The near-final PRA Rulebook PS9/24 published on 12 September 2024 includes substantial changes in credit risk regulation compared to the Consultation Paper CP16/22. While these amendments enhance clarity of Basel 3.1 implementation, institutions should conduct in-depth impact analysis to efficiently manage capital requirement. 

PRA has published draft proposal CP16/22 aligning closely with Basel 3.1 reforms. In response to industry feedback, the PRA has made material adjustments in PS9/24, which are aimed at better balancing alignment with international standards and maintaining the competitiveness of UK regulated institutions.  

Key takeaways 

1- Scope for a ‘Backstop’ revaluation every 5-years for valuation of real estate exposures 

2- SME and Infrastructure support factor is maintained, yet firm-specific adjustments will be introduced in pillar 2A. 

3- Despite industry concern on international competitiveness, the risk-sensitive approach for unrated corporate exposure is maintained. 

The implementation timeline is extended to 1 January 2026 with a 4-year transitional period, which is a one-year delay from the proposed implementation date of 1 January 2025 from CP16/22.

Real Estate Exposures  

According to the final regulations, the risk weights associated with regulatory real estate exposure will be calculated based on the type of property, the loan-to-value (LTV) ratio, and whether the repayments rely significantly on the cash flows produced by the property. In place of the potentially complex analysis proposed in CP16/22, the rules for determining whether a real estate exposure is materially dependent on cash flows have been significantly simplified and there is now a straightforward requirement for the classification of real estate exposures.  

One major change in the proposals relates to loans that are secured by commercial properties. The PRA has dropped the 100% risk weight floor for exposures backed by commercial real estate (CRE), provided that the repayment is not 'materially dependent on cash flows from the property' and that the exposure fits the 'regulatory real estate (RRE)' definition. Consequently, under the new rules, firms may, in some instances, benefit from low risk weights for commercial real estate, depending on the loan's loan-to-value (LTV) ratio and the type of counterparty involved. 

Additionally, the final rules regarding the revaluation of real estate have become more risk-sensitive. Although firms are still required to use the original valuation to calculate LTV, there is now a provision allowing for a ‘Backstop’ revaluation after five years. Going forward, the PRA has eliminated the need for firms to adjust valuations to reflect the 'prudent value' sustainable throughout the loan's duration. The requirements for downward revaluation have been simplified, mandating firms to reevaluate properties when they estimate a market value decline of over 10%. Furthermore, the PRA has specified that valuations can be conducted using a robust statistical model, such as an automated valuation model (AVM). 

SME support factor 

The PRA has maintained the draft proposal to remove the SME support factor under SA and IRB (Pillar 1), but has applied a firm-specific structural adjustment to Pillar 2A (the ‘SME lending adjustment’). The ‘SME lending adjustment’ aims to absorb the impact of removing the SME support factor in overall capital requirement.  

The PRA plans to communicate the adjusted Pillar 2 requirements to firms, ahead of the implementation date of the Basel 3.1 standards on 1 January 2026 (‘day 1’), so that firm-specific requirements will be updated at the same time as the Basel 3.1 standards are implemented. 

Infrastructure support factor 

The PRA has maintained the draft proposal to remove infrastructure support factor under SA and IRB approach, but has made two material changes which will diminish the impact on overall capital requirement.  

i. apply a firm-specific structural adjustment to Pillar 2A (the ‘SME lending adjustment’), which will minimize disruption in overall capital requirement. 

ii. introduce a new substantially stronger category in the slotting approach for IRB. PRA proposed lower risk-weight on the ‘substantially stronger’ IPRE exposures in CP16/22. The new definition of ‘substantially stronger’ is expected to include broader scope of IPRE exposures, thus lowering overall capital requirement. 

Unrated corporate exposures 

The PRA has maintained the draft proposal of introducing a two-way method on unrated corporate exposures: risk-sensitive and risk-neutral approach. Since the new approach does not apply in other jurisdictions, additional operational challenge is expected. Also, the 135% risk-weight on Non-IG(non-Investment Grade) unrated corporate exposure is higher than 100% in other jurisdictions, implying higher lending cost for UK regulated banks compared to its internationally regulated peers.

i. risk-sensitive approach : The PRA has proposed a risk-sensitive approach additional to the Basel III reforms. Exposures assessed by firms as IG would be risk-weighted at 65%, while exposures assessed by firms as Non-IG would be risk-weighted at 135%. This is a more risk-sensitive approach which aims to maintain an aggregate level of RWAs broadly consistent with the Basel III reforms.  

ii. risk-neutral approach: 100% risk weight is applied where the risk-sensitive approach is too costly or complex.  

In conclusion, the PRA's near-final Rulebook (PS9/24) reflects significant revisions to credit risk regulation that enhance clarity and alignment with Basel 3.1, while addressing industry feedback. The introduction of a five-year 'backstop' revaluation for real estate exposures, firm-specific adjustments for SMEs and infrastructure support factors, and the maintenance of a risk-sensitive approach for unrated corporate exposures underscore the PRA's commitment to balancing regulatory requirements with maintaining the competitiveness of UK institutions.  

The extended implementation timeline to 1 January 2026, along with the transitional period, allows firms adequate time for adjustment. Overall, these changes aim to foster a more robust and competitive banking environment, while also navigating the complexities introduced by differing international standards.  

How can Zanders help?  

We have extensive experience of implementation and validation of Pillar 1 & 2 models which allows us to effectively support our Clients managing the change process to full compliance with the latest regulations. 

From our experience, as the following are key areas on which our services can add most value: 

1- Carry out thorough self-assessment against new requirements including an impact analyses of new regulations on their capital requirements. 

2- Support model development activities to align models to new rules; we could be done either on an advisory basis or via direct supply of additional resources 

3- Support amendments to Pillar 2 models (which will have to reflect changes to Pillar 1 models) 

4- Support Internal Validation activities across Pillar 1 & 2 

5- Carry out quality assurance on final models & documentation before final submission to the PRA

6- Support adoption of solutions for prudential valuation of (real estate) collateral while integrating climate risk information. 

Please reach out to Paolo Vareschi or Suneet Dutta Roy to find out more about how we could support you on your journey to Basel 3.1 compliance. 

References

[1] Bank of England (2024), PS9/24 – Implementation of the Basel 3.1 standards near-final part 2 URL PS9/24 

[2] Bank of England (2022), CP16/22 – Implementation of the Basel 3.1 standards 
URL CP16/22 

After a long period of negative policy rates within Europe, the past two years marked a period with multiple hikes of the overnight rate by central banks in Europe, such as the European Central Bank (ECB), in an effort to combat the high inflation levels in Europe. These increases led to tumult in the financial markets and caused banks to adjust the pricing of consumer products to reflect the new circumstances. These developments have given rise to a variety of challenges in modeling non-maturing deposits (NMDs). While accurate and robust models for non-maturing deposits are now more important than ever. These models generally consist of multiple building blocks, which together provide a full picture on the expected portfolio behavior. One of these building blocks is the calibration approach for parametrizing the relevant model elements, which is covered in this blog post. 

One of the main puzzles risk modelers currently face is the definition of the expected repricing profile of non-maturing deposits. This repricing profile is essential for proper risk management of the portfolio. Moreover, banks need to substantiate modeling choices and subsequent parametrization of the models to both internal and external validation and regulatory bodies. Traditionally, banks used historically observed relationships between behavioral deposit components and their drivers for the parametrization. Because of the significant change in market circumstances, historical data has lost (part of) its forecasting power. As an alternative, many banks are now considering the use of forward-looking scenario analysis instead of, or in addition to, historical data. 

The problem with using historical observations 

In many European markets, the degree to which customer deposit rates track market rates (repricing) has decreased over the last decade. Repricing first decreased because banks were hesitant to lower rates below zero. And currently we still observe slower repricing when compared to past rising interest cycles, since interest rate hikes were not directly reflected in deposit rates. Therefore, the long period of low and even negative interest rates creates a bias in the historical data available for calibration, making the information less representative. Especially since the historical data does not cover all parts of the economic cycle. On the other hand, the historical data still contains relevant information on client and pricing behavior, such that fully ignoring observed behavior also does not seem sensible.  

Therefore, to overcome these issues, Risk and ALM managers should analyze to what extent the historically repricing behavior is still representative for the coming years and whether it aligns with the banks’ current pricing strategy. Here, it could be beneficial for banks to challenge model forecasts by expectations following from economic rationale. Given the strategic relevance of the topic, and the impact of the portfolio on the total balance sheet, the bank’s senior management is typically highly involved in this process.  

Improving models through forward looking information 

Common sense and understanding deposit model dynamics are an integral part of the modeling process. Best practice deposit modeling includes forming a comprehensive set of possible (interest rate) scenarios for the future. To create a proper representation of all possible future market developments, both downward and upward scenarios should be included. The slope of the interest rate scenarios can be adjusted to reflect gradual changes over time, or sudden steepening or flattening of the curve. Pricing experts should be consulted to determine the expected deposit rate developments over time for each of the interest rate scenarios. Deposit model parameters should be chosen in such a way that its estimations on average provide the best fit for the scenario analysis. 

When going through this process in your organization, be aware that the effects of consulting pricing experts go both ways. Risk and ALM managers will improve deposit models by using forward-looking business opinions and the business’ understanding of the market will improve through model forecasts. 

Trying to define the most suitable calibration approach for your NMD model?

Would you like to know more about the challenges related to the calibration of NMD models based on historical data? Or would you like a comprehensive overview of the relevant considerations when applying forward-looking information in the calibration process?

Read our whitepaper on this topic: 'A comprehensive overview of deposit modelling concepts'

Banks perform data analytics, statistical modelling, and automate financial processes using model software, making model software essential for financial risk management. 

Why banks are recently moving their model software to open-source¹: 

• Volume dependent costs: Open-source comes with flexible storage space and computation power and corresponding costs and capacity.  
• Popularity: Graduates are often trained in open-source software, whereas the pool of skilled professionals in some specific software is decreasing. 
• Customization: You can tailor it exactly to your own needs. Nothing more, nothing less. 
• Collaboration: Required level of version control and collaboration with other software available within the bank. 
• Performance: Customization and flexibility allow for enhanced performance. 

Often there is a need to reconsider the model software solution the Bank is using; open-source solutions are cost effective and can be set up in such a way that both internal and external (e.g. regulatory) requirements are satisfied. 

How Zanders can support in moving to open-source model software. 

Proper implementation of open-source model software is compliant with regulation.   

• Replication of historical situations is possible via version control, containerization and release notes within the open-source model software. Allowing third parties to replicate historical model outcomes and model development steps.   
• Governance setup via open-source ensures that everything is auditable and is released in a governed way; this can be done through releases pipelines and by setting roles and responsibilities for software users.  
• This ensures that the model goes from development to production only after governed review and approval by the correct stakeholders.  

Migration to open-source software is achievable. 

• The open-source model software is first implemented including governance (release processes and roles and responsibilities).  
• Then all current (data and) models are refactored from the current model software to the new one.  
• Only after an extensive period of successful (shadow) testing the migration to the new model software is completed.  

Zanders has a wealth of experience performing these exercises at several major banks, often in parallel to (or combined with) model change projects. Hence, if you have interest, we will be happy to share some further insights into our latest experiences/solutions in this specific area.  

Therefore, we welcome you to reach out to Ward Broeders (Senior Manager).

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 final article from Zanders on the DRM model presents the lifecycle of the DRM model over a single hedge accounting period and the prospective and retrospective assessments that are required to be carried out to ensure that the entity is correctly mitigating its interest rate risk for the assets/liabilities designated for the Current Net Open Risk Position (CNOP). The cycle will be illustrated by Scenario 1C taken from Agenda Paper 4A – May 2023¹. This is a relatively simple example, more complex ones can be found within the staff paper. 

Figure 1: DRM Cycle

Prospective (start of the hedge accounting period)

The first three steps are related to the prospective assessment in the DRM model cycle.  

The use of the prospective assessment is to ensure that the model is being used to mitigate interest rate risk and achieve the target profile that is set out in the RMS. The RMS should include the following: 

• The risk mitigation cannot create new risks 
• The RMI has to transform the CNOP position to a residual risk position that sits within the target profile (TP) 

Step 1: The entity decides on the securities to be hedged and calculates the net open risk position (from an outstanding notional perspective) per time bucket.  

In the example below the company has floating and fixed exposures. The business in this case has a five-year fixed mortgage starting in 20x2 which is fully funded by a five-year floating rate liability. The focus period is 20x2 (start of the hedge accounting period) to 20x3 (end of the hedge accounting period) and so the first period 20x1 has been removed. The entity manages its entity-level interest rate risk for a 5-year time horizon, based on notional exposure in ∆NII and has decided to set the TP to be +/-EUR 500 in each of the repricing periods. Below we present the total fixed and total floating exposures from the product defined above). The individual breakdown of the fixed and floating is not required as each exposure is hedged as a total. The exposure are positions at year end. 

Table 1: CNOP of the Entity with yearly buckets

Step 2: The entity will calculate the RMI based on the designated derivatives. The entity decided to mitigate 80% of the risk through the use of the following derivatives (existing and new). Please note that is a combination of derivatives from all the derivatives available in the books: 

• A 5-year pay fixed receive floating IR swap with notional of EUR 1,000, traded on 1^st January 20x1 (DD Swap 1) (existing deal). 
• A 4-year receive fixed pay floating IR swap with notional of EUR 200, traded on 1^st January 20x2 (DD Swap 2) 

This leads to the designated derivatives with the following exposures: 

Table: Exposures of the Designated Derivatives 

The exposures of the designated derivatives can then be compared to the CNOP as shown below: 

Table 2: Exposures of the CNOP and Designated Derivatives

As the entity manages its interest rate risk based on ∆NII, the RMI focuses on the floating exposure.  

The prospective test is performed by comparing the CNOP and Designated Derivatives exposures at each time bucket to see whether this moves the residual risk inside the TP (+/- EUR 500) set out within the RMS and not providing an over-hedge position. In this case the residual risk will be 0 (80% of CNOP versus DD exposures) and so the prospective assessments pass for all the time buckets. 

Table 3: Prospective test

Step 3: Benchmark derivatives (hypothetical derivatives) are constructed based on the RMI calculated above.

Table 4: Benchmark Derivatives created for the Fixed and Floating Exposures

Retrospective (end of the hedge accounting period) 

The following steps are related to the retrospective assessment of the DRM model.  

The IASB requires a retrospective assessment, to check that risks have been mitigated, as well as a future capacity assessment for each period². This is to ensure the company is correctly mitigating its interest rate risk, ensuring the CNOP sits within their TP and to quantify the potential misalignment arising from unexpected changes (during the hedge accounting period). 

Step 4: The entity updates the CNOP with the latest ALM information (note that new business is excluded from the updated CNOP). 

In this example, the financial asset was repaid fully at the end of 20x5. The revised expectation is that it will be  partially repaid per end 20x4 and the rest  repaid end 20x5. 

Table 5: Updated CNOP

Step 5: The potential misalignment due to unexpected changes is calculated. The new CNOP is compared to the RMI that was set in Step 2. Misalignments can occur due to: 

• Difference in changes in the fair value of the designated derivatives and  benchmark derivatives (i.e: different fixed rate, fair value adjustments) 
• The effect of the unexpected changes in the current net open position during the period  

Table 6: Updated CNOP

Table 7: Determining the effect of unexpected changes

If there are misalignments and the entity breaches the retrospective assessment, meaning that it has been over-mitigating its risk, the benchmark derivatives will need to be revised. One way in which this can be achieved is through the creation of additional benchmark derivatives which can represent the misalignment occurring. These will be based on the prevailing benchmark interest rates. 

Therefore, for this example, the entity will construct two additional benchmark derivatives to represent these changes: 

• A 4-year pay fixed rate receive floating IR swap with notional of EUR 300, maturing on the 31^st December 20x5 (BD Swap 3) 
• A 3-year receive fixed pay floating IR swap with notional of EUR 300, maturing on 31^st December 20x4 (BD Swap 4) 

Table 8: Additional Benchmark Derivatives

Step 6: The hedge accounting adjustments are calculated, and the DRM model outputs are required to be booked³: 

• a) The designated derivatives to be measured at fair value in the statement of financial position. 
• b) The DRM adjustments to be recognised in the statement of financial position, as the lower of (in absolute amounts): 
  • The cumulative gain or loss on the designated derivatives from the inception of the DRM model. 
  • The cumulative change in the fair value of the risk mitigation intention attributable to repricing risk from the inception of the DRM model. This would be calculated using the benchmark derivatives (from step 3 and step 5) as a proxy. 
• c) The net gain or loss from the designated derivatives calculated in accordance with (a) and the DRM adjustment calculated in accordance with (b) would be recognised in the statement of profit or loss. 

The table below presents the EUR booking figures for this example. Figures are for the period 20x2 to 20x3. 

The underlying items block represents the interest rate paid/received for the financial asset and financial liability for the period. 

The designated derivative block presents the fair value movement of the designated derivatives for the period and the realised cash flow (net interest rate paid or received) on these instruments (trading income). 

The DRM adjustment block presents the fair value movement of the benchmark derivatives for the period and the realised cash flow on these instruments (trading income). 

BS represents a balance sheet account when IS represents an income statement account.

Table 9: Booking figures

Table 10: Booking figures calculation 

Step 7: The last step is the future capacity assessment which was introduced by the IASB in February 2023 and is still under development so the final implementation of this is still to be released. This step is used to replace the previous retrospective assessment that compared the CNOP sensitivity to the TP. The IASB have yet to release more information on the methodology. The example shown does not assume that the future capacity assessment is carried out. 

What Next?

The IASB plans to publish an exposure draft by 2025 and so companies start thinking about their process for onboarding the DRM model in their accounting process. The DRM model introduces a range of changes to the hedge accounting framework and the transition process will not be an easy switch. Therefore, companies need to ensure that they have a clear and concise implementation plan to ensure a smooth transition. Involvement from stakeholders from across the company such as (IT, Front Office, Risk, Accounting, Treasury) is required to ensure the project is implemented correctly and in time. 

What can Zanders offer?

Transitioning to the new DRM model can be difficult due to the dynamic nature of the model, especially with a more complex balance sheet. Zanders can provide a wide range of expertise to support in the onboarding of the DRM model into your company’s hedging and accounting. We have supported various clients with hedge accounting– including impact analyses, derivative pricing and model validation, and are familiar with the underlying challenges. Zanders can manage the whole project lifecycle from strategizing the implementation, alignment with key stakeholders and then helping design and implement the required models to successfully carry out the hedge accounting at every valuation period. 

For further information, please contact Pierre Wernert, or Alexander Oldroyd.

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. 

In the second instalment of the Zanders series on the DRM model, the Risk Management Strategy (“RMS”) and the DRM process are introduced and with it the new concepts that the IASB have established. The RMS sets out how an entity will manage its interest rate risk, which is the basis of every other part of the DRM model. The IASB has laid out the following expectations for a company’s RMS¹:

1. Process to approve and amend RMS 
2. Risk management levels and scope of assets and liabilities 
3. Risk metrics used 
4. Range of acceptable risk limits (i.e. the target profile) 
5. Risk aggregation method and risk management time horizon 
6. Methodologies to estimate expected cash flows and/or core demand deposits. 

Changes to the RMS that result in a change in the target profile (“TP”), lead to a discontinuation of the hedge². The IASB will further deliberate on when the discontinuation occurs and whether such changes lead to discontinuation of the model at a future date³.

The overall aim of the model is to compare the target profile (“TP”) with the current net open positions (“CNOP”) and thereby produce a risk mitigation intention (“RMI”), which represents the amount of risk that the entity intends to mitigate through the use of designated derivatives. The IASB has tentatively decided that each separate currency should have its own DRM model. 

Below a figure of the DRM process can be found that shows how the different components of the model relate to each other. In the following sections a detailed explanation will be provided for each of these elements.

Figure 1: DRM process

As part of the RMS the entity is required to define the target risk metric. The company cannot change this metric for each period and must stick to the metric specified within the RMS. However, the RMS can specify the use of a different metric over different future time horizons. E.g. the company’s RMS could be to stabilise NII for the first three years on notional exposure and then the present value using PV01 for the following years.  

Current Net Open Position 

The first step in implementing the model is to decide on the assets and liabilities that should be hedged through the DRM framework. The eligible assets and liabilities are currently: 

• Financial assets or liabilities must be measured at amortised cost under IFRS 9 
• Future transactions that result in financial assets or financial liabilities that are classified as subsequently measured at amortised cost under IFRS 9 ($4.2.1). 

Furthermore, the IASB has imposed the following criteria on the eligible assets/liabilities that can be designated in the CNOP⁴, ⁵; an asset/liability is only eligible if all the criteria are met: 

Table 2: Criteria for Assets & Liabilities

An asset/liability is eligible for the CNOP if all the above criteria are met. The IASB has explored other eligible assets/liabilities and have concluded that assets/liabilities that are FVOCI⁷ are recommended to be eligible while the ones that are FVPL⁸ were not recommended to be eligible. Equity was deemed not to be eligible for designation in the CNOP. Since the DRM model is still under review, the eligible assets/liabilities could change before the draft is finalised. Therefore, we advise companies to stay up to date with the latest information. 

Target Profile 

The Target Profile (TP) is linked to a company’s RMS. It sets the risk limits on the CNOP, before risk mitigation actions can be initiated. When the company assesses the risk over different time buckets, it needs to be consistent with the company’s RMS. All of this should be clearly documented within the company’s RMS. The TP should be set at the time when the hedge relationship is designated. The company can also take action to mitigate risks even before the limits are breached. Stakeholders have raised concerns regarding the granularity for the TP. Therefore, the IASB will conduct further research in this area to identify a common principle to be used universally for the allocation of risk limits for the TP.⁹

Risk Mitigation Intention 

The Risk Mitigation Intention (RMI) is a calculated metric based on the company's efforts, through the use of derivatives, to reduce its CNOP for each period to align with the TP outlined in the RMS. Once the RMI is set, it cannot be changed retrospectively. When an entity is deciding on its RMI the following should be considered¹⁰: 

• The RMI cannot exceed the CNOP. When entities monitor their CNOP by time buckets, this must hold for any time bucket 
• The RMI needs to transform the CNOP position to a residual risk position that sits within the TP 
• The RMI needs to be evidenced by real actions taken such as the actual derivates traded in the market 

Stakeholders have been concerned that they may not be able to faithfully mitigate the risk with market traded instruments due to liquidity. E.g. there may be little liquidity for a nine-year interest rate swap to hedge an asset that reprices in nine years in the CNOP. Therefore, the IASB has tentatively stated that an entity could use a 10-year swap for a 9-year hedge. Then in the model the RMI is set to be 0 for the 10^th year and the benchmark derivative matures on the 9^th year. Therefore, the misalignment due to the extra year for the designated derivative would be reported in the profit and loss¹¹.  

Designated Derivatives 

Designated derivatives are the instruments that mitigate interest risk for the company. These are entered into with external counterparties. They are also used to evidence the RMI that a company is taking. The full list of designated derivatives has not been set, it is expected it will contain interest rate swaps (including basis swaps), forward starting swaps and forward rate agreements¹². In Staff Paper 4C – July 2023, the AISB recommended that non-linear derivatives, except for net written options, are eligible as designated derivatives. 

Benchmark Derivatives 

Benchmark derivatives (BD) are based on the same concepts as IFRS 9’s hypothetical derivatives. These are used to measure the efficacy of the hedging. The benchmark derivatives are based on the following specified characteristics¹³: 

1. The benchmark derivative is constructed to be on-market at designation – i.e constructing a “hypothetical” derivative that is nil at zero, where the floating leg replicates the managed risk, and the fixed leg is calibrated to the yield curve. Note that benchmark derivatives are only constructed once and are therefore not reset at every period. 
2. A benchmark derivative cannot be used to include features in the value of the RMI that only exist in the designated derivative (but not the RMI) – This means that features from the designated derivative cannot be used in the benchmark derivative if they don’t exist in the RMI. 
3. The amount of risk and the tenor of the benchmark derivative is prescribed by the RMI and expressed in the risk metric (i.e. KPI) the entity manages at the repricing time period – E.g. if an company is using PV01 as the managed KPI, the amount of risk is measured as the sensitivity of one basis point shift in the managed yield curve. 

Transitioning to the new DRM model can be difficult due to the dynamic nature of the model, especially with a more complex balance sheet. Zanders can provide a wide range of expertise to support in the onboarding of the DRM model into your company’s hedging and accounting. We have successfully supported various clients with hedge accounting– including impact analyses, derivative pricing and model validation, and are familiar with the underlying challenges. Zanders can manage the whole project lifecycle from strategizing the implementation, alignment with key stakeholders and then helping design and implement the required models to successfully carry out the hedge accounting at every valuation period. As the deadline is quickly approaching it would benefit entities to start assessing the key characteristics of the DRM model in order to understand how to change their current framework to the new one.

For further information, please contact Pierre Wernert, or Alexander Oldroyd.

The current standards for hedge accounting present significant challenges for financial institutions engaged in dynamically hedging their portfolios. The corresponding type of hedging accounting, known as “macro fair value hedge accounting”, is covered under IAS 39; however, the regulations fall short as they are unable to accurately reflect an organization’s risk management strategies in its financial reporting. In some instances, companies cannot apply hedge accounting as their hedge is deemed to be ineligible unless they perform some form of proxy hedging strategies. To address these issues, the international Accounting Standards Board (“IASB”) have introduced the Dynamic Risk Management (“DRM”) approach, which is intended to offer a more effective method for entities to apply macro hedging.

The current timeline by the IASB is for a first draft to be released in 2025. This article forms the first in a series of three that will delve into the DRM model, explore its improvements over the current regulations and provide a demonstration of a practical implementation of the current proposal. The insights provided within this series, are Zanders’ understanding drawn from the discussion papers that the IASB has released and so the information is subject to change before the publication of the draft in 2025.

The IASB is aiming for the DRM model to allow readers of the financial statement to gain the following insights: 

• The entity’s interest rate risk management strategy and how it is applied to manage interest rate risk. 
• How the entity’s interest rate risk management activities may affect the amount, timing and uncertainty of future cash flows. 
• The effect of the DRM model on the entity’s financial position and financial performance. 

Within the May 2022 Staff Paper¹, the IASB staff have identified a list of deficiencies of the current IAS 39 and IFRS 9 standards. The main limitations identified were: 

The next two articles in this series will provide a comprehensive exploration of the DRM model and introduce the new concepts that the IASB has proposed. The next article offers a breakdown of the Risk Management Strategy (“RMS”) within the DRM model, how it factors into a company’s overarching strategy for managing their interest rate risk. It will cover the new concepts that the IASB have established. The third and final article in this series will provide an overview of the DRM cycle as well as an example taken from the IASB of how the DRM model would be applied in practice for a singular accounting period. Stay tuned!

What can Zanders offer? 

Transitioning to the new DRM model can be difficult due to the dynamic nature of the model, especially with a more complex balance sheet. Zanders can provide a wide range of expertise to support in the onboarding of the DRM model into your company’s hedging and accounting. We have supported various clients with hedge accounting– including impact analyses, derivative pricing and model validation, and are familiar with the underlying challenges. Zanders can manage the whole project lifecycle from strategizing the implementation, alignment with key stakeholders and then helping design and implement the required models to successfully carry out the hedge accounting at every valuation period.

For further information, please contact Pierre Wernert, or Alexander Oldroyd.

In countries worldwide, associations of credit information providers play a crucial role in registering consumer-related credits. They are mandated by regulation, operate under local law and their primary aim is consumer protection. The Dutch Central Credit Registration Agency, Stichting Bureau Krediet Registratie (BKR), has reviewed the validity of the credit registration period, especially with regards to the recurrence of payment problems after the completion of debt restructuring and counseling. Since 2017, Zanders and BKR are cooperating in quantitative research and modeling projects and they joined forces for this specific research.

In the current Dutch public discourse, diverse opinions regarding the retention period after finishing debt settlements exist and discussions have started to reduce the duration of such registrations. In December 2022, the four biggest municipalities in the Netherlands announced their independent initiative to prematurely remove registrations of debt restructuring and/or counseling from BKR six months after finalization. Secondly, on 21 June 2023, the Minister of Finance of the Netherlands published a proposal for a Credit Registration System Act for consultation, including a proposition to shorten the retention period in the credit register from five to three years. This proposition will also apply to credit registrations that have undergone a debt rescheduling.

The Dutch Central Credit Registration Agency, Stichting Bureau Krediet Registratie (BKR) receives and manages credit registrations and payment arrears of individuals in the Netherlands. By law, a lender in the Netherlands must verify whether an applicant already has an existing loan when applying for a new one. Additionally, lenders are obligated to report every loan granted to a credit registration agency, necessitating a connection with BKR. Besides managing credit data, BKR is dedicated to gathering information to prevent problematic debt situations, prevent fraud, and minimize financial risks associated with credit provision. As a non-profit foundation, BKR operates with a focus on keeping the Dutch credit market transparent and available for all.

BKR recognizes that the matter concerning the retention period of registrations for debt restructuring and counseling is fundamentally of societal nature. Many stakeholders are concerned with the current discussions, including municipalities, lenders and policymakers. To foster public debate on this matter, BKR is committed to conducting an objective investigation using credit registration data and literature sources and has thus engaged Zanders for this purpose. By combining expertise in financial credit risk with data analysis, Zanders offers unbiased insights into this issue. These data-driven insights are valuable for BKR, lawmakers, lenders, and municipalities concerning retention periods, payment issues, and debt settlements.

Problem Statement

The Dutch Central Credit Registration Agency, Stichting Bureau Krediet Registratie (BKR) receives and manages credit registrations and payment arrears of individuals in the Netherlands. By law, a lender in the Netherlands must verify whether an applicant already has an existing loan when applying for a new one. Additionally, lenders are obligated to report every loan granted to a credit registration agency, necessitating a connection with BKR. Besides managing credit data, BKR is dedicated to gathering information to prevent problematic debt situations, prevent fraud, and minimize financial risks associated with credit provision. As a non-profit foundation, BKR operates with a focus on keeping the Dutch credit market transparent and available for all.

The research aims to gain a deeper understanding of the recurrence of payment issues following the completion of restructuring credits (recidivism). The information gathered will aid in shaping thoughts about an appropriate retention period for the registration of finished debt settlements. The research includes both qualitative and quantitative investigations. The qualitative aspect involves a literature study, leading to an overview of benchmarking, key findings and conclusions from prior studies on this subject. The quantitative research comprises data analyses on information from BKR's credit register.

External International Qualitative Research

The literature review encompassed several Dutch and international sources that discuss debt settlements, credit registrations, and recidivism. There is limited research published on recidivism, but there are some actual cases where retention period are materially shortened or credit information is deleted to increase access to financial markets for borrowers. Removing information increases information asymmetry, meaning that borrower and lender do not have the same insights limiting lenders to make well-informed decisions during the credit application process. The cases in which the retention period was shortened or negative credit registrations were removed demonstrate significant consequences for both consumers and lenders. Such actions led to higher default rates, reduced credit availability, and increased credit costs, also for private individuals without any prior payment issues.

In the literature it is described that historical credit information serves as predictive variable for payment issues, emphasizing the added value of credit registrations in credit reports, showing that this mitigates the risk of overindebtedness for both borrowers and lenders.

Quantitative Research with Challenges and Solutions

BKR maintains a large data set with information regarding credits, payment issues, and debt settlements. For this research, data from over 2.5 million individuals spanning over 14 years were analyzed. Transforming this vast amount of data into a usable format to understand the payment and credit behavior of individuals posed a challenge.

The historical credit registration data has been assessed to (i) gain deeper insights into the relationship between the length of retention periods after debt restructuring and counseling and new payment issues and (ii) determine whether a shorter retention period after the resolution of payment issues negatively impacts the prevention of new payment issues, thus contributing to debt prevention to a lesser extent.

The premature removal of individuals from the system of BKR presented an additional challenge. Once a person’s information is removed from the system, their future payment behavior can no longer be studied. Additionally, the group subject to premature removal (e.g. six months to a year) after a debt settlement registration constitutes only a small portion of the population, making research on this group challenging. To overcome these challenges, the methodology was adapted to assess the outflow of individuals over time, such that conclusions about this group could still be made.

Conclusion

The research provided BKR with several interesting conclusions. The data supported the literature that there is difference in risk for payment issues between lenders with and without debt settlement history. Literature shows that reducing the retention period increases the access to the financial markets for those finishing a debt restructuring or counseling. It also increases the risk in the financial system due to the increased information asymmetry between lender and borrower, with several real-life occasions with

increased costs and reduced access to lending for all private individuals. The main observation of the quantitative research is that individuals who have completed a debt rescheduling or debt counseling face a higher risk of relapsing into payment issues compared to those without debt restructuring or counseling history. An outline of the research report is available on the website of BKR.

The collaboration between BKR and Zanders has fostered a synergy between BKR's knowledge, data, and commitment to research and Zanders' business experience and quantitative data analytical skills. The research provides an objective view and quantitative and qualitative insights to come to a well informed decision about the optimal registration period for the credit register. It is up to the stakeholders to discuss and decide on the way forward.