While many business and SAP users are familiar with its core functionalities, such as limit management applying different limit types and the core functionality of attributable amount determination, several less known SAP standard features can enhance your credit risk management processes.

In this article, we will explore these hidden gems, such as Group Business Partners and the ways to manage the limit utilizations using manual reservations and collateral. 

Group Business Partner Use

One of the powerful yet often overlooked features of the SAP Credit Risk Analyzer is the ability to use Group Business Partners (BP). This functionality allows you to manage credit and settlement risk at a bank group level rather than at an individual transactional BP level. By consolidating credit and settlement exposure for related entities under a single group business partner, you can gain a holistic view of the risks associated with an entire banking group. This is particularly beneficial for organizations dealing with banking corporations globally and allocating a certain amount of credit/settlement exposure to banking groups. It is important to note that credit ratings are often reflected at the group bank level. Therefore, the use of Group BPs can be extended even further with the inclusion of credit ratings, such as S&P, Fitch, etc. 

Configuration: Define the business partner relationship by selecting the proper relationship category (e.g., Subsidiary of) and setting the Attribute Direction to "Also count transactions from Partner 1 towards Partner 2," where Partner 2 is the group BP. 

Master Data: Group BPs can be defined in the SAP Business Partner master data (t-code BP). Ensure that all related local transactional BPs are added in the relationship to the appropriate group business partner. Make sure the validity period of the BP relationship is valid. Risk limits are created using the group BP instead of the transactional BP. 

Reporting: Limit utilization (t-code TBLB) is consolidated at the group BP level. Detailed utilization lines show the transactional BP, which can be used to build multiple report variants to break down the limit utilization by transactional BP (per country, region, etc.). 

Having explored the benefits of using Group Business Partners, another feature that offers significant flexibility in managing credit risk is the use of manual reservations and collateral contracts. 

Use of Manual Reservations 

Manual reservations in the SAP Credit Risk Analyzer provide an additional layer of flexibility in managing limit utilization. This feature allows risk managers to manually add a portion of the credit/settlement utilization for specific purposes or transactions, ensuring that critical operations are not hindered by unexpected credit or settlement exposure. It is often used as a workaround for issues such as market data problems, when SAP is not able to calculate the NPV, or for complex financial instruments not yet supported in the Treasury Risk Management (TRM) or Credit Risk Analyzer (CRA) settings. 

Configuration: Apart from basic settings in the limit management, no extra settings are required in SAP standard, making the use of reservations simpler. 

Master data: Use transaction codes such as TLR1 to TLR3 to create, change, and display the reservations, and TLR4 to collectively process them. Define the reservation amount, specify the validity period, and assign it to the relevant business partner, transaction, limit product group, portfolio, etc. Prior to saving the reservation, check in which limits your reservation will be reflected to avoid having any idle or misused reservations in SAP. 

While manual reservations provide a significant boost to flexibility in limit management, another critical aspect of credit risk management is the handling of collateral. 

Collateral 

Collateral agreements are a fundamental aspect of credit risk management, providing security against potential defaults. The SAP Credit Risk Analyzer offers functionality for managing collateral agreements, enabling corporates to track and value collateral effectively. This ensures that the collateral provided is sufficient to cover the exposure, thus reducing the risk of loss.  

SAP TRM supports two levels of collateral agreements:  

1. Single-transaction-related collateral 
2. Collateral agreements.  

Both levels are used to reduce the risk at the level of attributable amounts, thereby reducing the utilization of limits. 

Single-transaction-related collateral: SAP distinguishes three types of collateral value categories: 

• Percentual collateralization 
• Collateralization using a collateral amount 
• Collateralization using securities 

Configuration: configure collateral types and collateral priorities, define collateral valuation rules, and set up the netting group. 

Master Data: Use t-code KLSI01_CFM to create collateral provisions at the appropriate level and value. Then, this provision ID can be added to the financial object. 

Reporting: both manual reservations and collateral agreements are visible in the limit utilization report as stand- alone utilization items. 

By leveraging these advanced features, businesses can significantly enhance their risk management processes. 

Conclusion

The SAP Credit Risk Analyzer is a comprehensive tool that offers much more than meets the eye. By leveraging its hidden functionalities, such as Group Business Partner use, manual reservations, and collateral agreements, businesses can significantly enhance their credit risk management processes. These features not only provide greater flexibility and control but also ensure a more holistic and robust approach to managing credit risk. As organizations continue to navigate the complexities of the financial landscape, unlocking the full potential of the SAP Credit Risk Analyzer can be a game-changer in achieving effective risk management. 

If you have questions or are keen to see the functionality in our Zanders SAP Demo system, please feel free to contact Aleksei Abakumov or any Zanders SAP consultant. 

In brief:

• Prevailing uncertainty in geopolitical, economic and regulatory environments demands a more dynamic approach to default modelling.
• Traditional methods such as logistic regression fail to address the non-linear characteristics of credit risk.
• Score-based models can be cumbersome to calibrate with expertise and can lack the insight of human wisdom.
• Machine learning lacks the interpretability expected in a world where transparency is paramount.
• Using the Bayesian Gaussian Process Classifier defines lending parameters in a more holistic way, sharpening a bank’s ability to approve creditworthy borrowers and reject proposals from counterparties that are at a high risk of default.

Historically high levels of economic volatility, persistent geopolitical unrest, a fast-evolving regulatory environment – a perpetual stream of disruption is highlighting the limitations and vulnerabilities in many credit risk approaches. In an era where uncertainty persists, predicting risk of default is becoming increasingly complex, and banks are increasingly seeking a modelling approach that incorporates more flexibility, interpretability, and efficiency.

While logistic regression remains the market standard, the evolution of the digital treasury is arming risk managers with a more varied toolkit of methodologies, including those powered by machine learning. This article focuses on the Bayesian Gaussian Process Classifier (GPC) and the merits it offers compared to machine learning, score-based models, and logistic regression.

A non-parametric alternative to logistic regression

The days of approaching credit risk in a linear, one-dimensional fashion are numbered. In today’s fast paced and uncertain world, to remain resilient to rising credit risk, banks have no choice other than to consider all directions at once. With the GPC approach, the linear combination of explanatory variables is replaced by a function, which is iteratively updated by applying Bayes’ rule (see Bayesian Classification With Gaussian Processes for further detail).

For default modelling, a multivariate Gaussian distribution is used, hence forsaking linearity. This allows the GPC to parallel machine learning (ML) methodologies, specifically in terms of flexibility to incorporate a variety of data types and variables and capability to capture complex patterns hidden within financial datasets.

A model enriched by expert wisdom

Another way GPC shows similar characteristics to machine learning is in how it loosens the rigid assumptions that are characteristic of many traditional approaches, including logistic regression and score-based models. To explain, one example is the score-based Corporate Rating Model (CRM) developed by Zanders. This is the go-to model of Zanders to assess the creditworthiness of corporate counterparties. However, calibrating this model and embedding the opinion of Zanders’ corporate rating experts is a time-consuming task. The GPC approach streamlines this process significantly, delivering both greater cost- and time-efficiencies. The incorporation of prior beliefs via Bayesian inference permits the integration of expert knowledge into the model, allowing it to reflect predetermined views on the importance of certain variables. As a result, the efficiency gains achieved through the GPC approach don’t come at the cost of expert wisdom.

Enabling explainable lending decisions

As well as our go-to CRM, Zanders also houses machine learning approaches to default modelling. Although this generates successful outcomes, with machine learning, the rationale behind a credit decision is not explicitly explained. In today’s volatile environment, an unexplainable solution can fall short of stakeholder and regulator expectations – they increasingly want to understand the reasoning behind lending decisions at a forensic level. 

Unlike the often ‘black-box’ nature of ML models, with GPC, the path to a decision or solution is both transparent and explainable. Firstly, the GPC model’s hyperparameters provide insights into the relevance and interplay of explanatory variables with the predicted outcome. In addition, the Bayesian framework sheds light on the uncertainty surrounding each hyperparameter. This offers a posterior distribution that quantifies confidence in these parameter estimates. This aspect adds substantial risk assessment value, contrary to the typical point estimate outputs from score-based models or deterministic ML predictions. In short, an essential advantage of the GPC over other approaches is its ability to generate outcomes that withstand the scrutiny of stakeholders and regulators.

A more holistic approach to probability of default modelling

In summary, if risk managers are to tackle the mounting complexity of evaluating probability of default, they need to approach it non-linearly and in a way that’s explainable at every level of the process. This is throwing the spotlight onto more holistic approaches, such as the Gaussian Process Classifier. Using this methodology allows for the incorporation of expert intuition as an additional layer to empirical evidence. It is transparent and accelerates calibration without forsaking performance. This presents an approach that not only incorporates the full complexity of credit risk but also adheres to the demands for model interpretability within the financial sector.

Are you interested in how you could use GPC to enhance your approach to default modelling? Contact Kyle Gartner for more information.

The Basel IV reforms published in 2017 will enter into force on January 1, 2025, with a phase-in period of 5 years. These are probably the most important reforms banks will go through after the introduction of Basel II. The reforms introduce changes in many areas. In the area of credit risk, the key elements of the banking package include the revision of the standardized approach (SA), and the introduction of the output floor. 

In this article, we will analyse in detail the recent updates made to real estate exposures and their impact on capital requirements and internal processes, with a particular focus on collateral valuation methods. 

Real Estate Exposures 

Lending for house purchases is an important business for banks. More than one-third of bank loans in the EU are collateralised with residential immovable property. The Basel IV reforms introduce a more risk-sensitive framework, featuring a more granular classification system. 

Standardized Approach 

The new reforms aim for banks to diminish the advantages gained from using the Internal Ratings-Based (IRB) model. All financial institutions that calculate capital requirements with the IRB approach are now required to concurrently use the standardized approach. Under the Standardized Approach, financial institutions have the option to choose from two methods for assigning risk weights: the whole-loan approach and the split-loan approach. 

Collateral Valuation  

A significant change introduced by the reforms concerns collateral valuation. Previously, the framework allowed banks to determine the value of their real estate collateral based on either the market value (MV) concept or the mortgage lending value (MLV) concept. The revised framework no longer differentiates between these two concepts and introduces new requirements for valuing real estate for lending purposes by establishing a new definition of value. This aims to mitigate the impact of cyclical effects on the valuation of property securing a loan and to maintain more stable capital requirements for mortgages. Implementing an independent valuation that adheres to prudent and conservative criteria can be challenging and may result in significant and disruptive changes in valuation practices.  

Conclusion  

To reduce the impact of cyclical effects on the valuation of property securing a loan and to keep capital requirements for mortgages more stable, the regulator has capped the valuation of the property, so that it cannot for any reason be higher than the one at origination, unless modifications to that property unequivocally increase its value. Regulators have high expectations for accounting for environmental and climate risks, which can influence property valuations in two ways. On the one hand, these risks can trigger a decrease in property value. On the other hand, they can enhance value, as modifications that improve a property's energy performance or resilience to physical risks - such as protection and adaptation measures for buildings and housing units - may be considered value-increasing factors. 

Where Zanders can help 

Based on our experience, we specialize in assisting financial institutions with various aspects of Basel IV reforms, including addressing challenges such as limited data availability, implementing new modelling approaches, and providing guidance on interpreting regulatory requirements.  

For further information, please contact Marco Zamboni. 

Dan Delean recently joined Zanders as Managing Director of our newly formed US risk advisory practice. With a treasury career spanning more than 30 years, including 15 years specializing in risk advisory, he comes to us with an impressive track record of building high performing Big 4 practices. As Dan will be spearheading the growth of Zanders’ risk advisory capabilities in the US, we asked him to share his vision for our future in the region.

Q. What excites you the most about leading Zanders' entry into the US market for risk advisory services?

Dan: This is a chance to build a world-class risk advisory practice in the US. Under the leadership of Paul DeCrane, the quality of Zanders reputation in the US has already been firmly established and I’m excited to build on this. I love to build – teams, solutions, physical building – and I am unnaturally passionate about treasury. Treasury is a small universe here, so getting traction is a key challenge – but once we do, it will catch fire.

Q. What do you see as the unique challenges (or opportunities) for Zanders in the US market?

Dan: A key concern for financial institutions in the US right now is the low availability of highly competent treasury professionals. Rising interest rates, combined with economic and political uncertainty, are driving up demand for deeper treasury insights in the US. In particular, the regulatory regime here is increasing its focus on liquidity and funding challenges, with a number of banking organizations on the ‘list’ for closing. But while the need for deep treasury competencies is growing fast, the pool of talent can’t keep up with this demand. This is an expertise gap Zanders is perfectly placed to address.

Q. How do you plan to tailor Zanders' risk advisory services to meet the specific needs and expectations of US clients?

Dan: My plan is to attract the best talent available, building a team with the capability to work with clients to tackle the hardest problems in the market. I want to build a recognized risk advisory team, that’s trusted by clients with difficult challenges. My intention is to focus on building these competencies through a highly focused approach to teaming.

Q. In what ways do you believe Zanders' approach to risk advisory services sets it apart from other firms in the US market?

Dan: Focus on competencies and effective teaming will make Zanders stand out among advisory businesses in the US.  Zanders is an expert-driven, competency focused practice, with a large team of seasoned treasury and risk professionals and a willingness to team up with other industry players. This approach is not common in the US. Most firms here deploy leverage models or are highly technical.

Q. What kind of culture or working environment do you aim to foster within the US branch of Zanders?

Dan: I’m committed to recruiting well, training even better, and being a key supporter of my team. I believe culture starts at the top, so all team members that join or work with us need to buy into the expert model and Zanders’ approach to advisory.  Within this culture, trust and accountability will always be core tenets – these will be central to my approach to teaming. 

With his value-driven, competency-led approach to teaming and practice development, there’s no-one better qualified than Dan to lead the growth of our US risk advisory. To learn more about Zanders and what makes us different, please visit our About Zanders page.

Economic instability, a pandemic, geopolitical turbulence, rising urgency to get to net zero – a continuousstream of demands and disruption have pushed businesses to their limits in recent years. What this has proven without doubt is that treasury can no longer continue to be an invisible part of the finance function. After all, accurate cash flow forecasting, working capital and liquidity management are all critical C-suite issues. So, with the case for a more strategic treasury accepted, CFOs are now looking to their corporate treasurer more than ever for help with building financial resilience and steering the business towards success.

The future form of corporate treasury is evolving at pace to meet the demands, so to bring you up to speed, we discuss in this article five key observations we believe will have the most significant impact on the treasury function in the coming year(s).

1. A sharper focus on productivity and performance

Except for some headcount reductions, treasury has remained fairly protected from the harsh cost-cutting measures of recent years. However, with many OPEX and CAPEX budgets for corporate functions under pressure, corporate treasurers need to be prepared to justify or quantify the added value of their function and demonstrate how treasury technology is contributing to operational efficiencies and cost savings. This requires a sharper focus on improving productivity and enhancing performance.

To deliver maximum performance in 2024, treasury must focus on optimizing structures, processes, and implementation methods. Further digitalization (guided by the blueprint provided by Treasury 4.0) will naturally have an influential role in process optimization and workflow efficiency. But to maintain treasury budgets and escape an endless spiral of cost-cutting programs will take a more holistic approach to improving productivity. This needs to incorporate developments in three factors of production – personnel, capital, and data (in this context, knowledge).

In addition, a stronger emphasis on the contribution of treasury to financial performance is also required. Creating this direct link between treasury output and company financial performance strengthens the function’s position in budget discussions and reinforces its role both in finance transformation processes and throughout the financial supply chain.

2. Treasury resilience, geopolitical risk and glocalization

Elevated levels of geopolitical risk are triggering heightened caution around operational and financial resilience within multinationals. As a result, many corporations are rethinking their geographical footprint and seeking ways to tackle overdependence on certain geographical markets and core suppliers. This has led to the rise of ‘glocalization’ strategies, which typically involve moving away from the traditional approach of offshoring operations to low-cost destinations to a more regional approach that’s closer to the end market.

The rise of glocalization is forcing treasury to recalibrate its target operating model to adopt a more regionalized approach. This typically involves changing from a ‘hub and spoke’ model to multiple hubs. But the impact on treasury is not only structural. Operating in many emerging and frontier markets creates heightened risks around currency restrictions, lack of local funding and the inability to repatriate cash. Geopolitical tensions can also have spillover effects to the financial markets in these countries. This necessitates the application of more financial resilience thinking from treasury.

3. Cash is king, data is queen

Cash flow forecasting remains a top priority for corporate treasurers. This is driving the rise of technology capable of producing more accurate cash flow predictions, faster and more efficiently. Predictive and prescriptive analytics and AI-based forecasting provide more precise and detailed outcomes compared to human forecasting. While interfaces or APIs can be applied to accelerate information gathering, facilitating faster and automated decision-making. But to leverage the benefits of these advanced applications of technology requires robust data foundations. In other words, while technology plays a role in improving the cash flow forecasting process, it relies on an accurate and timely source of real-time data. As such, one can say that cash may still be king, but data is queen.

In addition, a 2023 Zanders survey underscored the critical importance of high-quality data in financial risk management. In particular, the survey highlighted the criticality of accurate exposure data and pointed out the difficulties faced by multinational corporations in consolidating and interpreting information. This stressed the necessity of robust financial risk management through organizational data design, leveraging existing ERP or TMS technology or establishing a data lake for processing unstructured data.

4. The third wave of treasury digitalization

We’ve taken the three waves of digitalization coined by Steve Case (former CEO of US internet giant AOL) and applied them to the treasury function. The first wave was the development of stand-alone treasury and finance solutions, followed by the second wave bringing internal interfaces and external connectivity between treasury systems. The third wave is about how to leverage all the data coming from this connected treasury ecosystem. With generative AI predicted to have an influential role in this third phase, corporate treasurers need to incorporate the opportunities and challenges it poses into their organizations' digital transformation journeys and into discussions and decisions related to other technologies within their companies, such as TMS, ERP, and banking tools.

We also predict the impact and success of this third wave in treasury digitalization will be dependent on having the right regulatory frameworks to support its implementation and operation. The reality is, although we all aspire to work in a digital, connected world, we must be prepared to encounter many analogue frictions – like regulatory requirements for paper-based proof, sometimes in combination with ‘wet’ signatures and stamped documents. This makes the adoption of mandates, such as the MLETR (Model Law on Electronic Transferable Records) a priority.

5. Fragmentation and interoperability of the payment landscape

A side effect of the increasing momentum around digital transformation is fragmentation across the payments ecosystem. This is largely triggered by a rapid acceleration in the use of digital payments in various forms. We’ve now seen successful trials of Central Bank Digital Currency, Distributed Ledger Technology to enable cross border payments, a rise in the use of digital wallets not requiring a bank account, and the application of cross border instant payments. All of these developments lead us to believe that international banking via SWIFT will be challenged in the future and treasurers should prepare for a more fragmented international payment ecosystem that supports a multitude of different payment types. To benefit from this development, interoperability will be crucial.

Conclusion: A turning point for treasury

A succession of black swan events in recent years has exposed a deep need for greater financial resilience. The treasury function plays a vital role in helping their CFO build this. This is accelerating both the scale and pace of transformation across the treasury function, with wide-ranging effects on its role in the C-suite, position in finance, the priorities and structure of the function, and the investment required to support much-needed digitalization.

For more information on the five observations outline here, you can read the extended version of this article.

The European Committee (EC) has approved the regulatory technical standards (RTS) that include the specification of the Net Interest Income (NII) Supervisory Outlier Test (SOT). The SOT limit for the decrease in NII is set at 5% of Tier 1 capital. Since the three-month scrutiny period has ended it is expected that the final RTS will be published soon. 20 days after the publication the RTS will go into force. The acceptance of the NII SOT took longer than expected among others due to heavy pushback from the banking sector.  The SOT, and the fact that some banks rely heavily on it for their internal limit framework is also one of the key topics on the heatmap IRRBB published by the European Banking Authority (EBA). The heatmap detailing its scrutiny plans for implementing interest rate risk in the banking book (IRRBB) standards across the EU. In the short to medium term (2024/Mid-2025), the focus is on

• The EBA has noted that some banks use the as an internal limit without identifying other internal limits. The EBA will explore the development of complementary indicators useful for SREP purposes and supervisory stress testing.
• The different practices on behavioral modelling of NMDs reported by the institutions.
• The variety of hedging strategies that institutions have implemented.
• Contribute to the Dynamic Risk Management project of the International Accounting Standards Board (IASB), which will replace the macro hedge accounting standard.

In the medium to long-term objectives (beyond mid-2025) the EBA mentions it will monitor the five-year cap on NMDs and CSRBB definition used by banks. No mention is made by the EBA on the consultation started by the Basel Committee on Banking Supervision, on the newly calibrated interest rate scenarios methodology and levels. In the coming weeks, Zanders will publish a series of articles on the Dynamic Risk Management project of the IASB and what implications it will have for banks. Please contact us if you have any questions on this topic or others such as NMD modelling or the internal limit framework/ risk appetite statements.

The European Banking Authority (EBA) published its roadmap on the Banking Package, which implements the final Basel III reforms in the European Union. This roadmap develops over four phases, and it is expected to be completed as follows:

• Phase 1: Covers 32 mandates in the areas of credit, market and operational risk, which predominantly result from the transition to Basel III. In addition, this first phase will also see the first mandates under the Capital Requirements Directive (CRD) in the area of ESG.
• Phase 2: This phase will further progress in covering Capital Requirements Regulation (CRR) mandates related to credit, operational and market risk. Furthermore, a considerable number of CRD mandates related to high EU standards in terms of governance and access to the single market with regard to third-country branches will be developed in this phase.
• Phase 3: It includes most of the remaining mandates related to regulatory products as well as a number of reports, whereby further perspectives and initial monitoring efforts regarding banking regulation implementation are worth considering.•      
• Phase 4: In this last phase, a number of products, mostly consisting of reports, will be developed, providing information on the implementation progress, results and challenges.   

In addition, there are some mandates that are ongoing and reoccurring and are not part of any of the four phases but will be made operational at the date of implementation in 2025. As part of phase 1, the EBA has published multiple consultation papers, which form the first step in the implementation of the Banking Package. The three main consultation papers published are: 

1. A public consultation on two draft ITS amending Pillar 3 disclosure requirements and supervisory reporting requirements. The suggested amendments on the reporting obligations cover a wide range of topics such as the output floor, standardized and internal ratings-based models (IRB) for credit risk, the three new approaches for own funds requirements for CVA risk and the (simplified) standardized approach for market risk.
2. A public consultation launched by the EBA on the Regulatory Technical Standards (RTS) determining the conditions for an instrument with residual risk to be classified as a hedge. This consultation, on the standardized approach under the FRTB framework, focuses on the residual risk add-on (RRAO). Introduced by the Capital Requirements Regulation (CRR3), the RRAO framework allows exemptions for instruments hedging residual risks. The proposed RTS outline criteria for identifying hedges, distinguishing between non-sensitivity-based method risk factors and other reasons for RRAO charges.
3. A public consultation on two draft Implementing Technical Standards (ITS) amending Pillar 3 disclosures and supervisory reporting requirements for operational risk. These revisions align with the new Capital Requirements Regulation (CRR3) and aim to consolidate reporting and disclosure requirements for operational risk and broader CRR3 changes. These consultation papers should be read in conjunction with the consultation papers on the new framework for the business indicator for operational risk, published at the same time.

This paper offers a straightforward analysis of the Basel Committee on Banking Supervision's standards on crypto asset exposures and their adoption by 2025. It critically assesses infrastructure risks, categorizes crypto assets for regulatory purposes, and proposes a flexible approach to managing these risks based on the blockchain network's stability. Through expert interviews, key risk drivers are identified, leading to a framework for quantifying infrastructure risks. This concise overview provides essential insights for financial institutions navigating the complex regulatory and technological landscape of crypto assets.

In recent years, consumers’ and investors’ interest in sustainability has been growing. Since 2015, assets under management in ESG funds have nearly tripled, the outstanding value of green bonds issued by residents of the euro area has surged eightfold, and emission-related derivatives have seen a more than sevenfold increase¹. 

The global push for sustainable and environmentally responsible practices has led to an increased focus on the role of financial institutions in supporting green initiatives. One of the ways financial institutions use to incentivise sustainable investments, is by designing new products, such as blue bonds to protect marine areas and other sustainability-linked bonds², or by transitioning to funding sectors with positive sustainability impact.

However, amidst the growing wave of environmental consciousness, the credibility of "green" claims made by some financial institutions is a point of concern. This phenomenon, known as greenwashing, is gaining attention, not only within financial institutions, but also with regulators. Financial regulators, including the European Supervisory Authorities (ESAs) and UK’s Financial Conduct Authority (FCA) have taken action against potentially misleading green statements made by institutions. Despite these regulatory interventions, the persistent risk of greenwashing persists, primarily due to the absence of consistent standards governing sustainability claims and disclosures. The lack of uniform criteria poses an ongoing challenge to effectively combatting greenwashing practices within the financial landscape.

Defining Greenwashing

The ESAs describe greenwashing as “a practice where sustainability-related statements, declarations, actions, or communications do not clearly and fairly reflect the underlying sustainability profile of an entity, a financial product, or financial services. This practice may be misleading to consumers, investors, or other market participants” ³.

Financial institutions, as key players in the global economy, play a crucial role in fostering sustainability. However, some have been accused of using deceptive practices to push their green image without making substantial changes. This practice may be misleading to consumers, investors, and other market participants.

In practice, greenwashing can take different forms depending on the institution. For insurance companies, the European Insurance and Occupational Pensions Authority (EIOPA) found in their Advise to the European Commission on Greenwashing⁴ various examples where insurers misleadingly claimed to be transitioning their underwriting activities to net zero by 2050 without any credible plans to do so. Other examples include insurance companies falsely claiming to plant trees for each life insurance policy sold but failing to fulfil this promise, or products being marketed as sustainable merely because of a positive "ESG rating," despite the rating not taking into account any actual sustainability factors and focusing solely on financial risks.

Withing the banking sector, the EBA reported⁵ that the most common misleading claims relate to the current approach to integrating sustainability into the business strategy, claims on the sustainability results and the real-world impact, and claims on future commitments on medium and long-term plans.

Finally, for investment companies and pension funds, the European Securities and Markets Authority (ESMA) reported⁶ that most the common greenwashing practices result from exaggerated claims without any proven link between and ESG metric and the real-world impact.

Key Indicators of Greenwashing:

1. Vague and Ambiguous Language: Financial institutions engaging in greenwashing often use vague terms and ambiguous language in their marketing materials. This lack of clarity makes it challenging for consumers to discern the actual environmental impact of their investments.
2. Lack of Transparency: Genuine commitment to sustainability involves transparency about investment choices and the environmental impact of financial products. Institutions that are less forthcoming about their practices may be concealing less-than-green investments.
3. Inconsistent Policies: Greenwashing is also evident when there is a misalignment between a financial institution's sustainability claims and its actual policies and practices. Actions, or lack thereof, can speak louder than words.

The Role of Regulatory Bodies

Greenwashing poses potential reputational and financial risks for the institutions involved. Addressing greenwashing might not only improve consumer’s trust in the products and services offered by financial institutions, but also will allow customers to make informed decisions that are align with their sustainability preferences and increase the capital into products that genuinely represent a more sustainable choice and drive a positive change. Tackling greenwashing should therefore be a priority for regulatory supervisors.

The introduction of the EU’s Taxonomy Regulation and the Sustainable Finance Disclosure Regulation (SFDR) addresses the initial concerns of greenwashing within the financial sector. The Taxonomy determines which economic activities are environmentally sustainable and addresses greenwashing by enabling market participants to identify and invest in sustainable assets with more confidence. SFDR promotes openness and transparency in sustainable finance transactions and requires Financial Market Participants to share the environmental and social impact of their transactions with stakeholders. In May 2023, the ESA published their progress report on greenwashing monitoring and supervision⁷. The report aims to provide insights into an understanding of greenwashing and identify the specific forms it can take within banking. It also evaluates greenwashing risk within the EU banking sector and determines the extend to which it might be and issue from a regulatory perspective.

In the UK, the FCA published in November 2023 a guidance consultation on the Anti-Greenwashing Rule⁸. The anti-greenwashing rule is one part of a package of measures introduced through the Sustainability Disclosure Requirements (SDR). The anti-greenwashing rule requires FCA-authorised firms to ensure that any claims they make to the sustainability characteristics of their financial products and services are consistent with the actual sustainability characteristics of the product or service and are fair, clear and not misleading, and have evidence to back them up. The propose rule will come into force on 31 May 2024.

While the existing and planned regulation contributes to addressing aspects of greenwashing, several measures have not yet fully entered into application, making the impact of the frameworks not visible yet. Beyond disclosures, regulators should also focus on tightening requirements on sustainability data and ratings, and creating mandates to prevent misleading statements and unfair commercial practices.

Going forward, as regulators gain more experience to comprehensively address greenwashing, financial institutions should expect increased supervision and enforcement of sustainable finance policies aimed at preventing misleading sustainability claims.

Actions to mitigate greenwashing risk

One of the biggest challenges financial institutions faced in relation to sustainability is that scientific progress, policy development and social values are in constant evolution. What was a well-supported green initiative two years ago can potentially be considered as greenwashing today.

In the meantime that stricter regulations and guidance is in place, financial institutions should take a broad view on how to develop and communicate sustainability strategies to mitigate greenwashing risk.

Here are three ways on how to prevent greenwashing:

1. Promote disclosure: financial institutions should publish comprehensive sustainability reports and disclose ESG information as part of their financial reports.
2. Commit to transparency: claims about environmental aspects or performance of their products should be justified with science-based and verifiable methods. Financial institutions should be transparent about their ambitions, status, and be open about any shortcomings they identified.
3. Align business practices with purpose:  financial institutions should determine which climate-related and environmental risks impact business strategy in the short, medium and long term. They should reflect climate-related and environmental risks in business strategies and its implementation. In addition, they should balance sustainability ambitions with the reality of real transformation.

Zanders’ approach to managing reputational risk

Avoiding greenwashing should always be a priority for institutions. If a risk arises in this area, reputational risk management can help to limit negative effects. Due to the interdependencies between ESG, reputational, business and liquidity risk, the supervisory authorities are also increasingly focusing on this area.

In the context of reputational risk management, we recommend a holistic approach that includes both existing and new business in the analysis. In addition to identifying critical transactions from a reputational perspective, the focus is also on active stakeholder management. This requires cross-departmental cooperation between various units within the institution. In many cases, the establishment of a reputation risk management committee is key to manage that topic properly within the institution.

Conclusion

While many financial institutions genuinely strive for sustainability, the rise of greenwashing highlights the need for increased vigilance and scrutiny. Consumers, regulators, and industry stakeholders must work together to ensure that financial institutions align their actions with their environmental claims, fostering a truly sustainable and responsible financial sector.

Curious to learn more? Please contact: Elena Paniagua-Avila or Martin Ruf

Model risk from risk models has become a focal point of discussion between regulators and the banking industry. As financial institutions strive to enhance their model risk management practices, the need for robust model risk quantification becomes paramount.​​

An introduction to model risk quantification​

Many firms already have comprehensive model risk management frameworks that tier models using an ordinal rating (such as high/medium/low risk). However, this provides limited information on potential losses due to model risk or the capital cost of already identified model risks. Model risk quantification uses quantitative techniques to bridge this gap and calculate the potential impact of model risk on a business. ​

The goal of a model risk quantification framework​

As with many other sources of risk within a financial institute, the aim is to manage risk by holding capital against potential losses from the use of individual models across the firm. This can be achieved by including model risk as a component of Pillar 2 within the Internal Capital Adequacy Assessment Process (ICAAP).​

Concepts in Model Risk Quantification​

Impacts of Model Risk​

Model risk significantly influences financial institutions through valuations, capital requirements, and overall risk management strategies. The uncertainties tied to model outcomes can have profound impacts on regulatory compliance, economic capital, and the firm's standing in the financial ecosystem.​

Model tiering​

Model tiering is a qualitative exercise that assesses the holistic risk of a model by considering various factors (e.g. materiality, importance, complexity, transparency, operational intricacies, and controls).​

The tiering output grades the risk of a model on an ordinal scale, comparing it to other models within the institute. However, it doesn't provide a quantitative metric that can be aggregated with other models.​

Overlap with quantitative regulations​

Most firms already perform quantitative processes to measure the performance of Pillar 1 models that impact the regulatory capital held (such as the VaR backtesting multiplier applied to market risk RWA).​

Model Risk Quantification Framework​ - The Model Uncertainty Approach​

A crucial step in building a robust model risk quantification framework is classifying and assessing the impact of model risk. The model uncertainty approach is an internal quantitative approach in which model risks are identified and quantified on an individual level. Individual model risks are subsequently aggregated and translated into a monetary impact on the bank.   

​Regulatory Model Risk Quantificaiton Methods​ - RNIV, Backtesting Multiplier, Prudent Valuation and MoC​

Most banks are already familiar with quantification techniques recommend by regulators for risk management. Below we highlight some of these techniques that can be used as the basis for expansion of quantification within a firm. ​

Expanding Model Risk Quantification​

Our approach to efficient measurement relies on two key components. The first is model risk classifications to prioritize models to quantify, and the second is a knowledge base of already implemented regulatory and internally developed techniques to quantify that risk. This approach provides good risk coverage whilst also being extremely resource efficient.​

Looking to learn more about Model Risk Management? Reach out to our experts Dr. Andreas Peter, Alexander Mottram, Hisham Mirza.