Company: BCDRF
Filing Date: 2025-02-28
Form Type: 20-F
Source: 0000891478-25-000054
Chunk: 52

Company: Banco Santander, S.A.
Filing Date: 2025-02-28
Form: 20-F
Chunk 52
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 governance |     | Economic and financial review |     | Riskmanagement and compliance |

#### Physical risk
To assess physical risk by geographical location, we work with a leading provider in the reinsurance industry, which enables us to measure the risk of 13 physical hazards (eight acute and five chronic). We analyse the markets where we operate, with a breakdown of over 1,250 regions (NUTS 3 or equivalent), and cover all economic activities in our Risk Taxonomy, as well as the business lines (such as mortgages and automobiles).

We assess each region (NUTS3) to measure the associated physical risks by rating them on our five-point RAG (Red, Amber and Green) scale (low to very high).

To assess the frequency and intensity of natural hazards, we use RCP scenarios across different time horizons (present day, 2030, 2040, 2050 and 2100).

Our analysis, based on a conservative approach, uses RCP 4.5 scenario and time horizons of 2030 for economic activity sectors

and 2050 for collaterals at Group level. The results are included in our materiality assessment at Group level, by region (Europe, North America and South America) and by subsidiary. The results show that while certain sectors are more exposed to physical risks, such as agriculture, conventional energy and mining, their concentration in the Group is very low (between 1% and 2%). The collateralized portfolios have the lowest impact, albeit with very high concentration of exposure in the Group.

The granular, forward-looking physical risk assessment enables us to actively manage these risks through monitoring, metrics and mitigation measures.

Finally, to measure and quantify physical risks more accurately, we increased the granularity of physical risks for the European portfolio (Poland, Portugal, Spain and the UK, as we now have information at postcode level).

#### Real estate
Santander’s real estate portfolio accounts for a large proportion of Santander's balance sheet, which is why we developed a specific module within the Klima tool with a detailed overview of the portfolio’s transition and physical risk.

Regarding transition risk, we increased the quality and quantity of EPC data by obtaining information, developing estimation models and defining plans to gather information during the customer onboarding process. This internal model consists of a machine learning algorithm that applies a combination of variables related to the real estate guarantee (type of property, geographic location, etc.), learning from observations made from actual data available. These enhancements give