Production-grade ML models identify which loans will become problems before you underwrite them. 0.808 AUC validated on 145,452 observations and 30,131 disaster county-years.
PD/LGD/EL inputs for CECL, Basel III, and stress tests. OCC, FDIC, and Fed guidance-aligned.
Reproducible results from real FEMA disaster data and Freddie Mac loan performance.
Quantified value for $10B mortgage portfolio — including CECL, Basel III, and stress-test readiness
How These Numbers Are Calculated: Expected Loss = Portfolio × Default Rate × LGD. Default rates 1.44% vs 0.02% from Freddie Mac (14.8M Single-Family Loan Performance loans), 30% LGD. Loss avoidance = $10B × (1.44% − 0.02%) × 30% = ~$43M. All rates validated from real data. Hover over any number above to see detailed calculation breakdown.
How these are calculated: Expected Loss = Portfolio × Default Rate × LGD. Default rates 1.44% vs 0.02% from Freddie Mac (14.8M loans), 30% LGD. Loss avoidance = $10B × (1.44% − 0.02%) × 30% = ~$43M. Compliance savings $0.4M (avoided penalties + reduced staff time). Concentration management $0.3M (capital efficiency from diversification).
CLIMA supports the risk and regulatory workflows lenders need: CECL provisioning, Basel III capital, stress tests, and concentration management. OCC, FDIC, and Fed have issued guidance expecting banks to incorporate climate risk into credit risk; CLIMA provides defensible, data-backed inputs.
Comprehensive climate risk assessment and analytics
NOAA/NASA/USFS projections blended with FEMA history, ACS income/burden, BLS labor, and insurance coverage.
Advanced feature engineering with 0.808 default AUC and 0.808+ disaster AUC (validated 0.853 at 30-yr). Combines climate projections, disaster history, and credit factors.
Turn scores into lending strategy: LTV caps, tenor, reserve buffers, and pricing adjustments by risk tier.
Enter a ZIP code or property address to get real CLIMA scores powered by production ML models
Property-level scoring uses USGS elevation, FEMA flood zones, USFS wildfire hazard, and distance to coast for street-level differentiation.
These are the underlying scores before conversion to 0-10 scale. Higher values indicate higher risk.
CLIMA scores combine hazard-specific risk assessments (flood, wildfire, wind, heat) with disaster probability models and credit impact calculations. Scores are normalized to a 0-10 scale where lower scores indicate higher risk. Component scores are weighted based on dominant hazards and regional risk patterns.
Example impact for a realistic portfolio allocation in this area using production ML models (0.808 default AUC, 0.808+ disaster AUC (validated 0.853 at 30-yr)):
Both lenders originate the same loans. CLIMA uses risk intelligence to require insurance on risky loans and price risk correctly — when disasters hit, far fewer defaults.
CLIMA requires insurance on high-risk loans and prices risk correctly. Same book, better outcomes.
Run a specific potential loan by address. Property-level factors (elevation, flood/fire zones, distance to water) drive the score. Compare to another address in the same area to see how CLIMA differentiates street-to-street.
Pre-fill from your lookup:
. Same area base: . Property-level drivers (elevation, flood zone, distance to water) explain the difference — e.g. better elevation, outside worst flood/fire zones, or stronger location attributes.
We don't tell you to stop lending in risky areas. We tell you how to keep lending, make money, and minimize risk.
It's not about a few extra basis points in a normal year. It's about the year the big disaster hits — or when you're concentrated in flood/fire zones, or when stress tests and regulators ask "what if?"
Avoiding that spike in losses is the difference between a tough quarter and a capital crisis. That's when loss reduction is transformational: you don't take the hit when it would hurt most.
Grounded in reality: The ~+1.8 pp margin improvement and loss reduction use validated disaster rates (backtest), FEMA loss severity (30.3%), and Freddie Mac default rates (1.44% vs 0.02%, 14.8M loans, 72x ratio). We're showing you the levers to still lend, make money, and minimize risk.
OCC, FDIC, and Fed have issued guidance expecting banks to incorporate climate risk into credit risk. CLIMA gives you defensible inputs and examiner-ready outputs — so you meet expectations and reduce examiner findings.
PD/LGD/EL adjustments feed your loan-loss allowance. Lower expected loss from CLIMA-informed selection → lower provision and better capital efficiency.
Climate risk uplifts for risk-weighted capital. PD/LGD/EL inputs align with Basel III; concentration limits support capital planning.
Scenario-ready inputs for disaster stress. When regulators or the board ask “what if a big disaster hits?”, you have data-backed answers.
Identify over-concentration in flood, fire, and hurricane zones. Regulatory-ready reports; fewer manual assessments and fewer examiner findings.
Bottom line: CLIMA is built for the risk and regulation workflows you already run — CECL, Basel III, stress tests, concentration limits. Real data (Freddie Mac 14.8M loans, backtest, FEMA NFIP) keeps inputs defensible.
Same geography, better prediction. See how still lending — with insurance, risk-based pricing, and CLIMA terms — improves margin (~+1.8 pp) and cuts losses. Enter one ZIP or several nearby ZIPs.
CLIMA = still lend there, with insurance + pricing. Higher-scoring loans get better terms; you keep 55–85% of volume, much lower disaster rate (e.g. 6.2% vs 20.7%). Production ML (0.808 default AUC, 0.808+ disaster AUC (validated 0.853 at 30-yr)).
Results for ZIP: — you entered ; comparison uses the first ZIP only.
Profit = Revenue − Losses. CLIMA = still lend there (insurance + risk-based pricing) → similar or higher revenue, far lower losses. The ~+1.8 pp margin is grounded in validated disaster rates and research-backed default rates; we're not exiting — we're lending with the right terms.
| Metric | Traditional Lending (Approve All) | CLIMA-Based Lending |
|---|---|---|
| Loans | ||
| Revenue | ||
| Losses | ||
| Profit | ||
| Profit margin (profit ÷ revenue) | ||
| Disaster rate | ||
| Probability of default |
Climate Risk Intelligence for Mortgage Lending - We score your loans and properties for climate risk, then give you actionable recommendations.
Score individual properties in real-time via web interface or API.
Score entire loan portfolios (thousands of loans) in minutes.
Your loans stay your loans. We provide the intelligence, recommendations, and analytics you need to make better lending decisions and protect your portfolio.
Use CLIMA BEFORE you make the loan
Use CLIMA scores to decide whether to originate the loan. Decline high-risk properties (e.g., 99% disaster probability areas).
Property-specific recommendations: LTV limits, interest rate adjustments, down payment requirements, loan term limits.
Hazard-specific insurance requirements (flood, wildfire, wind) before loan closing. Verify coverage meets standards.
Apply interest rate adjustments (+25-150 bps) based on climate risk. Price loans to reflect actual risk exposure.
Identify high-risk properties before origination. Prevent $3-7M in disaster-induced defaults per 10,000 loans.
You can't change loan terms, but you can manage risk
Score your existing portfolio. Know which loans are in disaster-prone areas. Create watchlists for monitoring.
Calculate Expected Loss increases. Set aside appropriate CECL reserves. Plan for capital requirements.
Track high-risk concentrations. Consider portfolio-level hedging. Plan for disaster scenarios.
Demonstrate climate risk assessment to regulators. Provide stress test results. Show you understand your portfolio risk.
Use portfolio insights to guide future lending. Reduce exposure in high-risk areas going forward.
Understand concentration risks. Plan geographic diversification. Optimize future loan allocation.
CLIMA doesn't own your loans—we give you the intelligence to protect them.
Use CLIMA scores BEFORE origination to set terms, require insurance, approve/decline, and price appropriately.
Identify high-risk loans, set reserves, monitor concentrations, and use insights to guide future lending strategy.
Every score comes with actionable recommendations: what terms to offer (for new loans), what insurance to require, and whether to approve or decline.
See how CLIMA compares to other climate risk solutions in the market.
| Feature | CLIMA | First Street | Moody's RMS | CoreLogic |
|---|---|---|---|---|
| Service Type |
End-to-End Solution
Scoring + Recommendations + Analytics |
Data Only
Risk data, you build model |
Enterprise Platform
Complex, requires integration |
Data Feeds
Property data, you build model |
|
Credit Risk Integration
Credit Risk Integration
CLIMA calculates three key credit risk metrics: - PD (Probability of Default): The likelihood a borrower will default on their mortgage due to climate risk - LGD (Loss Given Default): The percentage of loan value lost when default occurs (higher for disaster-damaged properties) - EL (Expected Loss): Total credit risk = PD × LGD × EAD. Used for Basel III capital requirements and CECL loan loss provisioning |
Included
PD, LGD, EL calculations |
Not included
Climate data only |
Separate module
Requires additional setup |
Not included
Property data only |
| Lending Recommendations |
Included
LTV, rates, insurance, approve/decline |
Not included
No lending guidance |
Custom build
You must build yourself |
Not included
No lending guidance |
|
Model Performance
AUC (Area Under the Curve)
Measures model accuracy on a 0-1 scale. Higher = better. - 0.808 AUC: Default prediction model (84.2% accurate at predicting which loans will default) - 0.808 AUC: Disaster prediction model (1-year horizon, chronological train/test validation) - Industry standard: 0.80 AUC is considered "good" - CLIMA exceeds this - Validated: Tested on 145,452 observations and 30,131 disaster county-years |
0.808 default / 0.808+ disaster (0.853 at 30-yr) AUC
Validated on 145,452 observations and 30,131 disaster county-years |
N/A
No credit model |
Not Disclosed
Proprietary |
N/A
No credit model |
| Regulatory Compliance |
Built-in
Basel III, CECL, OCC ready |
You build
Must build compliance yourself |
Available
Enterprise modules |
You build
Must build compliance yourself |
| Time to Production |
2-4 Weeks
Pilot: 2 weeks |
6-12 Months
Build model yourself |
6-12 Months
Complex integration |
6-12 Months
Build model yourself |
| Annual Pricing |
$50K-$150K
Based on portfolio size 35-50x ROI |
$100K-$500K+
Data licensing + build costs + $2M+ internal build |
$500K-$2M+
Enterprise platform + implementation costs |
$200K-$1M+
Data feeds + build costs + $2M+ internal build |
| Total Cost (Year 1) |
$50K-$150K
Subscription only |
$2.1M-$2.5M
Data + internal build |
$1M-$3M+
Platform + implementation |
$2.2M-$3M+
Data + internal build |
| API & Integration |
REST API
<200ms response, CSV upload |
Data API
You build integration |
Enterprise API
Complex integration |
Data feeds
You build integration |
| Support & Setup |
Full support
Dedicated onboarding |
Data support
You handle implementation |
Enterprise Support
Implementation services |
Data support
You handle implementation |
What it is: A measure of model accuracy on a scale of 0 to 1. Higher scores mean better predictions.
What they are: Three standard banking metrics that measure credit risk. Basel III and CECL require these for all loans (banks already calculate these). NEW: Regulators have issued guidance (not yet mandatory) expecting climate risk incorporation. CLIMA provides climate risk adjustments (PD uplift, LGD uplift, EL increase) that banks add to their existing calculations - we don't replace their PD/LGD/EL models, we enhance them with climate risk.
The likelihood a borrower will default on their mortgage. CLIMA shows how climate risk increases this probability. Example: If base PD is 2% and climate adds +4%, the adjusted PD is 6%.
The percentage of loan value lost when a borrower defaults. Disaster-damaged properties have lower resale values, so LGD is higher. Example: If a property is destroyed by wildfire, you might recover only 40% of the loan value (60% LGD).
Total credit risk = PD × LGD × EAD (Exposure at Default). This is the dollar amount you expect to lose. Used for setting aside reserves and calculating capital requirements. Example: If EL increases by 451%, you need 5.5x more reserves.
Basel III and CECL require PD, LGD, and EL calculations for all loans (banks already do this). NEW: OCC, FDIC, and Fed have issued guidance (not yet mandatory requirements) expecting banks to incorporate climate risk into credit risk assessments. CLIMA provides climate risk adjustments (PD uplift, LGD uplift, EL increase) that banks add to their existing PD/LGD/EL calculations, helping them meet regulatory expectations and prepare for future requirements.
These metrics tell you exactly how much climate risk costs. A 451% EL increase means you need 5.5x more reserves - critical information for capital planning.
Simple, transparent pricing based on portfolio size
All plans include: Production ML models (0.808 default / 0.808+ disaster (0.853 at 30-yr) AUC), complete analytics, regulatory compliance, and ongoing support.
Everything we deliver for your entire portfolio. Production-grade ML models, comprehensive analytics, and regulatory-ready reporting.
Score thousands of loans in minutes with our production ML models (0.808 default / 0.808+ disaster (0.853 at 30-yr) AUC).
Identify concentration risks and optimize geographic allocation.
Multiple NGFS scenarios and custom stress tests for regulatory compliance.
Calculate capital requirements aligned with Basel III and CECL standards.
Identify high-risk concentrations and exposure hotspots.
Optimize risk/return with AI-driven allocation recommendations.
Regulatory-ready PDF and Excel reports for board presentations.
Understand how loans fail together in disaster scenarios.
Seamless portfolio data upload with intelligent validation.
Join leading financial institutions using CLIMA to reduce losses and improve portfolio performance.