Climate data for resilient lending

Physical climate risk data: the more we learn, the less we know? Khalid Azizuddin's recent piece in *Responsible Investor captures well what many practitioners are grappling with today: - asset-level data that remain incomplete or hard to interpret; - physical hazard exposure often disconnected from financial materiality; - little visibility on supply chains or customers; - adaptation and resilience efforts largely ignored; - and a risk of over-simplifying complex realities into a single “score.” Some three years ago, EDHEC Business School set out to address exactly these challenges, working to advance climate risk modelling and make decision-useful for investors, companies, and public authorities. In this work, we have developed: 🔹 a blueprint for a new generation of probabilistic climate scenarios; 🔹 high-resolution geospatial modeling capabilities to allow for geographic and sectoral downscaling, consistent with each scenario; 🔹 an open database of decarbonisation and resilience technologies through the #ClimaTech project, which officially launched this week. While the research is public, the new EDHEC Climate Institute has also been assisting a school-backed venture, Scientific Climate Ratings (SCR), which integrates this research to deliver forward-looking quantification of the #financialmateriality of climate risks for infrastructure companies and investors worldwide. While SCR provides a rating scale for comparability, it avoids the trap of over-simplification. Each rating is backed by probabilistic scenario modelling, analysis of physical and transition risk exposures, and explicit accounting for adaptation measures. The result is a synthesis that remains transparent, interpretable, and anchored in scientific rigour. Together, these initiatives aim to move the discussion from data abundance to decision relevance, equipping practitioners with tools that connect climate science, finance, and strategy.

𝗣𝗵𝘆𝘀𝗶𝗰𝗮𝗹 𝗖𝗹𝗶𝗺𝗮𝘁𝗲 𝗥𝗶𝘀𝗸 𝗮𝗻𝗱 𝘁𝗵𝗲 𝗖𝗼𝘀𝘁 𝗼𝗳 𝗖𝗮𝗽𝗶𝘁𝗮𝗹: 𝗡𝗲𝘄 𝗘𝘃𝗶𝗱𝗲𝗻𝗰𝗲 A new study in the Journal of Environmental Economics and Management (2026) quantifies how physical climate risk is being integrated into the pricing of bank loans. Analyzing 86,000 syndicated loans globally, the research demonstrates that climate vulnerability as a material driver of credit risk. 𝗞𝗲𝘆 𝗙𝗶𝗻𝗱𝗶𝗻𝗴𝘀: ➖𝗣𝗿𝗶𝗰𝗶𝗻𝗴 𝗜𝗺𝗽𝗮𝗰𝘁: A one standard deviation increase in climate vulnerability correlates to a 39 basis point increase in borrowing costs. ➖𝗟𝗼𝗮𝗻 𝗧𝗲𝗿𝗺𝘀: The impact of climate physical risk exposure tends to be greater for long maturity loans, with loans in the top maturity duration quartile exhibiting a 27 basis point increase in borrowing costs. This suggests that banks manage exposure by reducing loan sizes, shortening maturities, and increasing collateral requirements. ➖𝗥𝗶𝘀𝗸 𝗖𝗵𝗮𝗻𝗻𝗲𝗹: The effect is driven by an increase in firms’ default probabilities, specifically impacting long-term credit risk ratings. The study leverages Sustainable1 by S&P Global Energy Horizons climate physical risk data to validate these findings at the firm level. By analyzing specific climate hazard indicators—including heatwaves, floods, and hurricanes—the research confirms that granular asset-level risk scores are predictors of loan spreads. For organizations in high-risk regions, these findings underscore that climate adaptation is no longer just an ESG priority—it is a financial necessity to maintain favorable access to debt markets. Read the full paper here: https://lnkd.in/guWbK-AD #SustainableFinance #ClimateRisk #CreditRisk #Banking #SPGlobal #Sustainable1

What the updated NGFS Guide means for banks and potentially for companies’ cost of capital Climate risks are now financially material. This update is important because it gives a much stronger, more realistic tools to understand how climate change can threaten financial stability in both the near term and long term. The updated NGFS guidance raises expectations on how climate risks are priced into pricing, valuations, and capital planning. This has direct consequences for cost of capital, lending decisions, and credit valuations. Banks will face increasing pressure to price climate risks explicitly. Interestingly, this will sharpen their methods to identify risks related to climate which translate to: 1. Higher cost of capital for climate-exposed sectors. The more detailed transition and physical-risk pathways mean banks must recognise greater risk for: • high-emission sectors, • businesses with weak transition plans, • borrowers in climate-vulnerable regions. This results in higher loan spreads, tighter lending terms, shorter maturities, and reduced credit availability for riskier clients. Also, low-carbon or well-aligned firms may see more stable or lower funding costs. 2. Credit valuations increasingly include climate-adjusted risks. Banks are expected to incorporate climate impacts directly into default probabilities, loss estimates, and collateral valuations. We do already see how national authorities are looking after the EBA implementation. Transition shocks, policy changes, and physical events must now be reflected in near-term pricing, not just long-horizon models. This raises credit risk metrics in exposed sectors and widens credit spreads. 3. Capital requirements rise where climate risks are material as scenario outcomes feed into banks’ internal capital processes. Where climate pathways produce higher expected losses, banks will need to increase capital buffers, adjust provisions, and recognise higher risk-weighted assets. This reinforces the rise in funding costs for borrowers with significant climate exposures. 4. Lending portfolios shift toward lower-risk segments. With scenario analysis embedded in risk management: • Lending to carbon-intensive or climate-vulnerable sectors becomes more expensive and selective, • Banks gradually reallocate toward climate-resilient industries and clients with credible transition strategies, • Credit supply becomes more differentiated, with pricing reflecting detailed sectoral and regional climate risk assessments. 5. Short-term climate scenarios sharpen near-term repricing. The introduction of short-term NGFS scenarios means banks must prepare for climate-related shocks within their normal planning horizon. This leads to more frequent repricing of loans, higher volatility in valuations, and earlier recognition of potential losses. These rising expectations for clarifies what “good practice” looks like and supports consistent global standards.

Just released, 57 banks in the United States could face material financial risk as defined by the SEC. That's what the First Street 11th National Risk Assessment, Portfolio Pressures found. Full download here: https://lnkd.in/eMAs_tGv Using the First Street Correlated Risk Model, we identified the potential climate risk to the loan portfolios of all banks in the United States. Below are the key take aways: 1. Importance of Geographic Diversification: Financial institutions, particularly smaller banks, face higher risks with geographically concentrated portfolios, underscoring the need for strategic diversification to mitigate climate-related financial losses. 2. Comprehensive Climate Scenario Analysis: Effective climate risk assessment requires comprehensive scenario analyses that account for the interactions between different climate perils across various regions and timeframes. 3. Regulatory Challenges: Current regulatory frameworks do not mandate climate scenario analyses for smaller banks, creating a significant gap in climate risk oversight and leaving these institutions unprepared for future climate impacts. 4. Impact on Communities and Property Values: Climate events not only cause immediate losses from physical damage but also have long-term effects on property values and the broader economy, making comprehensive risk assessment crucial for financial stability. 5. Advancement in Risk Modeling: The First Street Correlated Risk Model (FS-CRM) is the climate risk financial modeling (CFRM) tool for a complete understanding of climate risk through the integration of correlations among multiple perils, with the precision of property-specific damage estimates and more accurate projections through the integration of forward looking climate data, a significant industry advancement. all of which allows for a clearer picture of potential financial impacts. 6. Strategic Risk Mitigation: By using advanced models like the FS-CRM, stakeholders can better understand and mitigate the risks associated with climate change, enhancing resilience in both the financial sector and the communities they serve. Reach out if you would like to learn more.

Machine learning models help banks manage climate risk by analyzing vast, complex #datasets to predict and quantify physical and transition risks. These models can integrate diverse data sources, such as climate projections, financial data, and text from corporate reports, to automate risk assessment, enhance scenario analysis, and improve regulatory #compliance. By learning from new data, these models adapt to evolving climate conditions, helping banks build more resilient financial systems. 𝗛𝗼𝘄 𝗺𝗮𝗰𝗵𝗶𝗻𝗲 𝗹𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗶𝘀 𝘂𝘀𝗲𝗱 𝘚𝘤𝘦𝘯𝘢𝘳𝘪𝘰 𝘢𝘯𝘢𝘭𝘺𝘴𝘪𝘴 𝘢𝘯𝘥 𝘴𝘵𝘳𝘦𝘴𝘴 𝘵𝘦𝘴𝘵𝘪𝘯𝘨: ML models can simulate various climate scenarios to assess their potential impact on loan portfolios, asset valuations, and overall financial stability.  𝘋𝘢𝘵𝘢 𝘢𝘯𝘢𝘭𝘺𝘴𝘪𝘴: ML algorithms excel at processing large and diverse datasets, including unstructured text from corporate reports, to automatically identify and extract key climate-related indicators. This reduces the need for manual feature engineering.  𝘊𝘳𝘦𝘥𝘪𝘵 𝘳𝘪𝘴𝘬 𝘢𝘴𝘴𝘦𝘴𝘴𝘮𝘦𝘯𝘵: Models can incorporate climate data into creditworthiness assessments, predicting how physical risks might impact a borrower's repayment capacity.  𝘙𝘪𝘴𝘬-𝘴𝘩𝘢𝘳𝘪𝘯𝘨: ML can help price climate risk for insurance products, enabling new financial products where banks and insurance companies can share risk and offer more resilient loan products.  𝘙𝘦𝘨𝘶𝘭𝘢𝘵𝘰𝘳𝘺 𝘤𝘰𝘮𝘱𝘭𝘪𝘢𝘯𝘤𝘦: Automated systems powered by ML help banks meet regulatory requirements for climate risk disclosure and provide transparent reporting to stakeholders. 𝗔𝗱𝘃𝗮𝗻𝘁𝗮𝗴𝗲𝘀 𝗼𝗳 𝘂𝘀𝗶𝗻𝗴 𝗺𝗮𝗰𝗵𝗶𝗻𝗲 𝗹𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝘏𝘢𝘯𝘥𝘭𝘦𝘴 𝘤𝘰𝘮𝘱𝘭𝘦𝘹𝘪𝘵𝘺 𝘢𝘯𝘥 𝘷𝘰𝘭𝘶𝘮𝘦: ML is well-suited for the complex, high-dimensional data involved in climate risk modeling, which includes climate projections, financial data, and other variables. 𝘈𝘥𝘢𝘱𝘵𝘢𝘣𝘪𝘭𝘪𝘵𝘺: Models can be retrained with new data to adapt to changing climate conditions, new policies, and evolving risks, which is crucial for long-term risk management. 𝘌𝘧𝘧𝘪𝘤𝘪𝘦𝘯𝘤𝘺: Automation through machine learning can streamline the process of risk assessment, reduce the manual effort required, and improve the consistency of results.  Some of the challenges include Data uncertainty where the chaotic nature of climate systems can lead to uncertainty in model outputs, meaning small errors in initial data can lead to significantly different future predictions. In addition, banks need to ensure their models are understandable and transparent to meet regulatory scrutiny. The attached compilation covers the latest research on the topic above. #riskmanagement #climaterisk #riskmeasurement #riskassessment #physicalrisk #transitionrisk #stresstesting #machinelearning #ML #neuralnetworks #DNN #riskmodeling #MRM #financialrisk #FRM #riskreport #riskdisclosure #riskanalysis #information #resources #research #knowledge