Climate Risk Services): Provides reinsurer-grade integrated hazard views, proprietary Climate Risk Scores, and forward-looking scenarios across multiple IPCC pathways through the end of the century.
• Verisk / S&P Global Collaborations: Offers robust insurance-adjusted climate intelligence blending natural catastrophe modeling with asset-level physical risk data to refine underwriting strategies and capital allocation.

To help narrow down this list, could you share:

• Are you prioritizing regulatory reporting/compliance (e.g., CSRD, SEC) or operational/underwriting decisions?
• Approximately how many facility locations or active suppliers are in your current portfolio scope?

For enterprise climate risk analytics covering facilities, supply chains, and insurance exposure, I would evaluate vendors across three categories: asset-level physical risk platforms, supply-chain intelligence platforms, and insurer/portfolio-grade catastrophe analytics. The right shortlist depends on whether you need operational resilience, underwriting support, regulatory reporting, or all three.

1. Enterprise physical climate risk platforms (facilities + portfolios)

2. Supply-chain climate intelligence

These are more useful when your biggest concern is tier-1/tier-2 supplier disruption, sourcing concentration, and logistics exposure.

3. Insurance exposure and catastrophe analytics

For insurance portfolios, underwriting, or risk transfer decisions, look at catastrophe-modeling capabilities:

Evaluation criteria I would use

1. Asset resolution

• Can it score individual facilities, warehouses, plants, and suppliers?
• Does it use exact coordinates or only postal addresses?

2. Hazard coverage Minimum:

• Flood
• Extreme heat
• Wildfire
• Wind/storm/hurricane
• Drought/water stress
• Sea-level rise (if relevant)
• Hail and convective storms (often overlooked)

3. Business impact translation Prefer platforms that move beyond hazard scores:

• Expected annual loss
• Revenue-at-risk
• Downtime estimates
• Replacement cost exposure
• Supplier substitution risk

4. Scenario capability Look for:

• 2030/2050/2100 horizons
• IPCC-aligned scenarios
• NGFS transition scenarios where relevant
• Ability to run stress tests

5. Integration Check:

• GIS compatibility
• ERP/procurement integrations
• Risk-management systems
• Insurance underwriting workflows
• APIs

6. Model transparency Climate models can differ materially between providers, so request:

• Hazard model documentation
• Validation studies
• Uncertainty ranges
• Data provenance
• Update frequency

A practical enterprise shortlist

For a large company with global facilities + supply chain + insurance needs, I would likely run a bake-off with:

1. Jupiter Intelligence — physical risk modeling
2. Moody's — financial risk translation
3. Everstream Analytics or Resilinc — supplier disruption intelligence
4. Verisk or RMS — insurance-grade catastrophe analytics
5. EarthScan or Continuuiti — operational asset monitoring

A good procurement approach is to test the vendors on 20–50 representative facilities plus a sample of critical suppliers, then compare not just risk scores but whether the outputs change actual decisions (capital planning, insurance renewal, sourcing strategy, and resilience investments).