Why Banks Need Physical Climate Risk Data: A Playbook for Climate‑Smart Lending and ESG Compliance

Agriculture accounts for 6.10% of the gross non-performing assets across Indian scheduled commercial banks, the highest of any sector. In H1 FY2025-26 alone, the government wrote off ₹14,122 crore in farm loans due to natural calamities.

For banks with significant agricultural or climate-exposed portfolios, this is not a policy footnote. It is a balance sheet problem, and it is growing every year.

The Problem: Climate Shocks Are Creating a Credit Risk Banks Cannot Ignore

India ranks among the world's most climate-vulnerable nations. The IMF's 2025 Financial Sector Assessment found that in a severe scenario, where climate shocks hit for three consecutive years, agricultural loan default probability could rise by more than 30 percentage points. Expected losses could exceed 25% of total scheduled commercial bank agricultural loans.

This is not a distant projection. Weather-related loan write-offs have grown year on year. As a result, banks face a structural gap. Their credit assessment models were built around borrower financials, collateral, and repayment history. They were not built to account for the flood risk in a borrower's district or the drought probability in a region where a kisan credit card portfolio is concentrated.This gap has now turned into a regulatory concern. Climate risk in lending has moved from voluntary disclosure territory to a formal compliance requirement.

There are three channels through which physical climate risk reaches bank balance sheets:

• Credit risk: Extreme weather reduces borrower income, driving loan defaults.

• Collateral risk: Floods and droughts destroy assets pledged against loans.

• Operational risk: Damage to bank branches and infrastructure in climate-affected areas.

What Physical Climate Risk Data Is and Why Banks Need It

Physical climate risk data refers to measurable, location-specific information on climate hazards, including flood frequency, drought probability, heat stress intensity, and cyclone exposure, mapped to specific geographies.

For banks, this data answers a question traditional credit scoring cannot: How likely is it that this borrower's income or asset base will be damaged by weather in the coming years?

The RBI recognised this gap directly. In October 2024, it announced the Reserve Bank Climate Risk Information System (RB-CRIS), a data repository providing standardised hazard, vulnerability, and physical risk datasets to regulated entities. The system launched on 29 May 2025. 

This makes physical climate risk data providers, whether public sources like the India Meteorological Department or private data services, central to how banks will conduct climate credit risk assessments going forward.

How Climate Risk Data Applies to Lending Decisions in Practice

Consider a bank in Vidarbha, Maharashtra, managing a Kisan Credit Card portfolio across five drought-prone districts. Using district-level physical risk data, the bank can:

• Identify which districts have above-average three-year drought probability.

• Adjust provisioning for loans in those districts ahead of the monsoon.

• Require crop insurance coverage as a condition for credit renewal in high-risk zones.

• Price risk more accurately instead of applying uniform interest rates across geographically distinct portfolios.

This is already in practice. ICICI Bank's FY2025 ESG report describes exactly this approach, using physical risk assessment for top counterparties in its wholesale lending portfolio, factoring in geographic concentration of borrowers in flood and cyclone-prone regions.

For banks lending to MSMEs in coastal districts or to small manufacturers dependent on monsoon-driven supply chains, the same logic applies. Climate-linked loan default risk is not confined to agriculture. It runs through any portfolio where borrower revenue depends on weather, water, or climate-stable infrastructure.

What Does Physical Climate Risk Data Enable a Bank to Do

By partnering with physical climate risk data providers, banks can achieve the following:

Action	Without climate data	With climate risk data
Credit appraisal for farm loans	Based on repayment history only	Adjusted for district drought/flood risk
Provisioning for a climate-exposed portfolio	Reactive, post-disaster	Proactive, based on hazard probability
ESG climate risk scoring of borrowers	Not available	Possible using hazard and exposure data
RBI climate disclosure compliance	Qualitative only	Quantifiable with location-specific data

What Changes When Banks Embed Climate Risk Into Lending

The RBI's climate disclosure framework, applicable to all scheduled commercial banks from FY2025-26, mandates physical and transition risk assessment as part of the Internal Capital Adequacy Assessment Process (ICAAP).

Banks that integrate climate resilience financing solutions into their lending workflow gain three concrete advantages. First, they can price climate-exposed loans more accurately. Second, they can reduce surprise write-offs by building climate risk into provisioning models. Third, they meet emerging ESG climate risk scoring requirements without scrambling for data at disclosure time.

Parametric insurance adds another dimension here. A farmer borrower covered by parametric weather insurance carries measurably lower climate-linked default risk. When a drought trigger is met, the farmer receives an automatic payout within days. 

This payout helps protect loan repayment capacity. Banks that factor parametric coverage into their climate credit risk assessment are measuring borrower resilience more accurately than those that do not. 

Financial institutions interested in reducing agricultural NPA risk have good reason to explore whether parametric insurance can form part of the credit product design for climate-exposed borrowers.

Limitations for Banks Using Climate Risk Data

Integrating physical climate risk data into lending is not simple. Banks face several real constraints.

Data quality and availability remain the central challenge

RB-CRIS is designed to address this, but standardised district-level physical hazard data for all of India's 700-plus districts does not yet fully exist in one place. ICICI Bank's own FY2025 ESG report acknowledges that available data is "fragmented, in varied formats, and inconsistent in frequency and units."

Risks associated with model limitations

Historical weather data may not reflect future climate patterns as they shift. Thus, alongside historical data, banks also need to evaluate recent weather data (and forecasting) for an accurate climate credit risk assessment. 

Mapping portfolio exposure to climate hazards requires loan-level geolocation data that many banks do not yet hold. The IMF's 2025 FSAP notes that the absence of loan-level geolocation data may cause banks to underestimate tail risks. 

Basis risk applies here as well 

Even well-designed climate risk models may not capture hyper-local events, such as a flash flood in one block of a district that leaves nearby blocks unaffected. Aggregate district data can mask highly localised loan exposure.

Integrating ESG climate risk scoring into credit decisions

To properly integrate ESG climate risk scoring with assessing climate-linked loan default risk, banks need specific internal capacity and trained staff. Most smaller regional rural banks lack this infrastructure, which is why they fall short.

Wrapping Up: The Regulatory Direction Behind Climate Credit Risk Assessment

Climate risk in lending has moved from a voluntary consideration to a formal compliance requirement for Indian banks. The RBI's mandatory disclosure framework, combined with the launch of RB-CRIS, signals that physical climate risk data will become as standard in credit appraisal as financial statements. 

Agriculture accounts for 13% of scheduled commercial bank loans and over 66% of regional rural bank loans, sectors where climate exposure is highest. The tools to manage this risk are being developed, and the expectation that banks will use them is already in place.

Ready to Learn More About Climate Risk Data and Its Impact on Loans?

Parametric insurance is quietly becoming part of how forward-looking lenders manage climate-exposed portfolios. Explore how it is being applied as a practical credit risk tool for Indian banks in agriculture and climate-exposed lending.

Frequently Asked Questions

Which types of banks benefit most from physical climate risk data?

Physical climate risk data is valuable for all lenders but offers the greatest benefits to banks with significant exposure to agriculture, MSMEs, infrastructure, housing finance, and rural lending. These portfolios are more vulnerable to weather-related disruptions that can affect borrowers' repayment capacity.

How often should banks update their climate risk assessments?

Climate risk assessments should be reviewed regularly, especially before major lending cycles and after significant weather events. Periodic updates using the latest climate observations and projections help banks keep credit decisions aligned with evolving environmental risks.

What role does geospatial technology play in climate risk assessment?

Geospatial technology combines satellite imagery, mapping tools, and climate datasets to identify hazard exposure at specific locations. This enables banks to assess climate risks more precisely than district-level averages and make better-informed lending and portfolio management decisions.

How can climate risk data improve stress testing for banks?

Climate risk data allows banks to model the potential impact of floods, droughts, heatwaves, and cyclones under different scenarios. These stress tests help estimate possible loan losses, capital requirements, and portfolio vulnerabilities before climate events occur.