Climate Stress on Operational Risk


Patrick Naim, risk modelling expert.
Published Sept, 09, 2021


As the Basel Committee on Banking Supervision has pointed out in their recent report Climate related financial risk – measurement methodologies, “banks and supervisors have predominantly focused on assessing credit risk, as they advance in applying methods to translate climate-related exposures into categories of financial risk”.

On the particular topic of operational risk, the Basel Committee also recommends in another document Climate related risk drivers and their transmission channels that “publicly available information regarding climate-related operational risks is scarcer than for other risk types, and therefore the whole risk category would benefit from more data and research”

As part of its regular exercise on operational risk scenario benchmarking, the ABA conducted a climate stress study of select operational risk scenarios between 2020 and 2021, the approach and results of which are particularly relevant to the Basel Committee recommendations and which we describe in detail below.

Categories of events

As we narrow down our focus to discuss the impact of climate change on banks operational risk, we consider 7 types of operational risks events: Conduct, Cyber, Disruption, Error, External Fraud, Internal Fraud and Legal.

There are potential dependencies between these operational risk categories and climate change, with the Disruption category being the most exposed, although other categories may also be affected, as shown in the table below; the Cyber, Error and Fraud operational risk categories being arguably the less impacted.

The transmission channels described in the Table 1 below are qualitative. To quantify them, it will be difficult to rely on past data, even if some basic mechanisms can be substantiated by data: for example, the relationship between climate and conflict has been quantitatively measured.

Using these transmission channels for the assessment of future operational risks, it is necessary to represent these risks by “loss generation mechanisms”, rather than data.

Operational Risk Category Sensitivity to Physical Risk Sensitivity to Transition Policy
Description of the possible transmission channels


New climate regulations will generate new obligations for banks and create new risks of misconduct.


Climate change may impact the geostrategic balance and increase the risk of cyber-attacks, especially by states.


Climate change increases the risk of natural disasters, disrupting not only banks but also their suppliers.


Increase of temperature and extreme meteorological events impacts productivity and increases the risk of error.

External Fraud

Second order impact. Possible mechanisms are the increase in conflicting tensions, or the emergence of new vulnerabilities as banks adapt their business processes to the transition.

Internal Fraud

Second order impact. Possible mechanisms are the increase in conflicting tensions, or the emergence of new vulnerabilities as banks adapt their business processes to the transition.

Climate change will expose some major companies to lawsuits and may expose their financial partners to liability.

Structured Scenarios for Operational Risk

Structured scenarios for operational risk are specifically designed to describe loss generation mechanisms. The loss generation mechanisms are quantified using a combination of business data, external observations, expert opinion, and other data.

In 2018, ABA launched an initiative to build and quantify structured scenarios with a bank-focused working group. This has been in particular applied to Cyber Risk in 2019. In 2020, an « ABA SSA Portal » hosting the scenarios and data has been developed and is now online.

Climate Stress on Structured Scenarios

We will now describe in detail the approach we have implemented with the Structured Scenario Work Group at ABA.

The first step was to define with the working group a set of scenarios for which there was a consensus on sensitivity to climate stress. This selection was not meant to be exhaustive but allowed us to focus on a subset of scenarios.

In the table below, we show an extract of the initial list of scenarios (35 scenarios were initially considered), and the resulting selection.

Operational Risk Category Scenario Name Main Sensitivity Description of the possible transmission channel Selected
Conduct    Corporate Client Misrepresentation Transition Transition may increase the probability of default of certain firms, and therefore the exposure of the bank to shareholders class actions.
Conduct Fund Improper Disclosure Transition Funds might be invested in non-environmentally friendly securities
Conduct Mis-selling Wholesale Transition Some products may depend on sectors impacted by climate change
Disruption Building Destruction Physical Building may be in location exposed to increased natural disasters
Disruption Datacenter Disruption Physical Datacenter might be in location exposed to increased natural disasters
Disruption External Payment System Disruption Physical External system facility might be exposed to natural disasters
Disruption Regional Disaster Physical Region might be exposed to increased probability of natural disaster
Disruption Supplier Failure Physical
Supplier might be in location exposed to increased natural disasters, or have an increased probability of default
Error Trading Algorithm Error Transition Market volatility may increase as the result of climate change
Error Trading Error Physical
Probability of human error may increase as temperature increases. Market volatility may increase as the result of climate change
Internal Fraud Unauthorized or Rogue Trading Transition Market volatility may increase as the result of climate change

The second step of the work was, for each scenario, to define a loss generation mechanism, and to quantify the variables of this mechanism. For the variables external to the banks, a quantification was proposed by the ABA based on research on external and robust sources.  The banks were asked to quantify their specific variables, i.e., mainly their exposure to certain risks depending on their internal controls or backup procedures.

We illustrate this process on the Regional Disaster scenario. The loss generation mechanism for this scenario is as follows and represented in a graphical model below.

A regional disaster, for instance an earthquake or a windstorm, hits a geographical area where the bank has significant assets or generates material revenue. The bank's premises - buildings, branches - and the lifelines - electricity, water, telecom, roads - in this area would be partially or fully destroyed. As a consequence, the bank would incur direct and indirect losses:

Disaster Model

As shown in the table below, the quantification of the different drivers is done either from external sources, or by the individual banks.

Category Drivers Bank Assessment External Assessment
Exposure Regions Bank’s Key Regions exposed -
Occurrence Regional disasters -
Probabilities from USGS; GEM NOAA
Impact Rebuild Assets Value, Damage Rate Default Damage Rate
Impact Relocation Relocation Cost
Time to Normal
Default Time to Normal
Impact Revenue Dependent Revenue per Region _

The third step of the work was to quantitatively assess the climate stress of the sensitive drivers.

For instance, the probability of regional disasters is increased, as global warming increases the frequency of hurricanes. Global warming causes a rise in sea levels and increases precipitation during hurricanes. These two factors combined increase the damage caused by hurricanes.

Based on our research, we have considered an increase of 10%-40% by the end of the 21st century for the average probability of a serious hurricanes (Category 1-2). For major hurricanes (Category 3 and more), their frequency has increased by 6% per decade over the four past decades, and an average increase of 28% is expected by 2100, but this projection varies a lot depending on the region (up to +338% for Northeast Pacific).

Results overview

Each of the participating banks has built its own assessment of each of the six operational risk scenarios included in the exercise as listed in the table below.

Operational Risk Category  Scenario Name
Conduct Corporate Client Misrepresentation
Fund Improper Disclosure
Mis-selling Wholesale
Disruption   Building Destruction
Regional Disaster
Supplier Failure

Each assessment was then stressed according to the climate assumptions we considered.

For the sake of simplicity of this first exercise:

The metric stressed was the 1 in 1000 Value at Risk (VaR) of each scenario.

The results are shown below, where each line represents a bank: