Big bank, local bank: the difference in climate risk isn't the one you'd expect

Flood exposure, energy labels, nature overlays: a large bank and a local sparebank are working from the same underlying climate data. Two assumptions usually get made about how the two differ. First, that the big bank understands climate risk better. Second, that the big bank has better data to work with. Neither holds up well against what actually shows up in practice.

What actually differs: headcount, not understanding

The real difference between a large bank and a local one isn't how much they understand about climate risk. It's how many people are dedicated to thinking about it.

A large bank typically has several people whose job is specifically sustainability or climate risk, spread across risk, credit, and reporting. A local sparebank might have one sustainability lead, sometimes at the individual bank level, sometimes shared across an alliance, and sometimes the responsibility sits informally with whoever in credit or risk picked it up alongside their other work. That variation in staffing is real. It just doesn't map onto a gap in understanding.

Conceptually, both tend to be in a similar place. A big bank with five people on climate risk and a small bank with one are usually still working through the same fundamental questions: what to measure, and how to let it change a decision. The big bank has more hands to work through those questions, and more developed process around them. What it doesn't necessarily have is a clearer answer.

The shared blind spot: energy labels as the whole picture

Here's the pattern that matters more than bank size. Both large and small banks tend to lean almost entirely on energy labels when they talk about climate risk.

Energy labels are useful and easy to obtain, which is exactly why they've become the default. But they speak to transition risk, and only part of it. They say almost nothing about the other two categories: physical risk, like surface flooding or quick clay, and nature risk, like protected habitats or peatland. Those get far less attention at banks of every size, not because they matter less, but because energy labels have been the data that's easiest to get hold of.

The consequence is easy to miss. A bank confident in its climate risk process because it has a solid handle on energy labels is approving and pricing loans against collateral whose physical exposure nobody has actually looked at. That's just as true at a large institution with a dedicated team as at a local sparebank with one person covering the topic part time.

The shared ceiling: data quality

The other constant across bank size is data. Both report the same underlying frustration: either the data they need doesn't exist in a usable form, or what does exist isn't granular enough to trust for a single lending decision.

This is the part more staff doesn't automatically fix. A large bank with a five-person team can still be working from the same postcode-level averages as a one-person operation at a local bank, if better data was never sourced in the first place. Headcount buys capacity to act. It doesn't, on its own, buy a better view of an individual property.

What this means in practice

Set the three apart and the picture is clearer than "mature versus immature." Understanding is broadly similar. Capacity differs, and it tracks headcount. Data quality is a shared ceiling that neither bank can simply staff its way past.

That last point is the useful one. What capacity mostly buys is the ability to chase down better data and build it into process. A local bank that sources the right data can close much of the gap without hiring a team to match a big bank's, because the limitation for both has rarely been understanding. It's access to something better than energy labels and regional averages.

The bottom line

Bank size mostly predicts how many people are working on climate risk. It doesn't predict how well any of them understand it, and it doesn't predict how good the data behind their work is. Every bank, large or small, is still leaning too heavily on one risk category out of three, and still working with data harder to trust than anyone would like to admit. The bank that fixes the data, not the headcount, is the one that pulls ahead.