The ECB published the results of its climate risk stress test

July 2022
7 min read

On Friday 8 July, the European Central Bank (ECB) published the results of the climate risk stress test that was performed in the first half of this year.


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.

Integrating ESG risks into a bank’s credit risk framework

July 2022
4 min read

On Thursday 9 June, we hosted a roundtable in our head office in Utrecht titled ’Integrating ESG risks into a bank’s credit risk framework’. The roundtable was attended by credit and climate risk managers, as well as model validators, working for Dutch banks of different sizes. In this article we briefly describe Zanders’ view on this topic and share the key insights of the roundtable.


The last three to four years have seen a rapid increase in the number of publications and guidance from regulators and industry bodies. Environmental risk is currently receiving the most attention, triggered by the alarming reports from the Intergovernmental Panel on Climate Change (IPCC). These reports show that it is a formidable, global challenge to shift to a sustainable economy in order to reduce environmental impact.

Zanders’ view

Zanders believes that banks have an important role to play in this transition. Banks can provide financing to corporates and households to help them mitigate or adapt to climate change, and they can support the development of new products such as sustainability-linked derivatives. At the same time, banks need to integrate ESG risk factors into their existing risk processes to prepare for the new risks that may arise in the future. Banks and regulators so far have mostly focused on credit risk.

We believe that the nature and materiality of ESG risks for the bank and its counterparties should be fully understood, before making appropriate adjustments to risk models such as rating, pricing, and capital models. This assessment allows ESG risks to be appropriately integrated into the credit risk framework. To perform this assessment, banks may consider the following four steps:

  • Step 1: Identification. A bank can identify the possible transmission channels via which ESG risk factors can impact the credit risk profile of the bank. This can be through direct exposures, or indirectly via the credit risk profile of the bank’s counterparties. This can for example be done on portfolio or sector level.
  • Step 2: Materiality. The materiality of the identified ESG risk factors can be assessed by assigning them scores on impact and likelihood. This process can be supported by identifying (quantitative) internal and external sources from the Network for Greening the Financial System (NGFS), governmental bodies, or ESG data providers.
  • Step 3: Metrics. For the material ESG risk factors, relevant and feasible metrics may be identified. By setting limits in line with the Risk Appetite Statement (RAS) of the bank, or in line with external benchmarks (e.g., a climate science-based emission path that follows the Paris Agreement), the exposure can be managed.
  • Step 4: Verification. Because of the many qualitative aspects of the aforementioned steps, it is important to verify the outcomes of the assessment with portfolio and credit risk experts.

Key insights

Prior to the roundtable, Zanders performed a survey to understand the progress that Dutch banks are marking with the integration of ESG risks into their credit risk framework, and the challenges they are facing.

Currently, when it comes to incorporating ESG risks in the credit risk framework, banks are mainly focusing their attention on risk identification, the materiality assessment, risk metric definition and disclosures. The survey also reveals that the level of maturity with respect to ESG risk mitigation and risk limits differs significantly per bank. Nevertheless, the participating banks agreed that within one to three years, ESG risk factors are expected to be integrated in the key credit risk management processes, such as risk appetite setting, loan origination, pricing, and credit risk modeling. Data availability, defining metrics and the quantification of ESG risks were identified by banks as key challenges when integrating ESG in credit risk processes, as illustrated in the graph below.

In addition to the challenges mentioned above, discussion between participating banks revealed the following insights:

  • Insight 1: Focus of ESG initiatives is on environmental factors. Most banks have started integrating environmental factors into their credit risk management processes. In contrast, efforts for integrating social and governance factors are far less advanced. Participants in the roundtable agreed that progress still has to be made in the area of data, definitions, and guidelines, before social and governance factors can be incorporated in a way that is similar to the approach for environmental factors.
  • Insight 2: ESG adjustments to risk models may lead to double counting. The financial market still needs to gain more understanding to what extent ESG risk factor will manifest itself via existing risk drivers. For example, ESG factors such as energy label or flood risk may already be reflected in market prices for residential real estate. In that case, these ESG factors will automatically manifest itself via the existing LGD models and separate model adjustments for ESG may lead to double counting of ESG impact. Research so far shows mixed signals on this. For example, an analysis of housing prices by researchers from Tilburg University in 2021 has shown that there is indeed a price difference between similar houses with different energy labels. On the other hand, no unambiguous pricing differentiation was found as part of a historical house price analysis by economists from ABN AMRO in 2022 between similar houses with different flood risks. Participating banks agreed that further research and guidance from banks and the regulators is necessary on this topic.
  •  Insight 3: ESG factors may be incorporated in pricing. An outcome of incorporating ESG in a credit risk framework could be that price differentiation is introduced between loans that face high versus low climate risk. For example, consideration could be given to charging higher rates to corporates in polluting sectors or ones without an adequate plan to deal with the effects of climate change. Or to charge higher rates for residential mortgages with a low energy label or for the ones that are located in a flood-prone area. Participating banks agreed that in theory, price differentiation makes sense and most of them are investigating this option as a risk mitigating strategy. Nevertheless, some participants noted that, even if they would be able to perfectly quantify these risks in terms of price add-ons, they were not sure if and how (e.g., for which risk drivers and which portfolios) they would implement this. Other mitigating measures are also available, such as providing construction deposits to clients for making their homes more sustainable.

Conclusion

Most participating banks have made efforts to include ESG risk factors in their credit risk management processes. Nevertheless, many efforts are still required to comply with all regulatory expectations regarding this topic. Not only efforts by the banks themselves but also from researchers, regulators, and the financial market in general.

Zanders has already supported several banks and asset managers with the challenges related to integrating ESG risks into the risk organization. If you are interested in discussing how we can help your organization, please reach out to Sjoerd Blijlevens or Marije Wiersma.

Climate change risk

April 2022
7 min read

On Friday 8 July, the European Central Bank (ECB) published the results of the climate risk stress test that was performed in the first half of this year.


The Working Group II contribution to the IPCC’s Sixth Assessment Report states that “climate change is a grave and mounting threat to our wellbeing and a healthy planet”. It is a formidable, global challenge to transition to a sustainable economy before time is running out. Banks have an important role to play in this transition. By stepping up to this role now, banks will be better prepared for the future, and reap the benefits along the way.

In the Paris Agreement (or COP21), adopted in December 2015, 196 parties agreed to limit global warming to well below 2.0°C, and preferably to no more than 1.5°C. To prevent irreversible impacts to our climate, the IPCC stresses that the increase in global temperature (relative to the pre-industrial era) needs to remain below 1.5°C. To achieve this target, a rapid and unprecedented decrease in the emission of greenhouse gasses (GHG) is required. With CO2 emissions still on the rise, as depicted in Figure 1, the challenge at hand has increased considerably in the past decade.

Figure 1 – Annual CO2 emissions from fossil fuels.

To further illustrate this, Figure 2 depicts for several starting years a possible pathway in CO2 reductions to ensure global warming does not exceed 2.0°C. Even under the assumption that CO2 emissions have already peaked, it becomes clear that the required speed of CO2 reductions is rapidly increasing with every year of inaction. We are a long way from limiting global warming to 2.0°C. This even holds under the assumption that all countries’ pledges to reduce GHG emissions will be achieved, as can be observed in Figure 3.

To quote the IPCC Working Group II co-chair Hans-Otto Pörtner: “Any further delay in concerted anticipatory global action on adaptation and mitigation will miss a brief and rapidly closing window of opportunity to secure a livable and sustainable future for all.”

Figure 2 – CO2 reduction need to limit global warming.

Figure 3 – Global GHG emissions and warming scenario’s.

The role of banks in the transition – and the opportunities it offers

Historically, banks have been instrumental to the proper functioning of the economy. In their role as financial intermediaries, they bring together savers and borrowers, support investment, and play an important role in facilitating payments and transactions. Now, banks have the opportunity to become instrumental to the proper functioning of the planet. By allocating their available capital to ‘green’ loans and investments at the expense of their ‘brown’ counterparts, banks can play a pivotal role in the transition to a sustainable economy. Banks are in the extraordinary position to make a fundamentally positive contribution to society. And even better, it comes with new opportunities.

The transition to a sustainable economy requires huge investments. It ranges from investments in climate change adaption to investments in GHG emission reductions: this covers for example investments in flood risk warning systems and financing a radical change in our energy mix from fossil-fuel based sources (like coal and natural gas) to clean energy sources (like solar and wind power). According to the Net Zero by 2050 Report from the International Energy Agency (IEA)2, the annual investments in the energy sector alone will increase from the current USD 2.3 trillion to USD 5.0 trillion by 2030. Hence, across sectors, the increase in annual investments could easily be USD 3.0 to 4.0 trillion. As much as 70% of this investment may need to be financed by the private sector (including banks and other financial institutions)3. To put this in perspective, the total outstanding credit to non-financial corporates currently stands at USD 86.3 trillion4. Assuming an average loan maturity of 5 years, this would translate to a 12-16% increase in loans and investments by banks on a global level. Hence, the transition to a sustainable economy will open up a large market for banks through direct investments and financing provided to corporates and households.

The transition to a sustainable economy is also triggering product development. The Climate Bonds Initiative (CBI) for example reports that the combined issuance of Environmental, Social, and Governance (ESG) bonds, sustainability-linked bonds and transition debt reached almost USD 500 billion in 2021H1, representing a 59% year-on-year growth rate. Other initiatives include the introduction of ‘green’ exchange-traded funds (ETFs) and sustainability-linked derivatives. The latter first appeared in 2019 and they provide an incentive for companies to achieve sustainable performance targets. If targets are met, a company is for example eligible for a more attractive interest coupon. Again, this is creating an interesting market for banks.

By embracing the transition, with all the opportunities that it offers, banks are also bracing themselves for the future. Banks that adopt climate change-resilient business models and integrate climate risk management into their risk frameworks will be much better positioned than banks that do not. They will be less exposed to climate-related risks, ranging from physical and transition risks to risks stemming from a reputational perspective or litigation, also justifying lower capital requirements. The early adaptors of today will be the leaders of tomorrow.

A roadmap supporting the transition

How should a bank approach this transition? As depicted in Figure 4, we identify four important steps: target setting, measurement and reporting, strategy and risk framework, and engaging with clients.

Figure 4 – The roadmap supporting the transition to a sustainable economy.

Target setting

The starting point for each transition is to set GHG emission targets in alignment with emission pathways that have been established by climate science. One important initiative that can support banks in setting these targets is the Science Based Targets initiative (SBTi). This organization supports companies to set targets in line with the goals of the Paris Agreement. Unlike many other companies, the majority of a bank’s GHG emissions are outside their direct control. They can influence, however, their so-called financed emissions, which are the GHG emissions coming from their lending and investment portfolios. The SBTi has developed a framework for banks that reflects this. It encourages banks to use the Absolute Contraction approach that requires a 2.5% (for a well-below 2.0°C target) or 4.2% (for a 1.5°C target) annual reduction in GHG emissions. A clear emission pathway guides banks in the subsequent steps of the transition process.

Measurement and reporting

With targets in place, the next important step for a bank is to determine their level of GHG emissions: both for scope 1 and 2 (the GHG emissions they control) and for scope 3 (the financed emissions). Important initiatives for the quantification of the financed emissions are the Paris Agreement Capital Transition Assessment (PACTA) and the Platform Carbon Accounting Financials (PCAF). PCAF is a Dutch initiative to deliver a global, standardized GHG accounting and reporting approach for financial institutions (building on the GHG Protocol). PACTA enables banks to measure the alignment of financial portfolios with climate scenarios.

By keeping track of the level of GHG emissions on an annual basis, banks can assess whether they are following their selected emission pathway. Reporting, in line with the recommendations of the Task Force on Climate-Related Financial Disclosures (TCFD), will contribute to a greater understanding of climate risks with their investors and other stakeholders.

Strategy and risk framework

Setting targets and measuring the current level of GHG emissions are necessary but not sufficient conditions to achieve a successful transition. Climate change risk needs to be fully integrated into a bank’s strategy and its risk framework. To assess the climate change-resilience of a bank’s strategy, a logical first step is to understand which climate change risks are material to the organization (e.g., by composing a materiality matrix). Subsequently, studying the transmission channels of these risks using scenario analysis and/or stress testing creates an understanding of what parts of the business model and lending portfolio are most exposed. This could lead to general changes in a bank’s positioning, but these risks should also be factored into the bank’s existing risk framework. Examples are the loan origination process, capital calculations, and risk reporting.

Engaging with clients

A fourth step in the game plan to successfully support the transition is for a bank to actively engage with its clients. A dialogue is required to align the bank’s GHG emission targets with those of its clients. This extends to discussing changes in the operations and/or business model of a client to align with a sustainable economy. This also may include timely announcing that certain economic activities will no longer be financed, and by financing client’s initiatives to mitigate or adapt to climate change: e.g., financing wind turbines for clients with energy- or carbon-intensive production processes (like cement or aluminium) or financing the move of production locations to less flood-prone areas.

Conclusion

Banks are uniquely positioned to play a pivotal role in the transition to a sustainable economy. The transition is already providing a wide range of opportunities for banks, from large financing needs to the introduction of green bonds and sustainability-linked derivatives. At the same time, it is of paramount importance for banks to adopt a climate change-resilient strategy and to integrate climate change risk into their risk frameworks. With our extensive track record in financial and non-financial risk management at financial institutions, Zanders stands ready to support you with this ambitious, yet rewarding challenge.

ESG risk management and Zanders

Zanders is currently supporting several clients with the identification, measurement, and management of ESG risks. For a start, we are supporting a large Dutch bank with the identification of ESG risk factors that have a material impact on the credit risk profile of its portfolio of corporate loans. The material risk factors are then integrated in the bank’s existing credit risk framework to ensure a proper management of this new risk type.

We are supporting other banking clients with the quantification of climate change risk. In one case, we are determining climate change risk-adjusted Probabilities of Default (PDs). Using expected future emissions and carbon prices based on the climate change scenarios of the Network for Greening the Financial System (NGFS), company specific shocks based on carbon prices and country specific shocks on GDP level are determined. These shocked levels are then used to determine the impact on the forecasted PDs. In another case, we are investigating the potential impact of floods and droughts on the collateral value of a portfolio of residential mortgage loans.

We also gained experience with the data challenges involved in the typical ESG project: e.g., we are supporting an asset manager with integrating and harmonizing ESG data from a range of vendors, which is underlying their internally developed ESG scores. We also support them with embedding these scores in the investment process.

With our extensive track record in financial and non-financial risk management at financial institutions in general, and our more recent ESG experience, Zanders stands ready to support you with the ambitious, yet rewarding challenge to adopt a climate change-resilient strategy and to integrate climate change risk in your existing risk frameworks.

Foot notes:

1 The IPCC Working Group II contribution: Climate Change 2022: Impacts, Adaptation and Vulnerability.
2 The IEA report, Net Zero by 2050.
3 See the Net Zero Financing roadmaps from the UN, ‘Race to Zero’ and the ‘Glasgow Financial Alliance for Net Zero’ (GFANZ).
4 Based on the statistics of the Bank for International Settlements (BIS) per 2021-Q3.
5 The Climate Bonds Initiative’s Sustainable Debt Highlights H1 2021.

EBA’s binding standards on Pillar 3 disclosures on ESG risks

February 2022
4 min read

On Thursday 9 June, we hosted a roundtable in our head office in Utrecht titled ’Integrating ESG risks into a bank’s credit risk framework’. The roundtable was attended by credit and climate risk managers, as well as model validators, working for Dutch banks of different sizes. In this article we briefly describe Zanders’ view on this topic and share the key insights of the roundtable.


This publication fits nicely into the ‘horizon priority’ of the EBA1 to provide tools to banks to measure and manage ESG-related risks. In this article we present a brief overview of the way the ITS have been developed, what qualitative and quantitative disclosures are required, what timelines and transitional measures apply – and where the largest challenges arise. By requiring banks to disclose information on their exposure to ESG-related risks and the actions they take to mitigate those risks – for example by supporting their clients and counterparties in the adaptation process – the EBA wants to contribute to a transition to a more sustainable economy. The Pillar 3 disclosure requirements apply to large institutions with securities traded on a regulated market of an EU member state.

In an earlier report2, the EBA defined ESG-related risks as “the risks of any negative financial impact on the institution stemming from the current or prospective impacts of ESG factors on its counterparties or invested assets”. Hence, the focus is not on the direct impact of ESG factors on the institution, but on the indirect impact through the exposure of counterparties and invested assets to ESG-related risks. The EBA report also provides examples for typical ESG-related factors.
While the ITS have been streamlined and simplified compared to the consultation paper published in March 2021, there are plenty of challenges remaining for banks to implement these standards.

Development of the ITS

The EBA has been mandated to develop the ITS on P3 disclosures on ESG risks in Article 434a of the Capital Requirements Regulation (CRR). The EBA has opted for a sequential approach, with an initial focus on climate change-related risks. This is further narrowed down by only considering the banking book. The short maturity and fast revolving positions in the trading book are out of scope for now. The scope of the ITS will be extended to included other environmental risks (like loss of biodiversity), and social and governance risks, in later stages.

In the development of the ITS, the EBA has strived for alignment with several other regulations and initiatives on climate-related disclosures that apply to banks. The most notable ones are listed below (and in Figure 1):

Figure 1 – Overview of related regulations and initiatives considered in the development of the ITS

  • Capital Requirements Directive and Regulation (CRD and CRR): article 98(8) of the CRD3 mandated the EBA to publish the EBA report on Management and Supervision of ESG risks, which includes the split of climate change-related risks in physical and transition risks. Article 434a of the CRR4 mandated the EBA to develop the draft ITS to specify the ESG disclosure requirements described in article 449a.
  • EBA report on Management and Supervision of ESG risks2: the report provides common definitions of ESG risks and contains proposals on how to include ESG risks in the risk frameworks of banks, covering its identification, assessment, and management. It also discusses the way to include ESG risks in the supervisory review process.
  • Task Force on Climate-related Financial Disclosures (TCFD)5: the Financial Stability Board’s TFCD has published recommendations on climate-related disclosures. The metrics and Key Performance Indicators (KPIs) included in the ITS have been aligned with the TCFD recommendations.
  • Taxonomy Regulation6: the European Union’s common classification system of environmentally sustainable economic activities is underpinning the main KPIs introduced in the ITS.
  • Climate Benchmark Regulation (CBR)7: In the CBR, two types of climate benchmarks were introduced (‘EU Climate Transition’ and ‘EU Paris-aligned’ benchmarks) and ESG disclosures for all other benchmarks (excluding interest rate and currency benchmarks) were required.
  • Non-Financial Reporting Directive (NFRD)8: the NFRD introduces ESG disclosure obligations for large companies, which include climate-related information.
  • Corporate Sustainability Reporting Directive (CSRD)9: a proposal by the European Commission to extend the scope of the NFRD to also include all companies listed on regulated markets (except listed micro-enterprises). One of the ITS’s KPIs, the Green Asset Ratio (GAR) is directly linked to the scope of the NFRD/CSRD.
  • Sustainable Finance Disclosure Regulation (SFDR)10: the SFDR lays down sustainability disclosure obligations for manufacturers of financial products and financial advisers towards end-investors. It applies to banks that provide portfolio management investment advice services.

Compared to the consultation paper for the ITS, several changes have been made to the required templates. Some templates have been combined (e.g., templates #1 and #2 from the consultation paper have been combined into template #1 of the final draft ITS) and several templates have been reorganized and trimmed down (e.g., the requirement to report exposures to top EU or top national polluters has been removed).

Quantitative disclosures

The ITS on P3 disclosure on ESG risks introduce ten templates on quantitative disclosures. These can be grouped in four templates on transition risks, one on physical risks, and five on mitigating actions:

  • Transition risks
    Two of the required templates are relatively straightforward. Banks need to report the energy efficiency of real estate collateral in the loan portfolio (#2) and report their aggregate exposure to the top 20 of the most carbon-intensive firms in the world (#4).
    The main challenge for banks though will be in completing the other two templates:
    • Template #1 requires banks to disclose the gross carrying amount of loans and advances provided to non-financial corporates, classified by NACE sector codes and residual maturities. It is also required to report on the counterparties’ scope 1, 2, and 3 greenhouse gas (GHG) emissions. Reflecting the challenge in reporting on scope 3 emissions, a transitional measure is in place. Full reporting needs to be in place by June 2024. Until then, banks need to report their available estimates (if any) and explain the methodologies and data sources they intend to use.
    • In the last template (#3), banks also need to report scope 3 emissions, but relate these to the alignment metrics defined by the International Energy Agency (IEA) for the ‘net zero by 2050’ scenario. For this scenario, a target for a CO2 intensity metric is defined for 2030. By calculating the distance to this target, it becomes clear how banks are progressing (over time) towards supporting a sustainable economy. A similar transitional measure applies as for template #1.
  • Physical risks
    In template #5, banks are required to disclose how their banking book positions are exposed to physical risks, i.e., “chronic and acute climate-related hazards”. The exposures need to be reported by residual maturity and by NACE sector codes and should reflect exposure to risks like heat waves, droughts, floods, hurricanes, and wildfires. Specialized databases need to be consulted to compile a detailed understanding of these exposures. To support their submissions, banks further need to compile a narrative that explains the methodologies they used.
  • Mitigating actions
    The final set of templates covers quantitative information on the actions a bank takes to mitigate or adapt to climate change risks.
    • Templates #6-8 all relate to the GAR, which indicates what part of the bank’s banking book is aligned with the EU’s Taxonomy:
      • In template #7, banks need to report the outstanding banking book exposures to different types of clients/issuers, as well as the amount of these exposures that are taxonomy-eligible (that is, to sectors included in the EU Taxonomy) and taxonomy-aligned (that is, taxonomy-eligible exposures financing activities that contribute to climate change mitigation or adaptation). Based on this information, the bank’s GAR can be determined.
      • In template #8, a GAR needs to be reported for the exposures to each type of client/issuer distinguished in template #7, with a distinction between a GAR for the full outstanding stock of exposures per client/issuer type, and a GAR for newly originated (‘flow’) exposures.
      • Template #6 contains a summary of the GARs from templates #7 and #8.
        In these templates, the numerator of the GAR only includes exposures to non-financial corporations that are required to publish non-financial information under the NFRD. Any exposures to other corporate counterparties therefore are considered 0% Taxonomy-aligned.
    • The main challenge in this group of templates is in template #9. To incentivize banks to support all of their counterparties to transition to a more sustainable business model, and to collect ESG data on these counterparties, the EBA introduces the Banking Book Taxonomy Alignment Ratio (BTAR). In this metric, the numerator does include the exposures to counterparties that are not subject to NFRD disclosure obligations. The BTAR ratios obtained from the information in template #9 therefore complement the GAR ratios obtained in templates #7 and #8.
    • In the final template (#10), banks have the opportunity to include any other climate change mitigating actions that are not covered by the EU Taxonomy. They can for example report on their use of green or sustainable bonds and loans.

An overview of the templates for quantitative disclosures in presented in Figure 2.

Qualitative disclosures

In the ITS on P3 disclosures on ESG risks, three tables are included for qualitative disclosures. The EBA has aligned these tables with their Report on Management and Supervision of ESG risks11. The three tables are set up for qualitative information on environmental, social, and governance risks, respectively. For each of these topics, banks need to address three aspects: on business strategy and processes, governance, and risk management. An overview of the required disclosures is presented in Figure 3.

Figure 3 – Overview of qualitative ESG disclosures (based on templates & section 2.3.2 of the EBA ITS on P3 disclosures on ESG risks)

Timelines and transitional measures

The ITS on P3 disclosure on ESG risks become effective per 30 June 2022 for large institutions that have securities traded on a regulated market of an EU member state. A semi-annual disclosure is required, but the first disclosure is annual. Consequently, based on 31 December 2022 data, the first reporting will take place in the first quarter of 2023.

The EBA has introduced a number of transitional measures. These can be summarized as follows:

  • The reporting of information on the GAR is only required as of 31 December 2023.
  • The reporting of information on the BTAR, the bank’s financed scope 3 emissions, and the alignment metrics is only required as of June 2024.

The EBA has further indicated in the ITS that they will conduct a review of the ITS’s requirements during 2024. They may then also extend the ITS with other environmental risks (other than the climate change-related risks in the current version). The EU Taxonomy is expected to cover a broader range of environmental risks by the end of 2022. Sometime after 2024, it is expected that the EBA will further extend the ITS by including disclosure requirements on social and governance risks.

An overview of the main timelines and transitional measures is presented in Figure 4.

Figure 4 – Overview of the main timelines and transitional measures for the ESG disclosures

Conclusion

Society, and consequently banks too, are increasingly facing risks stemming from changes in our climate. In recent years, supervisory authorities have stepped up by introducing more and more guidance and regulation to create transparency about climate change risk, and more broadly ESG risks. The publication of the ITS on P3 disclosures on ESG risks by the EBA marks an important milestone. It offers banks the opportunity to disseminate a constructive and positive role in the transition to a sustainable economy.

Nonetheless, implementing the disclosure requirements will be a challenge. Developing detailed assessments of the physical risks to which their asset portfolio is exposed and to estimate the scope 3 emissions of their clients and counterparties (‘financed emissions’) will not be straightforward. For their largest counterparties, banks will be able to profit from the NFRD disclosure obligations, but especially in Europe a bank’s portfolio typically has many exposures to small- and medium-sized enterprises. Meeting the disclosure requirements introduced by the EBA will require timely and intensive discussions with a substantial part of the bank’s counterparties.

Banks also need to provide detailed information on how ESG risks are reflected in the bank’s strategy and governance and incorporated in the risk management framework. With our extensive knowledge on market risk, credit risk, liquidity risk, and business risk, Zanders is well equipped to support banks with integrating the identification, measurement, and management of climate change-related risks into existing risk frameworks. For more information, please contact Pieter Klaassen or Sjoerd Blijlevens via +31 88 991 02 00.

References
  1. See the EBA 2022 Work Programme.
  2. The EBA’s Report on Management and Supervision of ESG risks for credit institutions and investment firms, published in June 2021.
  3. See the EBA’s interactive Single Rulebook.
  4. See Regulation (EU) 2019/876.
  5. See the TCFD’s Final Report on Recommendations of the Task Force on Climate-related Financial Disclosures published in June 2017.
  6. See the EBA’s response to EC Call for Advice on Article 8 Taxonomy Regulation.
  7. See Regulation (EU) 2019/2089.
  8. See Directive 2014/95/EU.
  9. See the European Commission’s Proposal for a Corporate Sustainability Reporting Directive.
  10. See Regulation (EU) 2019/2088.
  11. The EBA report can be found here.

The EBA expects banks to expand their CSRBB framework

February 2022
4 min read

On Thursday 9 June, we hosted a roundtable in our head office in Utrecht titled ’Integrating ESG risks into a bank’s credit risk framework’. The roundtable was attended by credit and climate risk managers, as well as model validators, working for Dutch banks of different sizes. In this article we briefly describe Zanders’ view on this topic and share the key insights of the roundtable.


The current version of the IRRBB Guidelines, published in 2018, came into force on 30 June 2019. At that time, the IRRBB Guidelines were aligned with the Standards on interest rate risk in the banking book, published by the Basel Committee on Banking Supervision (in short, the BCBS Standards) in April 2016.

This new update is triggered by the revised Capital Requirements Regulation (CRR2) and Capital Requirements Directive (CRD5). Both documents were adopted by the Council of the EU and the European Parliament in 2019 as part of the Risk Reduction Measures package. The CRR2 and CRD5 included numerous mandates for the EBA to come up with new or adjusted technical standards and guidelines. These are now covered in three separate consultation papers

  1. The first consultation paper1 describes the update of the IRRBB Guidelines themselves.
  2. The second paper2 concerns the introduction of a standardized approach (SA) which should be applied when a competent authority deems a bank’s internal model for IRRBB management non-satisfactory. It also introduces a simplified SA for smaller and non-complex institutions.
  3. The third consultation paper3 offers updates to the supervisory outlier test (SOT) for the Economic Value of Equity (EVE) and the introduction of an SOT for Net Interest Income (NII). Read our analysis on this consultation paper here »

In this article we focus on the first consultation paper. The update of the IRRBB Guidelines can be split up in three topics and each will be discussed in further detail:

  • Additional criteria for the assessment and monitoring of the credit spread risk arising from non-trading book activities (CSRBB)
  • The criteria for non-satisfactory IRRBB internal systems
  • A general update of the existing IRBBB Guidelines

CSRBB

The 2018 IRRBB Guidelines introduced the obligation for banks to monitor CSRBB. However, the publication did not describe how to do this. In the updated consultation paper the EBA defines the measurement of CSRBB as a separate risk class in more detail. The general governance related aspects such as (management) responsibilities, IT systems and internal reporting framework are separately defined for CSRBB, but are similar to those for IRRBB.

Also similar to IRRBB is that banks must express their risk appetite for CSRBB both from an NII as well as an economic value perspective.

The EBA defines CSRBB as:

“The risk driven by changes of the market price for credit risk, for liquidity and for potentially other characteristics of credit-risky instruments, which is not captured by IRRBB or by expected credit/(jump-to-) default risk. CSRBB captures the risk of an instrument’s changing spread while assuming the same level of creditworthiness, i.e. how the credit spread is moving within a certain rating/PD range.”

EBA

quote

Compared to the previous definition, rating/PD migrations are explicitly excluded from CSRBB. Including idiosyncratic spreads could lead to double counting since these are generally covered by a credit risk framework. However, the guidelines give some flexibility to include idiosyncratic spreads, as long as the results are more conservative than when idiosyncratic spreads are excluded. This is because, based on the Quantitative Impact Study of December 2020 (QIS 2020), banks indicated to find it difficult to separate the idiosyncratic spreads from the credit spread.

Also, the scope of CSRBB has changed from the current IRRBB Guidelines. All assets, liabilities and off-balance-sheet items in the banking book that are sensitive to credit spread changes are within the scope of CSRBB whereas the 2018 IRRBB Guidelines focused only on the asset side. Based on the results of the QIS 2020, the EBA concluded that most of the exposures to CSRBB are debt securities which are accounted for at fair value (via Profit and Loss or Other Comprehensive Income). However, this does not rule out that other assets or liabilities could be exposed to CSRBB. It is stated that banks cannot ex-ante exclude positions from the scope of CSRBB. Any potential exclusion of instruments from the scope of CSRBB must be based on the absence of sensitivity to credit spread risk and appropriately documented. At a minimum, banks must include assets accounted at fair value in their scope.

We believe that the obligation to report CSRBB for all assets accounted for at fair value will be challenging for exposures that do not have quoted market prices. Without a deep liquid market, it will be difficult to establish the credits spread risk (even when idiosyncratic risk is included).

Another possible candidate to be included in the scope of CSRBB is the issued funding on the liability side of the banking book, especially in a NII context. When market spreads increase, this could become harmful when the wholesale funding needs to be rolled over against higher credit spread without being able to increase client interest on the asset side. Similar to IRRBB, the exposure to this risk depends on the repricing gap of the assets and liabilities. In this case, however, swaps cannot be used as hedge.

Other products such as consumer loans, mortgages, and consumer deposits, which are typically accounted for at amortized cost, are less likely to be included. This is also stated by the BCBS standards. The BCBS states that the margin (administrative rate) is under absolute control of the bank and hence not impacted by credit spreads. However, it is unclear whether this is sufficient to rule these products out of scope.

Non-satisfactory IRRBB internal systems

The EBA is mandated to specify the criteria for determining an IRRBB internal system as non-satisfactory. The EBA has identified specific items for this that should be considered. At a minimum, banks should have implemented their internal system in compliance with the IRRBB Guidelines, taking into account the principle of proportionality. More specifically:

  • Such a system must cover all material interest rate risk components (gap risk, basis risk, option risk).
  • The system should capture all material risks for significant assets, liabilities and off-balance sheet type instruments (e.g. non-maturing deposits, loans, and options).
  • All estimated parameters must be sufficiently back tested and reviewed, considering the nature, scale and complexity of the bank.
  • The internal system must comply with the model governance and the minimum required validation, review and control of IRRBB exposures as detailed by the IRRBB guidelines.
  • Competent authorities may require banks to use the standardized approach3 if the internal systems are deemed non-satisfactory.

General update of existing IRRBB Guidelines

Major parts of the guidelines for managing IRRBB have not changed. In the section on IRRBB stress testing, however, a new article (#103) for products with significant repricing restrictions (e.g. an explicit floor on non-maturing deposits – NMDs) is introduced. As part of their stress testing, banks should consider the impact when these products are replaced with contracts with similar characteristics, even under a run-off assumption. The exact intention of this article is unclear. For NII-purposes it is common practice to roll over products with similar characteristics (or use another balance sheet development assumption). Our interpretation of this article is that banks are expected to measure the risk of continued repricing restrictions in an economic value perspective when the maturity of those funding sources is smaller than the maturity of the asset portfolio. This may for example materialize when banks roll over NMDs that are subject to a legally imposed floor.

Another update is the restriction on the maximum weighted average repricing maturity of five years for NMDs. This cap was prescribed for the EVE SOT and is now included for the internal measurement of IRRBB. We believe that the impact of this will be limited since only a few banks will have separate NMD models for internal measurement and the SOT.

Finally, some minor additions have been included in the guidelines. For example, the guidelines emphasize multiple times that when diversification assumptions are used for the measurement of IRRBB, these must be appropriately stressed and validated.

Conclusion

It is expected that the final guidelines will not deviate significantly from the consultation paper. Banks can therefore start preparing for these new expectations. For the measurement of IRRBB, limited changes are introduced in the consultation. Although the exact intention of the EBA is unclear to us, it is interesting to notice that the updated IRRBB Guidelines include the expectation that banks pay special attention in their stress tests to products with significant repricing restrictions. Furthermore, banks must invest in their CSRBB measurement. For their entire banking book, banks need to assess whether market wide credit spread changes will have an impact on an NII and/or economic value perspective. The scope of CSRBB measurement may need to be extended to include the funding issued by the bank. And to conclude, the obligation to measure CSRBB for fair value assets that do not have quoted market prices will be a challenge for banks.

References
  1. CP Draft GL on IRRBB and CSRBB.pdf
  2. CP Draft RTS on SA.pdf
  3. CP Draft RTS on SOTs.pdf

The EBA faces banks with a new supervisory outlier test on net interest income

February 2022
4 min read

On Thursday 9 June, we hosted a roundtable in our head office in Utrecht titled ’Integrating ESG risks into a bank’s credit risk framework’. The roundtable was attended by credit and climate risk managers, as well as model validators, working for Dutch banks of different sizes. In this article we briefly describe Zanders’ view on this topic and share the key insights of the roundtable.


In this article, we focus on one of these consultation papers, which concerns updates to the supervisory outlier test (SOT) for the Economic Value of Equity (EVE) and the introduction of an SOT for Net Interest Income (NII).

The current version of the IRRBB Guidelines, published in 2018, came into force on 30 June 2019. At that time, the IRRBB Guidelines were aligned with the Standards on interest rate risk in the banking book, published by the Basel Committee on Banking Supervision (in short, the BCBS Standards) in April 2016.

The new updates are triggered by the revised Capital Requirements Regulation (CRR2) and Capital Requirements Directive (CRD5). Both documents were adopted by the Council of the EU and the European Parliament in 2019 as part of the Risk Reduction Measures package. The CRR2 and CRD5 included numerous mandates for the EBA to come up with new or adjusted technical standards and guidelines. These are now covered in three separate consultation papers:

  1. The first consultation paper1 describes an update of the IRRBB Guidelines themselves. The main changes are the specification of criteria to identify “non-satisfactory internal models for IRRBB management” and the specification of criteria to assess and monitor Credit Spread Risk in the Banking Book (CSRBB). Read our analysis on this consultation paper here »
  2. The second paper2 concerns the introduction of a standardized approach (SA) which should be used when a competent authority deems a bank’s internal model for IRRBB management non-satisfactory. It also introduces a Simplified SA for smaller and non-complex institutions.
  3. The third consultation paper3 offers updates to the supervisory outlier test (SOT) for the Economic Value of Equity (EVE) and the introduction of an SOT for Net Interest Income (NII).

Please note that we recently also published an article about the new disclosure requirements for IRRBB which is closely related to this topic.

Changes to the supervisory outlier test

Banks have been subject to an SOT already since the 2006 IRRBB Guidelines. The SOT is an important tool for supervisors to perform peer reviews and to compare IRRBB exposures between banks. The SOT measures how the EVE responds to an instantaneous parallel (up and down) yield curve shift of 200 basis points. Changes in EVE that exceed 20% of the institution’s own funds will trigger supervisory discussions and may lead to additional capital requirements.

Some changes to the SOT were included in the 2018 update of the IRRBB Guidelines. Next to further guidance on its calculation, the existing SOT was complemented with an additional SOT. The additional SOT was based on the same metric and guidelines, but the scenarios applied were the six standard interest rate scenarios introduced in the BCBS Standards. Also, a threshold of 15% compared to Tier 1 capital was applied. In the 2018 IRRBB Guidelines, the additional SOT was considered an ‘early warning signal’ only.

The new update of the IRRBB Guidelines includes two important SOT-related changes, which are incorporated through amendments to Article 98 (5) of the CRD: the replacement of the 20% SOT for EVE and the introduction of the SOT for NII. Both changes are discussed in more detail below.

Changes to the supervisory outlier test

Banks have been subject to an SOT already since the 2006 IRRBB Guidelines. The SOT is an important tool for supervisors to perform peer reviews and to compare IRRBB exposures between banks. The SOT measures how the EVE responds to an instantaneous parallel (up and down) yield curve shift of 200 basis points. Changes in EVE that exceed 20% of the institution’s own funds will trigger supervisory discussions and may lead to additional capital requirements.

Some changes to the SOT were included in the 2018 update of the IRRBB Guidelines. Next to further guidance on its calculation, the existing SOT was complemented with an additional SOT. The additional SOT was based on the same metric and guidelines, but the scenarios applied were the six standard interest rate scenarios introduced in the BCBS Standards. Also, a threshold of 15% compared to Tier 1 capital was applied. In the 2018 IRRBB Guidelines, the additional SOT was considered an ‘early warning signal’ only.

The new update of the IRRBB Guidelines includes two important SOT-related changes, which are incorporated through amendments to Article 98 (5) of the CRD: the replacement of the 20% SOT for EVE and the introduction of the SOT for NII. Both changes are discussed in more detail below.

Replacement of the 20% SOT for EVE

The first part of the amended Article 98 (5) concerns the replacement of the original 20% SOT by the 15% SOT. While many banks are probably already targeting levels below 15%, we expect that this change will limit the maneuvering capabilities of banks as they will likely choose to implement a management buffer. Note that not only the threshold is lower (15% instead of 20%), but also the denominator (Tier 1 capital instead of own funds). Furthermore, the worst outcome of all six supervisory scenarios should be used, as opposed to worst outcome of just the two parallel ones. Combined, this leads to a significant reduction in the EVE risk to which a bank may be exposed.

Some other noteworthy updates to the SOT for EVE that are not directly related to the CRD amendment are listed below:

  • The post-shock interest floor decreases from -100 to -150 basis points and it increases to 0% over a 50-year instead of a 20-year period.
  • In the calibration of the interest rate shocks for currencies for which the shocks have not been prescribed, the most recent 16-year period should be used (instead of the 2000-2015 period which is still underlying the shocks for the other currencies).
  • When aggregating the results over currencies, some additional offsetting (80% as opposed to 50%) is granted in case of Exchange Rate Mechanism (ERM) II currencies with a formally agreed fluctuation band narrower than the standard band of +/-15%. Currently, only positions in the Denmark Krona (DKK) qualify for this treatment.

Introduction of the SOT for NII

The second part of the amended Article 98 (5) concerns the introduction of an entirely new SOT. It is aimed at measuring the potential decline in NII for two standard interest rate shock scenarios. Compared to the SOT for EVE, the SOT for NII requires many more modeling assumptions, in particular to determine the expected balance sheet development. The consultation paper provides clarity on the approach the EBA wants to take but two decisions are explicitly consulted.

The SOT for NII compares the NII for a baseline scenario with the NII in a shocked scenario over a one-year horizon. The two shocks that need to be applied are the two instantaneous parallel shocks that are also used in the SOT for EVE. Furthermore, the same requirements that are specified for the SOT for EVE apply, for example the use of the floor and the aggregation approach. The two exceptions are the requirements to use a constant balance sheet assumption (as opposed to a run-off balance sheet) and to include commercial margins and other spread components in the calculations. The commercial margins of new instruments should equal the prevailing levels (as opposed to historical ones).

The two decisions for which the EBA is seeking input are:

The scope/definition of NII
In its narrowest definition, the SOT will focus on the difference between interest income and interest expenses. The EBA, however, also considers using a broader definition where the effect of market value changes of instruments accounted for at Fair Value (∆FV) is added, and possibly also interest rate sensitive fees and commissions.

The definition of the SOT’s threshold
Article 98 (5) requires the EBA to specify what is considered a ‘large decline’ in NII, in which case the competent authorities are entitled to exercise their supervisory powers. This first requires a metric. The EBA is consulting two:

  • The first metric is calculating the change in NII (the difference between the shocked and baseline NII) relative to the Tier 1 Capital:
  • The second metric is calculating the change in NII relative to the baseline scenario, after correcting for administrative expenses that can be allocated to NII:
  • where α is the historical share of NII relative to the operating income as reported based on FINREP input.

The pros of the narrow definition of NII are improved comparability and ease of computation, where the main pro of the broader definition is that it achieves a more comprehensive picture, which is also more in line with the EBA IRRBB Guidelines. With respect to the metrics, the first (capital-based) metric is the simplest and it is comparable to the approach taken for the SOT for EVE. The second metric is close to a P&L-based metric and the EBA argues that its main advantage is that “it takes into account both the business model and cost structure of a bank in the assessment of the continuity of the business operations”. It does involve, however, the application of some assumptions on determining the α parameter.

Thresholds for the four possible combinations

For each of the four possible combinations (definition of NII and specification of the metric), the EBA has determined, using data from the December 2020 Quantitative Impact Study (QIS), what the corresponding thresholds should be. Their starting point has been to make the SOT for NII as stringent as the SOT for EVE. Effectively, they reverse engineered the threshold to achieve a similar number of outliers under both measures. We expect that the proposed threshold for any of the four possible combinations will not be constraining for the majority of banks. The resulting proposed thresholds are included in the table below:

Table 1 – Comparison of the proposed thresholds for each combination of metric and scope

The impact of including Fair Value changes seems arbitrary as it increases the threshold for the capital-based metric and decreases the threshold for the P&L-based metric. Also, from a comparability and computational perspective, the narrow definition of NII may be preferred. Furthermore, the capital-based metric is less intuitive for NII than it is for EVE, and consequently, the P&L-based one may be preferred. It is also noted in the consultation paper that if the shocked NII after the correction for administrative expenses (the numerator) is negative, it will also be considered an outlier.

Conclusion

In the past years, many banks have invested heavily in their IRRBB framework following the 2018 update of the IRRBB Guidelines. Once again, an investment is required. Even though there are not many surprises in the proposed updates related to the SOTs, small and large banks alike will need to carefully assess how the changes to the existing SOT and the introduction of the new SOT will impact their interest rate risk management. Banks still have the opportunity to respond to all three consultation papers until 4 April 2022.

References
  1. CP Draft GL on IRRBB and CSRBB.pdf
  2. CP Draft RTS on SA.pdf
  3. CP Draft RTS on SOTs.pdf

How can banks manage climate-related and environmental financial risks

July 2021
7 min read

On Friday 8 July, the European Central Bank (ECB) published the results of the climate risk stress test that was performed in the first half of this year.


As the financial sector is key for the transition towards a low-carbon and more circular economy, financial institutions have to deal with climate-related and environmental financial risks (C&E risks). At the same time, the increased importance of these C&E risks also presents new business opportunities for the financial sector. Therefore, to support banks in their self-assessment and action plans, Zanders developed a Scan & Plan Solution on C&E risks.

According to the 2021 World Economic Forum Global Risk Report1, extreme weather, climate action failure, human environmental damage and biodiversity loss are ranked as four of the top five global risks by highest likelihood and four of the six global risks with the largest impact. It is not surprising that over the past years, numerous articles on C&E risks have been published and many initiatives have been taken to identify, measure and manage these risks. The Paris Agreement2, the United Nations 2030 Agenda for Sustainable Development3 and more recently the European Green Deal4 are the main examples of international governmental responses to address C&E risks.

The financial sector is considered key for the transition towards a low-carbon and more circular economy. This is illustrated by the fact that the European Central Bank (ECB) has identified climate-related risks as a key risk driver for the euro area banking system in their Single Supervisory Mechanism Risk Map5. At the same time, the increased importance of C&E risks also presents new business opportunities for the financial sector, such as providing sustainable financing solutions and offering new financial instruments that facilitate C&E risk management. To illustrate, the UN’s Intergovernmental Panel on Climate Change6 (IPCC) estimates that the required investment for alignment with the Paris Agreement would be at least $3.5tn per year until 2050 for the energy sector alone.

To address C&E risks, the ECB published a Guide on climate-related and environmental risks7 for banks that describes the ECB’s supervisory expectations related to risk management and disclosure. Banks are required by the ECB to perform a self-assessment with respect to the supervisory expectations and to draft an action plan in 2021. The self-assessments and plans will subsequently be reviewed and challenged by the ECB as part of the supervisory dialogue. In 2022, the ECB will conduct a full supervisory review of bank’s practices related to C&E risks.

The Zanders Scan & Plan Solution provides clear insights into gaps with the ECB expectations and proposes practical actions that are tailored to a bank’s nature, scale and complexity. This article provides a brief explanation of C&E risks, outlines a few specific ECB supervisory expectations and elaborates on the Solution.

Definition of C&E risk

Financial risks from climate and environmental change arise from two primary risk categories:

  • Physical risks. The first risk category concerns physical risks caused by acute (direct events) or chronic (longer-term events which cause gradual deterioration) C&E events. Examples of acute events are floods, wildfires and heatwaves, whereas chronic events relate to the likes of rising sea levels, acidity and biodiversity losses, for example.
  • Transition risks. The second risk category comprises transition risks resulting from the process of moving towards a low-carbon economy. Changes in policy, regulation, technology, market sentiment and consumer sentiment could destabilize markets, tighten financial conditions and lead to procyclicality of losses.

These two risk categories are distinct from other risk categories in the following aspects: 1) they have a correlated and non-linear impact on all business lines, sectors and geographies, 2) they have a long-term nature and 3) the future impact is largely dependent on short-term actions. The risk categories are, among others, drivers of credit, market and operational risk, as shown in Figure 1.

Figure 1: Examples of the impact of physical risks and transition risks on traditional risk types from the ECB Guide on climate-related and environmental risks.

ECB Supervisory Expectations

In the Guide on climate-related and environmental risks7, published in November 2020, the ECB explains that banks are expected to reflect C&E risks as drivers of existing risk categories (e.g., credit risk, market risk, operational risk) rather than as a separate risk type. This indicates that not only efforts should be made by banks to develop risk management practices related to C&E risks but that these practices should also be integrated in existing risk management frameworks, models and policies.

The ECB Guide covers four main areas. First, in relation to ‘business models and strategy’, the ECB expects banks to understand the impact of C&E risks on their business environment and integrate C&E risks in their business strategy. Secondly, the guide addresses ‘governance and risk appetite’. The ECB expects a bank’s management to include C&E risks in the risk appetite framework, assign responsibilities related to C&E risks to the three lines of defense and establish internal reporting. Thirdly, in relation to ‘risk management’, the ECB expects that banks integrate C&E risks into their existing risk management framework. Finally, regarding ‘disclosures’, the ECB expects banks to disclose meaningful information and metrics on C&E risks. The four above-mentioned areas are covered in 13 expectations, which are in turn further divided in 46 sub-expectations. Banks are required by the ECB to perform a self-assessment with respect to the supervisory expectations and to draft an action plan in 2021. The self-assessments and plans will subsequently be reviewed and challenged by the ECB as part of the supervisory dialogue. The ECB recommends National Competent Authorities, in their supervision of less significant institutions, to apply these expectations proportionate to the nature, scale and complexity of the bank’s activities.

Zanders has thoroughly analyzed all (sub)-expectations and has highlighted a few specific expectations including possible actions below.

Expectation 4.2: Institutions are expected to develop appropriate key risk indicators (KRI) and set appropriate limits for effectively managing climate-related and environmental risks in line with their regular monitoring and escalation arrangements.

Here, the ECB expects institutions to monitor and report their exposures to C&E risks based on current data and forward-looking estimations. In addition, institutions are expected to assign quantitative metrics to C&E risks. It is however acknowledged that, since definitions, taxonomies and methodologies are still under development, qualitative metric or proxy data can be used as long as specific quantitative metrics are not yet available. The ECB deems that the metrics should reflect the long-term nature of climate change, and explicitly consider different paths for temperature and greenhouse gas emissions. Finally, the ECB expects institutions to define limits on the metrics based on the risk appetite regarding C&E risks.

This expectation has various elements to it, and Zanders has defined as the most important and first step to identify the internal and external data availability related to C&E risks. If the data availability allows it, an institution should set up quantitative metrics and limits on these metrics that reflect C&E risks. A first step to achieve this would be to define several firmwide KRIs, for example by limiting the carbon emission of the whole portfolio in line with the Paris Agreement2 or other (inter)national climate agreements. These KRIs can then be cascaded down to business lines and/or portfolios, such as limiting the exposure to specific sectors with large carbon footprints or substantial use of water. If the current data availability does not allow for quantitative metrics, the bank should identify which data is still lacking and assess ways to collect this data from internal data sources or external data providers in the course of time. In the meantime, qualitative metrics or proxies could be developed such as a qualitative score for the sophistication of the climate strategy of large corporates in the portfolio.

Should a bank already have some metrics in place, Zanders advises to evaluate if these metrics sufficiently cover all material physical and transition C&E risks that the bank is expected to face and if there are ways to increase the sophistication of these metrics, for example by including forward-looking estimations. Finally, Zanders advises banks to create a monitoring procedure to ensure that these metrics and their limits are evaluated regularly.

Expectation 8.1: Climate-related and environmental risks are expected to be included in all relevant stages of the credit-granting process and credit processing.

In this expectation, the ECB underlines that banks should identify and assess material factors that affect the default risk of loan exposures. The quality of the client’s own management of C&E risks may be taken into account in this case. In addition, banks are expected to consider changes in the credit risk profiles based on sectors and geographical areas.

The way to address this expectation is highly dependent on the nature of the bank, on the existing credit granting process and on the availability of data. Some examples of including C&E risk in the credit granting process in a qualitative way, which Zanders has observed in the market, are the development of shadow PD models that trigger mitigating actions in case of large discrepancies with the regular PDs, or the introduction of a scorecard based on quantitative as well as qualitative aspects.

It should be noted that these and other solutions are not mutually exclusive and that multiple approaches can be adopted for different parts of the portfolio. Also, the efforts for several individual expectations can be combined. For example, taking C&E risks into account in the credit granting process could also be linked to the qualitative and quantitative metrics set as part of expectation 4.2 (see above). The quality of the client’s own management of C&E risks can for instance be measured based on the qualitative score for the climate strategy sophistication of large corporates mentioned above.

Expectation 11: Institutions with material climate-related and environmental risks are expected to evaluate the appropriateness of their stress testing, with a view to incorporating them into their baseline and adverse scenarios.

This expectation indicates that banks should evaluate the appropriateness of their stress testing in relation to C&E risks. In this evaluation, the bank should take into account the following considerations: i) how will the bank be affected by physical and transition risks, ii) how will C&E risks evolve under various scenarios (thereby taking into account that these risks may not fully be reflected in historical data), and iii) how may C&E risks materialize in the short-, medium- and long-term. Based on these considerations and on scientific climate change pathways as well as on assumptions that fit with their risk profile and individual specification, banks should incorporate C&E risks in their baseline and adverse scenarios.

To address this recommendation, Zanders advises banks to first identify C&E risks scenarios with different severities based on the materiality of C&E risks for the bank. Examples of this include early or late transition to a low-carbon economy (transition risk), introduction of a carbon tax (transition risk) or staying below or above a 2°C temperature increase (physical risk). These scenarios could be based on scientific scenarios from the IPCC8 or the International Energy Agency9. The next step is to translate these scenarios into macro-economic variables, such as GDP, inflation and loan valuations, over a range of relevant sectors and geographies. Since there is not yet a single methodology to do this, banks need to be creative and combine new qualitative and quantitative approaches with existing modeling methodologies.

Zanders Scan & Plan Solution

To manage C&E risks, to seize new business opportunities and to meet the regulatory expectations related to C&E risks, it is crucial for banks to have transparency about their exposure to these risks. To support banks with this, the Zanders Scan & Plan Solution is available. The Scan assesses the gaps between the bank’s current practices and each of the expectations in the ECB guide. These gaps are scored based on a pre-defined scoring system and are shown in a heatmap that is easy to interpret. Subsequently, the Plan proposes possible actions to close the identified gaps. These possible actions will be tailored to the nature, scale, and complexity of the bank and to the level of sophistication of the bank in the field of C&E risks. The Scan & Plan will be provided in the form of a detailed report.

Zanders has previously supported clients on topics related to climate change, published market insights and supported research. In addition, Zanders has broad experience in supporting clients in each of the areas of the ECB guide: business models and strategy, governance and risk appetite, risk management and disclosure. Hence, Zanders is in an excellent position to also support banks with the implementation of the proposed actions from the Scan & Plan or with shaping new business activities related to C&E risks.

To learn more about the Zanders Scan & Plan solution and how Zanders can support your institution with managing C&E risks, please contact Petra van MeelMarije Wiersma or Pieter Klaassen.

References
1) http://www3.weforum.org/docs/WEF_The_Global_Risks_Report_2021.pdf
2) https://unfccc.int/sites/default/files/english_paris_agreement.pdf
3) https://sdgs.un.org/2030agenda
4) https://ec.europa.eu/info/strategy/priorities-2019-2024/european-green-deal_en
5) https://www.bankingsupervision.europa.eu/ecb/pub/ra/html/ssm.ra2021~edbbea1f8f.en.html#toc1
6) https://www.ipcc.ch/site/assets/uploads/sites/2/2019/02/SR15_Chapter4_Low_Res.pdf
7) https://www.bankingsupervision.europa.eu/ecb/pub/pdf/ssm.202011finalguideonclimate-relatedandenvironmentalrisks~58213f6564.en.pdf
8) https://www.ipcc.ch/report/emissions-scenarios/
9) https://www.iea.org/reports/world-energy-model/sustainable-development-scenario

How can treasury become a sustainable function?

July 2021
7 min read

On Friday 8 July, the European Central Bank (ECB) published the results of the climate risk stress test that was performed in the first half of this year.


One of the key subjects in this re-assessment is the implementation of tangible and transparent Environment, Social, and Governance (ESG) factors into the business.

Treasury can drive sustainability throughout the company from two perspectives, namely through initiatives within the Treasury function and initiatives promoted by external stakeholders, such as banks, investors, or its clients. When considering sustainability, many treasurers first port of call is to investigate realizing sustainable financing framework. This is driven by the high supply of money earmarked for sustainable goals. However, besides this external focus, Treasury can strive to make its own operations more sustainable and, as a result, actively contribute to company-wide ESG objectives.

Figure 1: ESG initiatives in scope for Treasury

Treasury holds a unique position within the company because of the cooperation it has with business areas and the interaction with external stakeholders. Treasury can leverage this position to drive ESG developments throughout the company, stay informed of latest updates and adhere to regulatory standards. This article shows how Treasury can become a sustainable support function in its own right, highlights various initiatives within and outside the Treasury department and marks the benefits for Treasury – on top of realising ESG targets.

Internal initiatives

Automation and digitalization drive certain environmental initiatives within the Treasury department. Full digitalized records and bank statement management and the digitalization of form processes reduce the adverse environmental impact of the Treasury department. Besides reducing Treasury’s environmental footprint, digitalization improves efficiency of the Treasury team. By reducing the number of manual, cumbersome operational activities, time can be spent on value-adding activities rather than operational tasks.

Another great example of how Treasury can contribute to the ESG goals of the company, is to incorporate ESG elements in the capital allocation process. This can be done by adding ESG related risk factors to the weighted average cost of capital (WACC) or hurdle investment rates. By having an ESG linked WACC, one can evaluate projects by measuring the real impact of ESG on the required return on equity (ROE). By adjusting the WACC to, for example, the level of CO2 that is emitted by a project, the capital allocation process favours projects with low CO2 emissions.

An additional internal initiative is the design of a mobility policy with the objective to lower CO2 emissions. On one hand, this relates to decreasing the amount of business trips made by the Treasury department itself. On the other hand, it relates to the reduction of business travel by stakeholders of treasury such as bankers, advisors and system vendors. A framework that offsets the added value of a real-life meeting against the CO2 emission is an example of a measure that supports CO2 reduction on both sides. Such a framework supports determination whether the meeting takes place online or in person.

Furthermore, embedding ESG requirements into bank selection, system selection and maintenance processes is a valuable way of encouraging new and existing partners to undertake ESG related measures.

When it comes to social contributions, the focus could be on the diversity and inclusion of the Treasury department, which includes well-being, gender equality and inclusivity of the employees. Pursuing these policies can increase the attractiveness of the organization when hiring talent and make it easier to retain talent within the company, which is also beneficial to the Treasury function.

The development of a structured model that defines the building blocks for Treasury to support the achievement of companywide ESG objectives is a governance initiative that Treasury could undertake. An example of such a model is the Zanders Treasury and Risk Maturity Model, which can be integrated in any organization. This framework supports Treasury in keeping track of its ESG footprint and its contribution to company-wide sustainable objectives. In addition, the Zanders sustainability dashboard provides information on metrics and benchmarks that can be applied to track the progress of several ESG related goals for Treasury. Some examples of these are provided in our ‘Integration of ESG in treasury’ article.

External initiatives

Besides actions taken within the Treasury department, Treasury can boost company-wide ESG performance by leveraging their collaboration with external stakeholders. One of these external initiatives is sustainability linked financing, which is a great tool to encourage the setting of ambitious, company-wide ESG targets and link these to financing arrangements. Examples of sustainability linked financing products include green loans and bonds, sustainability-linked loans, and social bonds. To structure sustainability-linked financing products, corporates often benefit from the guidance of external parties when setting KPIs and ambitious targets and linking these to the existing sustainability strategy. Besides Treasury’s strong relationships with banks, retaining good relationships with (ESG) rating agencies and financial institutions is critical to stay abreast of the latest updates and adhere to regulatory standards. Additionally, investing excess cash in a sustainable manner, using green money market funds or assessing the ESG rating of counterparties, is an effective way of supporting sustainability.

Apart from financing instruments, Treasury can drive the ESG strategy throughout the organization in other ways. Treasury can seek collaborations with business partners to comply with ESG targets, which is another effective manner to achieve ESG related goals throughout the supply chain. An increasing number of corporates is looking to reduce the carbon footprint of their supply chain, for which collaboration is essential. Treasury can support this initiative by linking supplier onboarding on its supply chain finance program to the sustainability performance of suppliers.

To conclude

As developments in ESG are rapidly unfold9ing, Zanders has started an initiative to continuously update our clients to stay ahead of the latest trends. Through the knowledge and network that we have built over the years, we will regularly inform our clients on ESG trends via articles on the news page on our website. The first article will be devoted to the revision of the Sustainability Linked Loan Principles (SLLP) by the Loan Market Association (LMA) and its American and Asian equivalents.

We are keen to hear which topics you would like to see covered. Feel free to reach out to Joris van den Beld or Sander van Tol if you have any questions or want to address ESG topics that are on your agenda.

Impact of climate change on financial institutions

July 2021
7 min read

On Friday 8 July, the European Central Bank (ECB) published the results of the climate risk stress test that was performed in the first half of this year.


The Bank of England is even of the opinion that climate change represents the tragedy of the horizon: “by the time it is clear that climate change is creating risks that we want to reduce, it may already be too late to act” [1]. This article provides a summary of the type of financial risks resulting from climate change, various initiatives within the financial industry relating to the shift towards a low-carbon economy, and an outlook for the assessment of climate change risks in the future.

At the December 2015 Paris Agreement conference, strict measures to limit the rise in global temperatures were agreed upon. By signing the Paris Agreement, governments from all over the world committed themselves to paving a more sustainable path for the planet and the economy. If no action is taken and the emission of greenhouse gasses is not reduced, research finds that per 2100, the temperature will have increased by 3°C to 5°C2.. Climate change affects the availability of resources, the supply and demand for products and services and the performance of physical assets. Worldwide economic costs from natural disasters already exceeded the 30-year average of USD 140 billion per annum in seven out of the last ten years. Extreme weather circumstances influence health and damage infrastructure and private properties, thereby reducing wealth and limiting productivity. According to Frank Elderson, Executive Director at the DNB, this can disrupt economic activity and trade, lead to resource shortages and shift capital from more productive uses to reconstruction and replacement3.

According to the Bank of England, financial risks from climate change come down to two primary risk factors4:

Increasing concerns about climate change has led to a shift in the perception of climate risk among companies and investors. Where in the past analysis of climate-related issues was limited to sectors directly linked to fossil fuels and carbon emissions, it is currently being recognized that climate-related risk exposures concern all sectors, including financials. Banks are particularly vulnerable to climate-related risks as they are tied to every market sector through their lending practices.

Financial risks

  • Physical risks. The first risk factor concerns physical risks caused by climate and weather-related events such as droughts and a sea level rise. Potential consequences are large financial losses due to damage to property, land and infrastructure. This could lead to impairment of asset values and borrowers’ creditworthiness. For example, as of January 2019, Dutch financial institutions have EUR 97 billion invested in companies active in areas with water scarcity5. These institutions can face distress if the water scarcity turns into water shortages. Another consequence of extreme climate and weather-related events is the increase in insurance claims: in the US alone, the insurance industry paid out USD 135 billion from natural catastrophes in 2017, almost three times higher than the annual average of USD 49 billion.
  • Transition risks. The second risk factor comprises transition risks resulting from the process of moving towards a low-carbon economy. Revaluation of assets because of changes in policy, technology and sentiment could destabilize markets, tighten financial conditions and lead to procyclicality of losses. The impact of the transition is not limited to energy companies: transportation, agriculture, real estate and infrastructure companies are also affected. An example of transition risk is a decrease in financial return from stocks of energy companies if the energy transition undermines the value of oil stocks. Another example is a decrease in the value of real estate due to higher sustainability requirements.

These two climate-related risk factors increase credit risk, market risk and operational risk and have distinctive elements from other risk factors that lead to a number of unique challenges. Firstly, financial risks from physical and transition risk factors may be more far-reaching in breadth and magnitude than other types of risks as they are relevant to virtually all business lines, sectors and geographies, and little diversification is present. Secondly, there is uncertainty in timing of when financial risks may be realized. The possibility exists that the risk impact falls outside of current business planning horizons. Thirdly, despite the uncertainty surrounding the exact impact of climate change risks, combinations of physical and transition risk factors do lead to financial risk. Finally, the magnitude of the future impact is largely dependent on short-term actions.

Initiatives

Many parties in the financial sector acknowledge that although the main responsibility for ensuring the success of the Paris Agreement and limiting climate change lies with governments, central banks and supervisors also have responsibilities. Consequently, climate change and the inherent financial risks are increasingly receiving attention, which is evidenced by the various recent initiatives related to this topic.

Banks and regulators

The Network of Central Banks and Supervisors for Greening the Financial System (NGFS) is an international cooperation between central banks and regulators6. NGFS aims to increase the financial sector’s efforts to achieve the Paris climate goals, for example by raising capital for green and low-carbon investments. NGFS additionally maps out what is needed for climate risk management. DNB and central banks and regulators of China, Germany, France, Mexico, Singapore, UK and Sweden were involved from the start of NGFS in 2017. The ECB, EBA, EIB and EIOPA are currently also part of the network. In the first progress report of October 2018, NGFS acknowledged that regulators and central banks increased their efforts to understand and estimate the extent of climate and environmental risks. They also noted, however, that there is still a long way to go.

In their first comprehensive report of April 2019, NGFS drafted the following six recommendations for central banks, supervisors, policymakers and financial institutions, which reflect best practices to support the Paris Agreement7:

  1. Integrating climate-related risks into financial stability monitoring and micro-supervision;
  2. Integrating sustainability factors into own-portfolio management;
  3. Bridging the data gaps by public authorities by making relevant data to Climate Risk Assessment (CRA) publicly available in a data repository;
  4. Building awareness and intellectual capacity and encouraging technical assistance and knowledge sharing;
  5. Achieving robust and internationally consistent climate and environment-related disclosure;
  6. Supporting the development of a taxonomy of economic activities.

All these recommendations require the joint action of central banks and supervisors. They aim to integrate and implement earlier identified needs and best practices to ensure a smooth transition towards a greener financial system and a low-carbon economy. Recommendations 1 and 5, which are two of the main recommendations, require further substantiation.

  • The first recommendation consists of two parts. Firstly, it entails investigating climate-related financial risks in the financial system. This can be achieved by (i) mapping physical and transition risk channels to key risk indicators, (ii) performing scenario analysis of multiple plausible future scenarios to quantify the risks across the financial system and provide insight in the extent of disruption to current business models in multiple sectors and (iii) assessing how to include the consequences of climate change in macroeconomic forecasting and stability monitoring. Secondly, it underlines the need to integrate climate-related risks into prudential supervision, including engaging with financial firms and setting supervisory expectations to guide financial firms.
  • The fifth recommendation stresses the importance of a robust and internationally consistent climate and environmental disclosure framework. NGFS supports the recommendations of the Task Force on Climate-related Financial Disclosures (TCFD8) and urges financial institutions and companies that issue public debt or equity to align their disclosures with these recommendations. To encourage this, NGFS emphasizes the need for policymakers and supervisors to take actions in order to achieve a broader application of the TCFD recommendations and the growth of an internationally consistent environmental disclosure framework.

Future deliverables of NGFS consist of drafting a handbook on climate and environmental risk management, voluntary guidelines on scenario-based climate change risk analysis and best practices for including sustainability criteria into central banks’ portfolio management.

Asset managers

To achieve the climate goals of the Paris Agreement, €180 billion is required on an annual basis5. It is not possible to acquire such a large amount from the public sector alone and currently only a fraction of investor capital is being invested sustainably. Research from Morningstar shows that 11.6% of investor capital in the stock market and 5.6% in the bond market is invested sustainably9. Figure 1 shows that even though the percentage of capital invested in sustainable investment funds (stocks and bonds) is growing in recent years, it is still worryingly low.

Figure 1: Percentage of invested capital in Europe in traditional and sustainable investment funds (shares and bonds). Source: Morningstar [9].

The current levels of investment are not enough to support an environmentally and socially sustainable economic system. As a result, the European Commission (EC) has raised four initiatives through the Technical Expert Group on sustainable finance (TEG) that are designed to increase sustainable financing10. The first initiative is the issuance of two kinds of green (low-carbon) benchmarks. Offering funds or trackers on these indices would lead to an increase in cash flows towards sustainable companies. Secondly, an EU taxonomy for climate change mitigation and climate change adaptation has been developed. Thirdly, to enable investors to determine to what extent each investment is aligned with the climate goals, a list of economic activities that contribute to the execution of the Paris Agreement has been drafted. Finally, new disclosure requirements should enhance visibility of how investment firms integrated sustainability into their investment policy and create awareness of the climate risks the investors are exposed to.

Insurance firms

Within the insurance sector, the Prudential Regulation Authority (PRA) requires insurers to follow a strategic approach to manage the financial risks from climate change. To support this, in July 2018, the Bank of England (BoE) formed a joint working group focusing on providing practical assistance on the assessment of financial risks resulting from climate changes. In May 2019, the working group issued a six-stage framework that helps insurers in assessing, managing and reporting physical climate risk exposure due to extreme weather events11. Practical guidance is provided in the form of several case studies, illustrating how considering the financial impacts can better inform risk management decisions.

Authorities

Another initiative is the Climate Financial Risk Forum (CFRF), a joint initiative of the PRA and the Financial Conduct Authority (FCA)12. The forum consists of senior representatives of the UK financial sector from banks, insurers and asset managers. CFRF aims to build capacity and share best practices across financial regulators and the industry to enhance responses to the financial climate change risks. The forum set up four working groups focusing on risk management, scenario analysis, disclosure and innovation. The purpose of these working groups, which consist of CFRF members as well as other experts such as academia, is to provide practical guidance on each of the four focus areas.

Current status and outlook

On 5 June 2019, the TCFD published a Status Report assessing a disclosure review on the extent to which 1,100 companies included information aligned with these TCFD recommendations in their 2018 reports. The report also assessed a survey on companies’ efforts to live up to TCFD recommendations and users’ opinion on the usefulness of climate-related disclosures for decision-making13. Based on the disclosure review and the survey, TCFD concluded that, while some of the results were encouraging, not enough companies are disclosing climate change-linked financial information that is useful for decision-making. More specifically, it was found that:

  • “Disclosure of climate-related financial information has increased, but is still insufficient for investors;
  • More clarity is needed on the potential financial impact of climate-related issues on companies;
  • Of companies using scenarios, the majority do not disclose information on the resilience of their strategies;
  • Mainstreaming climate-related issues requires the involvement of multiple functions.”

Further, the BoE finds that despite the progress, there is still a long way to go: while many banks are incorporating the most immediate physical risks to their business models and assess exposures to transition risks, many of them are not there yet in their identification and measurement of the financial risks. They stress that governments, financial firms, central banks and supervisors should work together internationally and domestically, private sector and public sector, to achieve a smooth transition to a low-carbon economy. Mark Carney, Governor of the BoE, is optimistic and argues that, conditional on the amount of effort, it should possible to manage the financial climate risks in an orderly, effective and productive manner4.

With respect to the future, Frank Elderson made the following claim: “Now that European banking supervision has entered a more mature phase, we need to retain a forward-looking strategy and develop a long-term vision. Focusing on greening the financial system must be a part of this.”3.

References

https://www.bankofengland.co.uk/-/media/boe/files/speech/2019/avoiding-the-storm-climate-change-and-the-financial-system-speech-by-sarah-breeden.pdf
https://public.wmo.int/en/media/press-release/wmo-climate-statement-past-4-years-warmest-record
https://www.bankingsupervision.europa.eu/press/interviews/date/2019/html/ssm.in190515~d1ab906d59.en.html
https://www.bankofengland.co.uk/-/media/boe/files/prudential-regulation/report/transition-in-thinking-the-impact-of-climate-change-on-the-uk-banking-sector.pdf
https://fd.nl/achtergrond/1294617/beleggers-moeten-met-de-billen-bloot-over-klimaatrisico-s
https://www.dnb.nl/over-dnb/samenwerking/network-greening-financial-system/index.jsp
https://www.banque-france.fr/sites/default/files/media/2019/04/17/ngfs_first_comprehensive_report_-_17042019_0.pdf
https://www.fsb-tcfd.org/publications/final-recommendations-report/
http://www.morningstar.nl/nl/
10 https://ec.europa.eu/info/publications/sustainable-finance-technical-expert-group_en
11 https://www.bankofengland.co.uk/-/media/boe/files/prudential-regulation/publication/2019/a-framework-for-assessing-financial-impacts-of-physical-climate-change.pdf
12 https://www.bankofengland.co.uk/news/2019/march/first-meeting-of-the-pra-and-fca-joint-climate-financial-risk-forum
13 https://www.fsb-tcfd.org/wp-content/uploads/2017/06/FINAL-2017-TCFD-Report-11052018.pdf

Machine learning in risk management

February 2021
4 min read

On Thursday 9 June, we hosted a roundtable in our head office in Utrecht titled ’Integrating ESG risks into a bank’s credit risk framework’. The roundtable was attended by credit and climate risk managers, as well as model validators, working for Dutch banks of different sizes. In this article we briefly describe Zanders’ view on this topic and share the key insights of the roundtable.


The current trend to operate a ‘data-driven business’ and the fact that regulators are increasingly focused on data quality and data availability, could give an extra impulse to the use of ML models.

ML models

ML models study a dataset and use the knowledge gained to make predictions for other datapoints. An ML model consists of an ML algorithm and one or more hyperparameters. ML algorithms study a dataset to make predictions, where hyperparameters determine the settings of the ML algorithm. The studying of a dataset is known as the training of the ML algorithm. Most ML algorithms have hyperparameters that need to be set by the user prior to the training. The trained algorithm, together with the calibrated set of hyperparameters, form the ML model.

ML models have different forms and shapes, and even more purposes. For selecting an appropriate ML model, a deeper understanding of the various types of ML that are available and how they work is required. Three types of ML can be distinguished:

  • Supervised learning.
  • Unsupervised learning.
  • Semi-supervised learning.

The main difference between these types is the data that is required and the purpose of the model. The data that is fed into an ML model is split into two categories: the features (independent variables) and the labels/targets (dependent variables, for example, to predict a person’s height – label/target – it could be useful to look at the features: age, sex, and weight). Some types of machine learning models need both as an input, while others only require features. Each of the three types of machine learning is shortly introduced below.

Supervised learning

Supervised learning is the training of an ML algorithm on a dataset where both the features and the labels are available. The ML algorithm uses the features and the labels as an input to map the connection between features and labels. When the model is trained, labels can be generated by the model by only providing the features. A mapping function is used to provide the label belonging to the features. The performance of the model is assessed by comparing the label that the model provides with the actual label.

Unsupervised learning

In unsupervised learning there is no dependent variable (or label) in the dataset. Unsupervised ML algorithms search for patterns within a dataset. The algorithm links certain observations to others by looking at similar features. This makes an unsupervised learning algorithm suitable for, among other tasks, clustering (i.e. the task of dividing a dataset into subsets). This is done in such a manner that an observation within a group is more like other observations within the subset than an observation that is not in the same group. A disadvantage of unsupervised learning is that the model is (often) a black box.

Semi-supervised learning

Semi-supervised learning uses a combination of labeled and unlabeled data. It is common that the dataset used for semi-supervised learning consist of mostly unlabeled data. Manually labeling all the data within a dataset can be very time consuming and semi-supervised learning offers a solution for this problem. With semi-supervised learning a small, labeled subset is used to make a better prediction for the complete data set.

The training of a semi-supervised learning algorithm consists of two steps. To label the unlabeled observations from the original dataset, the complete set is first clustered using unsupervised learning. The clusters that are formed are then labeled by the algorithm, based on their originally labeled parts. The resulting fully labeled data set is used to train a supervised ML algorithm. The downside of semi-supervised learning is that it is not certain the labels are 100% correct.

Setting up the model

In most ML implementations, the data gathering, integration and pre-processing usually takes more time than the actual training of the algorithm. It is an iterative process of training a model, evaluating the results, modifying hyperparameters and repeating, rather than just a single process of data preparation and training. After the training is performed and the hyperparameters have been calibrated, the ML model is ready to make predictions.

Machine learning in financial risk management

ML can add value to financial risk management applications, but the type of model should suit the problem and the available data. For some applications, like challenger models, it is not required to completely explain the model you are using. This makes, for example, an unsupervised black box model suitable as a challenger model. In other cases, explainability of model results is a critical condition while choosing an ML model. Here, it might not be suitable to use a black box model.

In the next section we present some examples where ML models can be of added value in financial risk management.

Data quality analysis

All modeling challenges start with data. In line with the ‘garbage in, garbage out’ maxim, if the quality of a dataset is insufficient then an ML model will also not perform well. It is quite common that during the development of an ML model, a lot of time is spent on improving the data quality. As ML algorithms learn directly from the data, the performance of the resulting model will increase if the data quality increases. ML can be used to improve data quality before this data is used for modeling. For example, the data quality can be improved by removing/replacing outliers and replacing missing values with likely alternatives.

An example of insufficient data quality is the presence of large or numerous outliers. An outlier is an observation that significantly deviates from the other observations in the data, which might indicate it is incorrect. Outlier detection can easily be performed by a data scientist for univariate outliers, but multivariate outliers are a lot harder to identify. When outliers have been detected, or if there are missing values in a dataset, it might be useful to substitute some of these outliers or impute for missing values. Popular imputation methods are the mean, median or most frequent methods. Another option is to look for more suitable values; and ML techniques could help to improve the data quality here.

Multiple ML models can be combined to improve data quality. First, an ML model can be used to detect outliers, then another model can be used to impute missing data or substitute outliers by a more likely value. The outlier detection can either be done using clustering algorithms or by specialized outlier detection techniques.

Loan approval

A bank’s core business is lending money to consumers and companies. The biggest risk for a bank is the credit risk that a borrower will not be able to fully repay the borrowed amount. Adequate loan approval can minimize this credit risk. To determine whether a bank should provide a loan, it is important to estimate the probability of default for that new loan application.

Established banks already have an extensive record of loans and defaults at their disposal. Together with contract details, this can form a valuable basis for an ML-based loan approval model. Here, the contract characteristics are the features, and the label is the variable indicating if the consumer/company defaulted or not. The features could be extended with other sources of information regarding the borrower.

Supervised learning algorithms can be used to classify the application of the potential borrower as either approved or rejected, based on their probability of a future default on the loan. One of the suitable ML model types would be classification algorithms, which split the dataset into either the ‘default’ or ‘non-default’ category, based on their features.

Challenger models

When there is already a model in place, it can be helpful to challenge this model. The model in use can be compared to a challenger model to evaluate differences in performance. Furthermore, the challenger model can identify possible effects in the data that are not captured yet in the model in use. Such analysis can be performed as a review of the model in use or before taking the model into production as a part of a model validation.

The aim of a challenger model is to challenge the model in use. As it is usually not feasible to design another sophisticated model, mostly simpler models are selected as challenger model. ML models can be useful to create more advanced challenger models within a relatively limited amount of time.

Challenger models do not necessarily have to be explainable, as they will not be used in practice, but only as a comparison for the model in use. This makes all ML models suitable as challenger models, even black box models such as neural networks.

Segmentation

Segmentation concerns dividing a full data set into subsets based on certain characteristics. These subsets are also referred to as segments. Often segmentation is performed to create a model per segment to better capture the segment’s specific behavior. Creating a model per segment can lower the error of the estimations and increase the overall model accuracy, compared to a single model for all segments combined.

Segmentation can, among other uses, be applied in credit rating models, prepayment models and marketing. For these purposes, segmentation is sometimes based on expert judgement and not on a data-driven model. ML models could help to change this and provide quantitative evidence for a segmentation.

There are two approaches in which ML models can be used to create a data-driven segmentation.  One approach is that observations can be placed into a certain segment with similar observations based on their features, for example by applying a clustering or classification algorithm. Another approach to segment observations is to evaluate the output of a target variable or label. This approach assumes that observations in the same segment have the same kind of behavior regarding this target variable or label.

In the latter approach, creating a segment itself is not the goal, but optimizing the estimation of the target variable or classifying the right label is. For example, all clients in a segment ‘A’ could be modeled by function ‘a’, where clients in segment ‘B’ would be modeled by function ‘b’. Functions ‘a’ and ‘b’ could be regression models based on the features of the individual clients and/or macro variables that give a prediction for the actual target variable.

Credit scoring

Companies and/or debt instruments can receive a credit rating from a credit rating agency. There are a few well-known rating agencies providing these credit ratings, which reflects their assessment of the probability of default of the company or debt instrument. Besides these rating agencies, financial institutions also use internal credit scoring models to determine a credit score. Credit scores also provide an expectation on the creditworthiness of a company, debt instrument or individual.

Supervised ML models are suitable for credit scoring, as the training of the ML model can be done on historical data. For historical data, the label (‘defaulted’ or ‘not defaulted’) can be observed and extensive financial data (the features) is mostly available. Supervised ML models can be used to determine reliable credit scores in a transparent way as an alternative to traditional credit scoring models. Alternatively, credit scoring models based on ML can also act as challenger models for traditional credit scoring models. In this case, explainability is not a key requirement for the selected ML model.

Conclusion

ML can add value to, or replace, models applied in financial risk management. It can be used in many different model types and in many different manners. A few examples have been provided in this article, but there are many more.

ML models learn directly from the data, but there are still some choices to be made by the model user. The user can select the model type and must determine how to calibrate the hyperparameters. There is no ‘one size fits all’ solution to calibrate a ML model. Therefore, ML is sometimes referred to as an art, rather than a science.

When applying ML models, one should always be careful and understand what is happening ‘under the hood’. As with all modeling activities, every method has its pitfalls. Most ML models will come up with a solution, even if it is suboptimal. Common sense is always required when modeling. In the right hands though, ML can be a powerful tool to improve modeling in financial risk management.

Working with ML models has given us valuable insights (see the box below). Every application of ML led to valuable lessons on what to expect from ML models, when to use them and what the pitfalls are.

Machine learning and Zanders

Zanders already encountered several projects and research questions where ML could be applied. In some cases, the use of ML was indeed beneficial; in other cases, traditional models turned out to be the better solution.

During these projects, most time was spent on data collection and data pre-processing. Based on these experiences, an ML based dataset validation tool was developed. In another case, a model was adapted to handle missing data by using an alternative available feature of the observation.

ML was also used to challenge a Zanders internal credit rating model. This resulted in useful insights on potential model improvements. For example, the ML model provided more insight in variable importance and segmentation. These insights are useful for the further development of Zanders’ credit rating models. Besides the insights what could be done better, the ML model also emphasized the advantages of classical models over the ML-based versions. The ML model was not able to provide more sensible ratings than the traditional credit rating model.

In another case, we investigated whether it would be sensible and feasible to use ML for transaction screening and anomaly detection. The outcome of this project once more highlighted that data is key for ML models. The available data was numerous, but of low quality. Therefore, the used ML models were not able to provide a helpful insight into the payments, or to consistently detect divergent payment behavior on a large scale.

Besides the projects where ML was used to deliver a solution, we investigated the explainability of several ML models. During this process we gained knowledge on techniques to provide more insights into otherwise hardly understandable (black box) models.

This site is registered on wpml.org as a development site.