The Climate Data Disconnect: Why Models Still Aren’t Moving Markets

I’ve sat on both sides of the climate data equation. With the scientists building extraordinary models. And with the credit committees deciding whether to lend against a property in a floodplain.

Here’s the paradox I keep seeing:

We are flooded with climate risk data. Yet that data still struggles to influence the decisions that matter most. In conversation after conversation, lenders and portfolio managers say some version of the same thing: 'We have the data. It just doesn’t show up when we need it.'

The science is not the problem. In many cases, it’s exceptional.

Models today are global, forward-looking and asset-level. We can quantify flood depth to a single building decades into the future. We can map wildfire exposure across entire portfolios.

And yet, much of that intelligence still sits outside the decision.

Not because it lacks rigour. But because it doesn’t fit.

Where climate risk stalls

In my experience, the friction tends to fall into four areas.

Format mismatch
The data arrives as GIS layers or technical reports. The decision lives in a credit engine or a pricing spreadsheet. If it doesn’t land in the system already being used, it rarely makes it into the room.

Timing mismatch
Sometimes the data is strong, it just arrives too late. After pricing. After underwriting. After capital has already been allocated.

Translation gap
A one-in-100-year return period is precise scientifically. But unless it connects to loan-to-value, earnings-at-risk or cost of capital, it remains abstract.

One size fits none
A reinsurer, an asset manager and a municipal bond desk do not think alike. Context, incentives and risk appetite shape how data needs to land.

This is not only a technical integration issue. It is a cultural one.

The messy middle

There is a space I often call the ‘messy middle’ between world-class climate science and daily financial workflows.

It is not glamorous. It involves APIs, metadata, credit policy discussions and occasionally uncomfortable questions like, ‘If this risk is real, why isn’t it in our pricing model?’

But this is where impact happens.

It is where hazard becomes financial exposure.
Where geospatial data becomes credit policy.
Where physical risk moves from ESG narrative to capital allocation.

And it is harder than it sounds.

Legacy systems. Governance constraints. Budget cycles. Internal politics.

I have watched brilliant science struggle simply because it did not arrive in the right format at the right time.

Why this matters now

Climate risk is already reshaping financial outcomes.

Mortgage lending is tightening in high-risk wildfire zones.
Physical risk scores correlate with sovereign default probabilities.
Banks are beginning to reflect physical risk in mortgage pricing.

Capital is adjusting, often quietly and often reactively.

The real window for influence is not disclosure. It is much earlier, inside pricing decisions, credit assessments and risk appetite frameworks.

That is where resilience is either built or missed.

Closing the loop

I’ve heard the line many times: ‘If you can’t see it, it won’t be priced.’

I would add this: if it isn’t designed for the system making the decision, it still won’t be priced.

Scientific accuracy matters. But delivery, timing and context determine whether insight becomes influence.

Because when climate risk appears in cash flow models, capital plans and credit decisions, it starts to be managed.

And when it is managed, resilience becomes strategy.

A note to the Women in Climate community

Many of the women leading in climate are working in this ‘messy middle’ every day. Translating science into capital decisions. Bridging disciplines. Asking the uncomfortable questions.

It is not always visible work. But it is foundational.

If you are navigating that space, between technical insight and financial power, you are not alone.

Women in Climate exists to make that leadership more visible, more connected and more influential