As part of our ongoing series on the ECB’s 2026 Geopolitical Reverse Stress Test, we previously explored why channels matter more than numbers and how geopolitical risk shapes failure pathways in reverse stress testing.

Why This Matters

A key objective of the ECB’s 2026 Reverse Stress Test is for banks to assess the resilience of their business model. This requires a comprehensive taxonomy of geopolitical risk drivers linked to business impact via plausible transmission channels. Without this foundation, reverse stress testing becomes guesswork, blind to hidden vulnerabilities and hard to defend under supervisory scrutiny.

Step 1: Identify and Engage the Right Stakeholders

Creating a useful taxonomy is not a siloed exercise. It requires bringing together a cross-functional working group: risk management to define categories and metrics, treasury and finance to assess balance sheet sensitivities, business lines to validate exposures, geography and concentrations, compliance and legal to capture sanction regimes and regulatory constraints. Early engagement ensures the taxonomy reflects the bank’s unique business model and risk profile.

Step 2: Structure the Taxonomy Using a Layered Approach

The starting point is to move from broad geopolitical themes to tangible impacts. Begin by identifying high-level drivers such as sanctions, trade fragmentation, energy disruption or military escalation.

From there, think about how these drivers ripple through the organization—through financial markets, the real economy, and safety & security. The goal is to connect these channels to your business model and balance sheet exposures, and then drill down to measurable risk parameters like PD/LGD shocks or market sensitivities.

Step 3: Apply Robust Modeling Approaches and “Reverse-Orientation”

Once the taxonomy is defined, the next step is to make it actionable. Start with scenario-based analysis to explore plausible geopolitical shocks and their effects across channels. Then, use sensitivity screening to identify which sectors and counterparties are most exposed.

It is not uncommon for this exercise to yield a constellation of viable assumptions leading to the desired outcome; quantitative methods, such as Monte Carlo simulations or optimization methods, can aid in exploring the solution space and guide in the choice of the scenario which best fits the profile and narrative of your organization. The aim is not to build the most complex model but to ensure the taxonomy translates into meaningful insights for decision-making.

Step 4: Leverage External Data and Benchmarks

No taxonomy should be built in isolation. External data adds credibility and depth. Regulatory guidance from the ECB provides a clear baseline, while industry benchmarks and rating agency data can help calibrate sector sensitivities.

Geopolitical risk indices and historical stress events offer valuable context for scenario design. Combining internal insights with external references ensures your taxonomy reflects both supervisory expectations and real-world dynamics.

Step 5: Establish Governance and Documentation

Finally, governance is what turns a taxonomy into a trusted framework. This means securing board-level oversight, involving cross-functional committees, and maintaining clear documentation of assumptions and methodologies.

Regular updates are essential, as geopolitical risks evolve. A well-governed process not only satisfies regulatory scrutiny but also embeds the taxonomy into the bank’s risk culture, making it a living tool rather than a one-off exercise.

How Zanders Can Help

We guide banks through this process end-to-end:

• Quantitative modeling to support or benchmark scenario design.
• Advisory support to design and validate a complete taxonomy.
• Hands-on assistance during stress test exercises, leveraging experience with European banks.
• Tooling development or deployment of our Credit Risk Suite (CRS) for scenario modeling, automated ECL/CET1 impact calculations, and advanced optimization techniques.

Transform compliance into strategic capability for resilience. Contact us to find out how we can help your organization.

Read our previous blog on ECB 2026 Geopolitical Reverse Stress Test.

Why This Matters

For banks, the ECB’s 2026 thematic reverse stress test on geopolitical risk is more than a regulatory exercise. It’s a reality check on failure pathways. The cost of missing how shocks transmit through your business can be severe: capital depletion, liquidity strain, and reputational damage when the next crisis hits.

This is not about ticking boxes or plugging in generic macro scenarios. It is about demonstrating to supervisors and to your board that you understand which channels could break your business model and how those channels map to tangible impacts on capital, liquidity, and operations.

In reverse stress testing, you start from a pre-defined failure outcome and work backwards to plausible scenarios that could cause it. In our previous blog, we argued for a bank-specific taxonomy of geopolitical risk drivers because reverse stress testing is only credible when the transmission channels are correctly selected and explained. Today, we take the next step: translating a risk driver into business‑model impact that can credibly produce the failure condition.

Worked Example: Military Escalation

In the context of Military Escalation, as an illustrative example, let’s consider a conflict disrupting shipping lanes in the South China Sea. The chain of transmission may unfold as follows:

• The incident would disrupt global trade routes, creating severe supply chain¹ bottlenecks and driving up costs for  industries depending on imports from and exports to the region. These disruptions would ripple through the real economy, affecting manufacturing, logistics, energy and semiconductor² sectors, and ultimately impacting banks with concentrated exposures in these sectors.

• Investors would seek safe assets, triggering sharp movements in commodity and foreign exchange markets. Banks with open positions in these markets could face significant mark-to-market losses, while liquidity strains emerge as funding costs rise. In addition, cargo insurance premiums on conflict‑adjacent corridors can spike, prompting re-routing and a broader repricing of risk.

• Operational risks would likely increase. Military tensions often coincide with heightened cyber and/or physical threats, increasing the likelihood of state-sponsored attacks on financial infrastructure. Banks would need to increase investment in cyber defence and resilience measures.

• Sanctions may be imposed by multiple parties, exposing banks to potential breaches and contractual disputes with counterparties linked to conflict zones. This adds complexity to transaction screening and legal oversight.

• Finally, these channels translate into measurable risk parameters. Credit portfolios tied to vulnerable sectors would see severe PD shocks, alongside LGD adjustments for collateral impacted by trade restrictions. Together with the market and operational risk impacts described above, they could erode CET1 ratios, revealing failure pathways that standard stress tests might miss.

Reverse The Logic

Because a reverse stress test starts from the outcome, the final step is iterative - the engine room of the exercise. To reach the targeted impact (300 bps CET1 depletion in the ECB’s thematic exercise), you will likely cycle through the channel, mechanism, risk‑parameter mapping and tune the shocks, strengthening or weakening their severity and duration until the constellation of assumptions consistently delivers the failure condition. But a key fallacy is to treat this as a mechanic “tuning” exercise rather than a careful consideration of which combination of shocks and channels that plausibly will drive CET1 below the threshold?

Scaling This Approach

The method extends to other relevant drivers: sanctions, energy disruptions, cyber threats, but the taxonomy must be granular and defensible, with a clear line‑of‑sight from event to channel, and then to portfolio, risk parameters and capital metrics. This strengthens Reverse Stress Testing credibility and ICAAP alignment, and produces governance‑ready narratives for senior decision‑makers.

How We Can Help

We support banks in building a robust RST framework. Our approach includes:

• Advisory support to design a purposeful, bank-specific taxonomy and link it to ICAAP.
• Quantitative modeling to support or benchmark scenario design.
• Hands-on assistance during stress test exercises, leveraging our experience with several European banks.
• Tool development and deployment of our Credit Risk Suite (CRS) for scenario modeling, automated ECL and CET1 impact calculations, and advanced scenario building.

With Zanders, you can move beyond compliance to create a stress test framework that enhances your strategic capability.

Introduction: Why This Matters Now 

Geopolitical risk has become a defining feature of today’s financial landscape. Trade fragmentation, sanctions, and regional conflicts are reshaping markets and business models. Recognizing this, the European Central Bank (ECB) will run a thematic Reverse Stress Test (RST) on geopolitical risk in 2026 as part of its explicit supervisory priorities. Unlike traditional stress tests, RST starts from a failure condition and works backward to identify plausible scenarios that could lead to this situation.  

Hence, this exercise is not about plugging in generic macro shocks—it’s about uncovering hidden vulnerabilities. And that requires one critical ingredient: an informed and detailed view of how geopolitical events may affect your organization. 

The Key Challenge: Seeing the Full Picture 

To pass muster with supervisors, selecting and explaining the transmission channels will matter far more than the numerical modeling. If relevant channels are missed, the backward search becomes blind, undermining the credibility of the entire exercise. The ECB has made clear that banks must go beyond traditional macroeconomic modeling and identify how disruption to trade flows and supply chains, cyberattacks, and even physical risks related to conflicts might affect banks and their clients, and in turn how this transmits to banks’ capital, liquidity, and operations.  

While the ECB has mapped out the primary pathways through which geopolitical risks propagate, the size and nature of the impact will very much depend on each bank’s location, exposures, and business models — meaning a one-size-fits-all approach will not work. Reverse stress testing is designed to uncover failure pathways, but this only happens if transmission channels have been studied and selected with care.  

Building a granular, bank-specific taxonomy of geopolitical risk drivers and their linkages to the portfolio is therefore a critical step. 

What Does a Geopolitical Risk Taxonomy Look Like? 

Well-defined transmission channels should link high-level risk drivers to specific impacts and risk parameters. For example: 

• Drivers: Trade tensions, sanctions, regional conflicts, cyber threats, energy disruptions, and overall market volatility. 

• Impacts: Credit losses (through direct and indirect exposures), loss of revenue (loss of markets, loss of pricing power), cost increases (funding costs, safety and security measures, insurance premiums, staff compensation and relocation), compliance and legal risks (sanctions breaches, disputes). 

• Risk Parameters: PD and LGD shocks, market risk factors, operational risk metrics. 

Once relevant transmission channels have been defined and quantified, the severity of the shocks to the risk drivers can be tuned so that the targeted reverse stress impact is achieved. In the case of the ECB reverse stress test, a CET1 capital impact of 300 basis points is targeted. Finding a balanced set of shocks to achieve the reverse stress target will require expert judgement and needs to be documented properly. 

A layered approach like this will help ensure that the exercise does not become a paper product but a strategic diagnostic tool that meets supervisors’ expectations. In our next blog, we will spend more time on how to set up a proper taxonomy and make it actionable for your organization. 

How Zanders Can Help 

At Zanders, we support banks in building a robust RST framework. Our approach includes: 

• Quantitative modeling to support or benchmark scenario design.  

• Advisory support to design a purposeful, bank-specific taxonomy and link it to ICAAP. 

• Hands-on assistance during stress test exercises, leveraging our experience with several European banks. 

• Tool development and deployment of our Credit Risk Suite (CRS) for scenario modeling, automated ECL and CET1 impact calculations, and advanced scenario building. 

With Zanders, you can move beyond compliance to create a stress test framework that enhances your strategic capability.

The International Organization for Standardization (ISO) sets global standards to improve quality, safety, and efficiency across industries. ISO 20022, in particular, is transforming financial communications. It provides a universal framework for structured, data-rich messages, improving interoperability between institutions and enabling innovation in payments, securities, and foreign exchange.

Globally, ISO 20022 is replacing older messaging standards, such as SWIFT MT messages, with XML-based messaging. While the worldwide adoption deadline has been extended to November 2026 , Japan is sticking to its original November 2025 timeline—especially for cross-border payments.

Japan’s Transition: Zengin and Cross-Border Payments

Japan’s domestic payment system, Zengin, handles interbank transfers. While the domestic Zengin format remains unchanged, the cross-border Zengin format is being phased out. Japanese banks now require cross-border payments to follow the ISO 20022 XML standard, specifically the “pain.001” message type.

This shift affects corporates as well. Banks are encouraging clients to submit payments in XML format rather than converting older MT101 or cross-border Zengin messages which means Treasury Management Systems (TMS) and ERP systems may need updates.

Opportunities and Challenges for Corporates

ISO 20022 requires structured data, particularly for beneficiary addresses. While Treasury payments to a limited number of counterparties may be manageable, handling tens of thousands of global vendors is far more complex. Many corporates face inconsistent or outdated vendor data, which may not meet ISO standards.

Tools for master data cleanup, like those which can proposed by Zanders, will automate validation and ensure compliance, helping corporates navigate this transition efficiently.

Taking Action Now

Even though Japan is leading with its November 2025 deadline, corporates worldwide are encouraged to start preparing. Steps include:

• Assessing current processes and systems
• Updating ERP and TMS inputs for hybrid or fully structured address formats
• Leveraging master data validation tools

Conclusion

Japan’s commitment to ISO 20022 is a pivotal moment for cross-border financial transactions. Corporates must act quickly, adopting structured data practices to ensure compliance and maintain operational efficiency. With the right tools and preparation, businesses can turn this regulatory shift into an opportunity to standardize and modernize their financial messaging. Zanders can provide support to companies, offering high-end solutions and expert guidance to navigate the complexities of ISO 20022 adoption.

On July 28th, the European Central Bank (ECB) published its revised guide to internal models ECB publishes revised guide to internal models. On top of changes necessary for alignments with CRR3, the ECB improves their guidelines based on supervisory experience with the aim of harmonized and transparent internal modeling practices at credit institutions. 

In this article, we share our perspective on the changes in the credit risk chapter, focusing on the impact on PD, LGD and CCF modeling: 

1- PD: Institutions must use at least five years of default data and demonstrate a meaningful correlation between default rates and macroeconomic indicators for LRA DR calibration. 

2- LGD: The ECB will benchmark calibration windows against the 2008–2018 period. Downturn LGD calibration must cover all relevant components and include yearly elevated LGDs, even if they don’t perfectly match downturn periods. The LGD reference value is now an active challenge in model validation, requiring consistent calculation and action if weaknesses appear. 

3- CCF: For CCF modeling, risk drivers must use data exactly 12 months before default, 0% CCFs for non-retail exposures are no longer allowed, and negative CCFs must be floored at zero, while high CCFs remain uncapped.

The following chapters elaborate on these three proposed amendments in more detail.

1 PD

1.1 Minimum requirements historical data 

The ECB now specifies data requirements for the representativeness analysis referenced in paragraphs 82 and 83 of the EBA Guidelines on PD and LGD estimation (EBA/GL/2017/16), which assess whether the PD calibration dataset reflects a balanced mix of "good" and "bad" years. 

According to paragraph 236(A) of the revised EGIM, institutions must use a minimum of 5 years of historical data as of the calibration date. Additionally, they must have enough one-year default rates to calculate a statistically meaningful correlation between default rates and relevant (macro)economic indicators. 

If an institution lacks the required data (either the 5 years or enough data for meaningful correlation), paragraph 236(B) states the period is automatically deemed not representative, and the institution must apply adjustments and quantify a Margin of Conservatism (MoC). However, paragraph 236(C) allows that if an institution has enough data but cannot find significant correlations with macroeconomic indicators, the historical period may still be considered representative if it includes both the minimum and maximum of the institution’s internal one-year default rates. 

1.2 LRA DR reference value 

The ECB introduces a reference calibration level for LRA DR in paragraph 237, based on the period January 2008 to December 2018, which is considered to be representative of a full economic cycle. Institutions must calculate this reference LRA DR and use it as an anchor value. If an institution proposes a lower LRA DR based on a different time period, it must justify why that period is more representative. Although not a strict floor, the reference LRA DR serves as an anchor value for ECB assessment. Even without sufficient default data, institutions must estimate the reference LRA DR using the accounting definition of default (DoD) as an approximation for the prudential definition. 

2 LGD

For the loss given default (LGD) risk parameter, the ECB now sets out clearer expectations on two areas: the estimation of downturn LGD based on observed impact and the calculation and use of the LGD reference value.  

2.1 Calibration of downturn LGD based on observed impact 

The revised EGIM retains flexibility in how downturn LGD is calibrated but introduces stricter requirements for the analyses underpinning the calibration based on observed impact. Zanders expects that most institutions already conduct these analyses and may only need to refine their application. 

The ECB emphasizes that all analyses¹ required under paragraph 27(a) of the EBA guidelines must be conducted. If calibration is done at the component level (e.g., secured vs. unsecured), these analyses should be done separately for each component, with results aggregated into a total downturn LGD. At minimum, the most impactful component must be included; others may be needed if it doesn't capture downturn effects sufficiently. 

Regardless of calibration method, elevated realized LGDs (e.g. defined as the average of defaults in a given year) must be used.. Although downturns are often defined more granularly, the ECB states that yearly averages should still be used, even if they don’t align exactly with downturn periods. 

2.2 Reference value 

The LGD reference value, unlike the new LRA DR, is an existing non-binding benchmark from the EBA guidelines meant to challenge an institution’s downturn LGD estimates. While still non-binding, the ECB has raised expectations for how it should be calculated and used. Zanders supports this, seeing it as a useful tool for diagnosing weaknesses in LGD calibration. 

The reference value should follow paragraph 37 of the EBA guidelines², typically calculated as the average LGD in the two years with the highest economic loss. The ECB emphasizes that incomplete recovery processes must be included in identifying these years and in the LGD calculations, and that this should be done at least at the calibration-segment level. 

For comparing the reference value with actual downturn LGD estimates (per paragraph 19), the ECB offers guidance when the reference value is higher. If this isn’t due to a missed downturn period, institutions must reassess their calibration methodology for possible flaws. If a missed downturn may be the reason, they are expected to re-evaluate their downturn identification and consider timing lags between downturn events and losses. 

3. CCF 

The main changes to the Credit Conversion Factor (CCF) guidelines focus on calibration and aim to reduce variability across institutions' modeling practices. The ECB is aligning with the EBA’s goal of lowering RWA variability: EBA’s Revised Definition of Default - Zanders.  

Key updates include: 

• AIRB Approach: Institutions can now only use risk driver data from exactly 12 months before default (the reference date), eliminating the option to consider longer-term behavioral patterns.  

• FIRB Approach: The ECB has removed the option for institutions to justify using a 0% CCF for non-retail exposures through an annual materiality analysis. As a result, institutions not using their own CCF models must now apply the standardised (SA) CCFs under Article 168(8a). 

• Negative CCFs: In line with CRR3 (effective January 2025), negative observed CCFs must be floored at zero. However, very high CCFs (over 100%) are not capped, and the ECB provides no specific guidance on handling such outliers. Zanders recommends isolating these into separate grades or pools and reviewing the data in detail to correct any structural issues. 

Conclusion 

This post highlights the most relevant changes in the ECB guide to internal models for IRB credit risk modeling. Institutions must use at least five years of default data and demonstrate a meaningful correlation between default rates and macroeconomic indicators for LRA DR calibration. The ECB will benchmark calibration windows against the 2008–2018 period. Downturn LGD calibration must cover all relevant components and include yearly elevated LGDs, even if they don’t perfectly match downturn periods. The LGD reference value is now an active challenge in model validation, requiring consistent calculation and action if weaknesses appear. For CCF modeling, risk drivers must use data exactly 12 months before default, 0% CCFs for non-retail exposures are no longer allowed, and negative CCFs must be floored at zero, while high CCFs remain uncapped. 

Reach out to our experts John de Kroon and Dick de Heus, if you are interested in getting a better understanding of what the proposed amendments mean for your credit risk portfolio.  

Zanders actively monitors regulatory updates relevant for (credit) risk modeling. Keep a close eye on our LinkedIn and website for more information or subscribe to our newsletters here.  

In total 104 banks participated in the stress test that was intended as a learning exercise, for the ECB and the participating banks alike. In this article we provide a brief overview of the main results.

The ECB’s goal with the climate risk stress test was to assess the progress banks have made in developing climate risk stress-testing frameworks and the corresponding projections, as well as understanding the exposures of banks with respect to both transition and physical climate change risks. The stress test therefore consisted of three modules: 1) a qualitative questionnaire to assess the bank’s climate risk stress testing capabilities, 2) two climate risk metrics showing the sensitivity of the banks’ income to transition risk and their exposure to carbon emission-intensive industries, and 3) constrained bottom-up stress test projections for four scenarios specified by the ECB¹. The third module only had to be completed by 41 directly supervised banks to limit the burden for some of the smaller banks included in the climate risk stress test.

An understatement of the true risk

The constrained bottom-up stress test projections show that the combined market and credit risk losses for the 41 banks in the sample amount to approximately EUR 70 billion in the short-term disorderly transition scenario. The ECB emphasizes that this probably is an understatement of the true risk, because it does not consider the scenarios underlying the stress test to be ‘adverse’. Second round economic effects from climate risk changes have, for example, not been factored in. Furthermore, only a third of the total exposures of the 41 banks were in scope and, on top of that, the ECB considers the banks’ modeling capabilities to be ‘rudimentary’ in this stage: they report that around 60% of the banks do not yet have a well-integrated climate risk stress testing framework in place, and they expect that it will take several years before banks achieve this. Even though banks are not meeting the ECB’s expectations yet, the ECB does conclude that banks have made considerable progress with respect to their climate stress testing capabilities.

Be aware of clients’ transition plans

A further analysis of the results shows that the share of interest income related to the 22 most carbon-intensive industries amounts to more than 60% of the total non-financial corporate interest income (on average for the banks in the sample). Interestingly, this is higher than the share of these sectors (around 54%) in the EU economy in terms of gross added value. The ECB argues that banks should be very much aware of the transition plans of their clients to manage potential future transition risks in their portfolio. The exposure to physical risks is much more varied across the sample of banks. It primarily depends on the geographical location of their lending portfolios’ assets.

The ECB points out that only a few banks account for climate risk in their credit risk models. In many cases, the credit risk parameters are fairly insensitive to the climate change scenarios used in the stress test. They also report that only one in five banks factor climate risk into their loan origination processes. A final point of attention is data availability. In many cases, proxies instead of actual counterparty data have been used to measure (for example) greenhouse gas emissions, especially for Scope 3. Consequently, the ECB is also promoting a higher level of customer engagement to improve in this area.

Many deficiencies, data gaps and inconsistencies

The outcome of the climate risk stress test will not have direct implications for a bank’s capital requirements, but it will be considered from a qualitative point of view as part of the Supervisory Review and Evaluation Process (SREP). This will be complemented by the results from the ongoing thematic review that is focused on the way banks consider climate-related and environmental risks into their risk management frameworks. The combination will indicate to the ECB how well a bank is meeting the expectations laid down in the ‘Guide on climate-related and environmental risks’ that was published by the ECB in November 2020.

The ECB notes that the exercise revealed many deficiencies, data gaps and inconsistencies across institutions and expects banks to make substantial further progress in the coming years. Furthermore, the ECB concludes that banks need to increase customer engagement to obtain relevant company-level information on greenhouse gas emissions, as well as to invest further in the methodological assumptions that are used to arrive at proxies.

If you are looking for support with the integration of Environmental, Social, and Governance risk factors into your existing risk frameworks, please reach out to us.

Notes
1) See our earlier article on the ECB’s climate stress test methodology for more details.