Clarity AI Teams Up with RiskThinking .ai to Add Asset‑Level Climate Risk Data to Its ESG Platform

Clarity AI, the London‑based provider of extra‑financial intelligence, announced a strategic partnership with RiskThinking .ai, a specialist in asset‑level physical climate risk modelling. The collaboration will embed RiskThinking .ai’s Climate Digital Twin™ data and modelling capabilities into Clarity AI’s ESG analytics suite, giving institutional investors and corporations granular visibility into climate‑related hazards for more than three million individual assets across roughly 15,000 ultimate parent companies.

The move arrives at a time when regulators worldwide are tightening disclosure requirements and investors are demanding higher‑resolution data to assess climate exposure. By marrying top‑down ESG scores with bottom‑up physical risk metrics, the combined offering aims to bridge a long‑standing data gap that has limited the practical utility of many sustainability tools.

The partnership in detail

The agreement, disclosed on 26 March 2026, does not involve a cash transaction but focuses on technology integration and joint product development. RiskThinking .ai’s Climate Digital Twin™ platform runs full hydrologic simulations for every recognized climate scenario and warming level, producing high‑fidelity physical risk outputs such as flood depth, heat stress, and sea‑level rise projections. These outputs will be ingested by Clarity AI’s platform, which already aggregates ESG data, corporate disclosures, and AI models for its client base that collectively oversees more than $80 trillion in assets under management (AUM).

• Asset‑level granularity – Latitude and longitude coordinates for millions of facilities, ranging from manufacturing plants and data centres to retail outlets and corporate headquarters.
• Physical risk modelling – Bottom‑up simulations that quantify exposure to climate hazards under multiple scenarios, without relying on proxy or sector‑average assumptions.
• Enhanced nature and biodiversity analysis – Geospatial overlays that reveal how corporate footprints intersect with ecologically sensitive zones.
• Regulatory‑ready tools – Features designed to meet the reporting standards set by the EU Sustainable Finance Disclosure Regulation (SFDR), the Task Force on Climate‑Related Financial Disclosures (TCFD), and emerging national frameworks.

The integration will be accessible through Clarity AI’s web application, API endpoints, and a suite of connectors that support third‑party workflows. The company also highlighted the availability of AI agents that can query the combined dataset in natural language, a functionality that aligns with the growing trend of conversational analytics in finance.

Technical integration: From climate twins to ESG dashboards

RiskThinking .ai’s Climate Digital Twin™ is a cloud‑native platform that ingests climate model outputs from sources such as the Intergovernmental Panel on Climate Change (IPCC) and runs them through high‑resolution hydrologic and thermodynamic models. The resulting risk layers are then mapped to the precise coordinates of corporate assets supplied by Clarity AI’s data partners.

Clarity AI’s engineering team will standardise the geospatial data, resolve any duplicate records, and enrich the asset list with corporate hierarchy information. This enables end‑users to roll up asset‑level risk metrics to the level of subsidiaries, business units, or the ultimate parent company, preserving the analytical flexibility required for portfolio‑level stress testing.

The combined solution also leverages Clarity AI’s native AI models, which can flag anomalous risk patterns, suggest mitigation actions, and generate narrative insights for reporting. By integrating the two platforms, the partnership promises a seamless workflow: a user can start with a high‑level ESG score, drill down to the physical risk exposure of a specific factory, and then retrieve a concise, AI‑generated explanation for inclusion in a board presentation.

Why asset‑level data matters now

Institutional investors have long complained that ESG scores, while useful for benchmarking, often mask the heterogeneity of risk within a company’s asset base. A utility with a strong carbon‑intensity rating may still own a handful of power plants located in flood‑prone river basins. Conversely, a low‑carbon tech firm could have data centres situated in regions vulnerable to heatwaves.

“The market is moving quickly from aggregated disclosures to the need for audit‑ready, asset‑specific evidence,” said Rebeca Minguela, CEO and Founder of Clarity AI. “While top‑down models provide an essential high‑level perspective, our partnership with RiskThinking .ai adds the granular detail required for rigorous audit and risk analysis. Our solutions, available as a standalone web app, through AI agents and through integrations such as API, MCP, and connectors, empower our clients to see the full picture of how climate and nature affect their portfolios.”

RiskThinking .ai’s founder, Dr. Ron Dembo, echoed the sentiment, emphasizing the immediacy of climate risk: “Climate risk is no longer a future concern; it is repricing assets, straining insurance markets, and reshaping investment decisions right now. What has been missing is the scientific rigour to quantify that risk at the asset level, across every scenario, without shortcuts. By partnering with Clarity AI, we are now accelerating access to that level of precision across a broader set of financial institutions at scale — giving them the fidelity they need to make decisions they can defend.”

These statements reflect a broader industry shift. According to a recent survey by the Global Sustainable Investment Alliance, over 70 % of asset managers plan to incorporate physical climate risk data into their investment processes within the next 12 months. At the same time, insurers are tightening underwriting criteria for assets exposed to high‑impact events, creating a cross‑sector demand for the type of granular data the Clarity‑RiskThinking alliance promises to deliver.

Regulatory backdrop

Regulators in Europe, the United States, and parts of Asia are converging on a common theme: firms must disclose not only carbon emissions but also the physical exposure of their assets to climate hazards. The European Union’s SFDR requires detailed reporting on both transition and physical risks, while the U.S. Securities and Exchange Commission (SEC) is drafting rules that would compel public companies to disclose climate‑related asset vulnerabilities.

In this environment, the partnership’s “Regulatory Readiness” component could be a decisive differentiator. By providing scenario‑based risk metrics that align with IPCC pathways, the combined platform helps clients map their exposures to the temperature thresholds (e.g., 1.5 °C, 2 °C) that regulators are increasingly using as benchmarks for stress testing. Moreover, the biodiversity overlays respond to growing expectations under the EU’s Biodiversity Strategy, which calls for companies to assess and mitigate impacts on protected habitats.

Competitive landscape

Clarity AI is not the first player to integrate physical climate data into an ESG platform. Competitors such as MSCI, Sustainalytics, and S&P Global have launched proprietary climate‑risk modules, often based on third‑party datasets like the Climate Central or the World Resources Institute. However, many of these solutions rely on sector‑average assumptions or coarse geographic aggregations, limiting their usefulness for asset‑level decision‑making.

RiskThinking .ai’s niche lies in its “digital twin” approach, which simulates climate dynamics at a resolution comparable to that used by municipal flood‑risk planners. By combining this with Clarity AI’s extensive corporate dataset, the partnership offers a level of precision that could set a new benchmark for the market.

Potential impact on asset managers

For large asset managers, the ability to drill down from a portfolio’s aggregate ESG score to the risk profile of individual facilities could reshape several core processes:

1. Portfolio construction – Managers can exclude or underweight assets that sit in high‑risk zones, even if the parent company’s overall ESG rating is strong.
2. Risk reporting – Detailed, scenario‑based risk maps simplify the preparation of TCFD‑aligned disclosures, reducing reliance on external consultants.
3. Engagement strategy – Asset‑level data enables more targeted shareholder engagement, allowing investors to ask portfolio companies for specific mitigation plans (e.g., flood‑defence upgrades at a particular plant).
4. Insurance procurement – Insurers can use the same data to price coverage more accurately, potentially lowering premiums for assets that demonstrate robust resilience measures.

Given that Clarity AI’s client base collectively manages over $80 trillion in AUM, the ripple effect of this integration could be significant, potentially influencing capital allocation decisions across a sizable slice of the global market.

Industry reaction and analyst perspectives

Early feedback from industry analysts has been cautiously optimistic. Jane Thompson, senior analyst at GreenFin Research, noted, “The partnership addresses a real data gap that has limited the operationalisation of climate risk in investment processes. The challenge will be ensuring data quality and consistency across the massive asset inventory.”

Risk management firms have also signalled interest. A spokesperson for a leading global insurer, who preferred to remain anonymous, said, “Having asset‑level exposure data that is both scenario‑based and auditable could streamline our underwriting workflow and improve loss‑reserve estimates.”

Nonetheless, some observers warn that the integration’s success will hinge on the ability to keep the data current. Climate models are regularly updated, and asset locations can change due to corporate restructurings or new construction. Clarity AI and RiskThinking .ai will need robust pipelines for continuous data refresh to maintain relevance.

Looking ahead

Both companies have hinted at future enhancements, including the incorporation of real‑time sensor data and the expansion of biodiversity metrics to cover marine ecosystems. If delivered, these additions could further differentiate the platform in a crowded ESG‑tech market.

For now, the partnership marks a concrete step toward the “granular climate risk” vision that many investors have been championing. As regulators tighten disclosure rules and capital markets increasingly price climate exposure, solutions that combine high‑resolution physical risk modelling with enterprise‑grade ESG analytics are likely to become indispensable tools for the financial industry.

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