cross-posted from: https://mander.xyz/post/38752499
A recent global survey by UNEP FI and Global Credit Data found that only 18 per cent of banks integrate climate risk into their internal ratings-based models [IRBs], which drive regulatory capital requirements. The study cites data gaps and methodological hurdles but does not explain the deeper problem: credit risk and climate risk models are built on fundamentally different logics.
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Unless supervisors adapt, the IRB models of today will remain blind to one of the most significant credit risk drivers of this century.
Credit risk models are precision tools honed on the past. Under the IRB approach, they calculate probabilities of default, losses given default, and exposure at default using deep pools of historical data.
Defaults, losses and macroeconomic patterns from years gone by are fed into these systems, with the underlying belief that yesterday’s relationships will largely hold tomorrow. This backward-looking design is reinforced by strict regulatory requirements: every risk driver must be de facto statistically significant, rigorously validated, and continuously monitored.
The result is a disciplined, data-heavy framework built to forecast the next 12 months of creditworthiness — and nothing beyond.
Climate risk models speak a different language. They are not grounded in borrower default histories but in climate science and policy pathways. Instead of asking “what happened last year, and will it repeat?”, they ask “what could happen in the decades ahead and how prepared are we if it does?” — on the premise that past performance is no guarantee of future results.
"“A factory in a flood zone may operate for years without incident, then suffer catastrophic losses from a single storm wiping it out overnight.”"
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