AI Underwriting Makes Geothermal Projects Bankable

A geothermal project can look “approved” on paper and still fail for one stubborn reason: you don’t truly know what’s happening underground until you drill. That uncertainty doesn’t just slow projects down—it scares off lenders, inflates the cost of capital, and turns promising renewable heat plans into stalled spreadsheets.

That’s why Munich Re’s newly announced geothermal risk insurance—built as a public-private partnership with Germany’s KfW Development Bank and backed by €600 million in government guarantees plus €50 million in budget funds—matters far beyond Germany. It’s a template for how insurers, banks, and governments can make high-uncertainty energy infrastructure financeable.

This post is part of our “AI in Energy & Utilities” series, where we track how AI helps the energy system run cleaner, cheaper, and more reliably. Here’s the point I’ll argue strongly: geothermal will not scale on optimism; it will scale on better risk selection and clearer risk transfer—and AI-enabled underwriting is quickly becoming the deciding factor.

Why geothermal financing breaks (and why insurance fixes it)

Geothermal financing breaks at the exploration stage because the loss is binary and front-loaded. You spend heavily on drilling, then either confirm a productive resource or you don’t. If you miss, you can’t “iterate” your way out like a software team; the money is already in the ground.

The RSS story captures the core issue: geothermal is abundant and clean, but upfront drilling costs can exceed future returns when a project fails—so investors hesitate. The EU context is equally telling: geothermal made up only 0.5% of renewable electricity generation in 2024. That’s not because geothermal lacks promise; it’s because early-stage risk is difficult to finance.

Insurance can fix this when it does two things well:

• Converts geological uncertainty into a priced, contractible risk (something a lender can underwrite)
• Stabilizes downside outcomes so the project looks “bankable,” not speculative

Munich Re’s structure is notable: it will insure 30% to 70% of a loan for a feasible project, while KfW provides a partial debt waiver if unsuccessful exploration triggers a claim. That is a direct attack on the exact risk that blocks deals.

What “bankable” actually means in geothermal

“Bankable” is often used like a vibe. In project finance, it’s practical:

• The lender can model downside scenarios with credible probabilities
• Loss severity is capped or transferred (insurance, guarantees, covenants)
• Construction and exploration risks are allocated to parties that can manage them

Geothermal fails the first test when risk probabilities are fuzzy. It fails the second when there’s no robust protection for exploration-stage disappointment.

The reality? Insurance is the missing bridge between subsurface science and spreadsheet certainty.

The Munich Re + KfW program: a public-private risk playbook

The program’s significance is the partnership design, not just the policy. Munich Re called it the first insurance policy based on a public-private partnership of this kind in Europe.

Here’s the mechanism in plain terms:

• A geothermal project gets a loan.
• Munich Re insures a portion of that loan (30%–70%) for projects deemed feasible.
• If exploration fails and triggers a claim, KfW grants a partial debt waiver.
• The German state supports the system with substantial guarantees and budget funds.

This matters because geothermal drilling risk isn’t just “high.” It’s hard to diversify when you don’t have enough projects, enough consistent data, and enough standardization across geology, contractors, and measurement.

Public backing helps solve the “thin market” problem:

• It increases deal flow (more projects = more data)
• It improves risk pooling (insurer isn’t hostage to a small sample)
• It signals political commitment (capital follows policy clarity)

Germany also has a concrete target: at least 65 additional geothermal projects by 2030, aiming to raise geothermal’s share in heating 10-fold compared to current levels. Targets like that create a pipeline—but pipelines only convert to shovels-in-the-ground when risk is financeable.

“We want to solve the problem that geothermal projects are not yet bankable because the outcome of a drilling operation is completely unclear.”

That underwriting statement is the heart of the story.

Where AI actually helps: underwriting geothermal risk like a portfolio, not a bet

AI helps geothermal insurance by turning messy geoscience and operational data into clearer probabilities, faster decisions, and tighter terms. Not magic. Just better modeling, better triage, better monitoring.

In the “AI in Energy & Utilities” world, we talk a lot about grid forecasting and predictive maintenance. Geothermal underwriting is similar: you’re predicting performance under uncertainty with imperfect signals. The difference is that in geothermal, errors are expensive and early.

1) AI-driven risk selection: deciding which projects are truly “feasible”

Underwriters have always assessed feasibility, but AI can make it more consistent and scalable by learning from multiple inputs at once:

• Subsurface data: seismic surveys, well logs, temperature gradients
• Geological analogs: similarity to nearby basins or historical wells
• Drilling plans: depth targets, casing plans, contractor track records
• Operational assumptions: flow rates, reinjection strategy, corrosion scaling risks

The goal is not to replace geologists. It’s to reduce variance in decision quality, especially when the market scales and underwriting teams get stretched.

A practical way to use AI here is as a ranking and triage layer:

• Fast-screen projects into “decline,” “needs more info,” and “underwrite” buckets
• Highlight the variables that drive expected loss most (so teams ask smarter questions)
• Suggest coverage structures aligned to the specific risk drivers

2) Smarter pricing: moving from blunt premiums to risk-based terms

Geothermal insurance struggles when pricing is simplistic. If premiums are too high, projects don’t proceed. If they’re too low, the insurer exits the market (Munich Re has pioneered geothermal insurance before and later scrapped it, per the article).

AI-enabled pricing helps by:

• Estimating probability distributions (not single-point guesses)
• Separating correlated risks (geology vs. contractor performance vs. equipment failure)
• Stress-testing scenario sets (e.g., flow rate shortfalls, temperature decay, induced seismicity controls)

This is where insurers can get genuinely useful: price the uncertainty you can model, and exclude or cap the uncertainty you can’t.

3) Claims prevention and loss control: underwriting doesn’t stop at bind

Most energy infrastructure insurers are shifting toward continuous risk management. Geothermal is a strong fit.

AI can support:

• Real-time anomaly detection from drilling telemetry
• Early warning indicators for stuck pipe, kicks, lost circulation, or tool failure
• Monitoring of induced seismicity thresholds and operational adjustments

That reduces claim frequency and improves outcomes for everyone involved. It’s also a way to make public-private programs more defensible politically: fewer “we paid for failure” headlines, more “we managed risk responsibly” proof.

What insurers and energy developers should copy (even outside Germany)

The copyable insight is not “state-backed insurance exists.” It’s how the risk is sliced so private capital can participate. If you’re an insurer, broker, utility, municipality, or developer, here are the pieces worth replicating.

Design coverage around the one risk that kills the deal

For geothermal, it’s exploration performance uncertainty. For other energy projects, it might be interconnection delay, supply chain disruption, or extreme weather downtime.

Ask lenders directly: What would make you say no? Then structure coverage to target that.

Standardize the data package (so AI can do its job)

AI underwriting fails when every submission is a bespoke PDF soup.

A “bankable geothermal submission” should include structured fields such as:

• Location, basin, nearby analog wells
• Temperature gradient estimates and confidence bands
• Target depth and drilling method
• Contractor and rig history (with measurable KPIs)
• Reservoir stimulation plan (if any)
• Monitoring plan (seismic, pressure, chemistry)

Standard inputs don’t just help models—they help human underwriters make faster, cleaner decisions.

Treat geothermal as a portfolio, not a hero project

One of the fastest ways to break geothermal insurance is to underwrite one-off projects with no portfolio logic. The better approach is to build diversified books by:

• Geography and geology type
• Developer maturity and contractor ecosystem
• Depth and technical complexity
• Heat vs. electricity use cases (the article notes heat is technically less challenging than electricity)

AI helps here by identifying correlation and concentration risks that are easy to miss when deal volume rises.

FAQ-style answers your team will ask in 2026

Is geothermal risk insurance only for electricity projects?

No. In fact, the article highlights a shift: generating heat is less technically challenging than producing electricity, which is one reason Munich Re has re-entered this market.

Why does public backing matter if the private market exists?

Because early-stage geothermal is still a “thin market.” Public guarantees help create deal flow and data, which makes private pricing more credible and sustainable.

What’s the role of AI in renewable energy insurance, specifically?

AI improves risk modeling, underwriting speed, and monitoring, which lowers uncertainty premiums and reduces preventable losses—especially in high-variance infrastructure like geothermal.

What happens next: geothermal scales where underwriting scales

Germany’s program is a signal: policymakers are tired of climate targets that die at the financing stage. Insurers are tired of products they can’t price sustainably. Developers are tired of being told their projects are “great” right up until the bank says no.

AI won’t remove subsurface uncertainty. It will do something more useful: make uncertainty measurable enough to insure, finance, and manage. That’s the path to more geothermal wells, more renewable heat networks, and fewer stalled projects.

If you’re leading underwriting, innovation, or risk at an insurer—or building geothermal and heat infrastructure on the energy side—this is the moment to get practical:

• Standardize submissions.
• Build portfolio logic.
• Use AI where it improves consistency and monitoring, not where it merely sounds impressive.

The forward-looking question I keep coming back to is simple: when geothermal becomes a normal asset class, will your underwriting and risk ops be ready to handle volume without dropping decision quality?