In the ongoing efforts to enhance tax transparency for multinational corporations, tax authorities have progressively increased scrutiny on intercompany financial transactions. While the interest rates on intra-group loans have long been a focus of regulatory attention, recent administrative guidelines have shifted the spotlight toward the level of indebtedness of borrowers. For instance, the German Federal Ministry of Finance recently issued new guidelines mandating a debt capacity test for intercompany financial transactions¹.

Although many multinational entities already have compliant solutions in place to determine arm’s length interest rates, the same cannot be said for debt capacity tests. Historically, verifying the level of indebtedness for subsidiaries has relied on complex, manual analyses conducted in Excel spreadsheets. These methods, while tailored, often lack efficiency and scalability. 

Today, we are thrilled to announce the launch of a new addition to our Transfer Pricing Suite: the Debt Capacity module. This innovative tool allows clients to build on their pricing analyses by quickly and accurately testing the debt capacity of borrower entities. Staying true to the essence of our Transfer Pricing Suite, the module is user-friendly yet delivers best-in-class support for tax compliance. 

To streamline your in-house workflow, the standard package includes access to comparable data for a wide variety of borrowers. Within seconds, the application can automatically generate 40 comparable peers based on the borrower’s size, country, and industry through our connection with Dun & Bradstreet. Additionally, users can adjust and amend the list of comparable peers to ensure robust and tailored debt capacity tests for any scenario. 

The debt capacity test leverages a flexible framework of financial ratios, which can be customized on a case-by-case basis. Our financial models dynamically adjust a borrower’s ratios to account for the impact of new loans on the balance sheet. With financial data for comparable entities readily available in the application, users receive feedback on debt capacity tests in under a minute. 

Upon completing the analysis, the application offers the option to generate a comprehensive report, available in Word or PDF formats. This detailed report outlines the methodology and underlying data used in the analysis, serving as an excellent complement to existing pricing reports and providing critical compliance support when it matters most. 

After releasing the initial version of the Debt Capacity module to clients, we will work on continuing to improve our applications. For example, by further supporting the debt capacity test through the inclusion of a dedicated cash flow forecast and an increase in comparable companies. If you’re interested in learning more, we invite you to contact our Transfer Pricing team to schedule a demo or trial the new module within your Zanders Inside environment. 

Zanders Transfer Pricing Solution   

 As tax authorities intensify their scrutiny, it is essential for companies to carefully adhere to the recommendations outlined above. Does this mean additional time and resources are required? Not necessarily. 

Technology provides an opportunity to minimize compliance risks while freeing up valuable time and resources. The Zanders Transfer Pricing Suite is an innovative, cloud-based solution designed to automate the transfer pricing compliance of financial transactions.  

With over seven years of experience and trusted by more than 80 multinational corporations, our platform is the market-leading solution for intra-group loans, guarantees, and cash pool transactions. 

Our clients trust us because we provide:  

• Transparent and high-quality embedded intercompany rating models.  

• A pricing model based on an automated search for comparable transactions.  

• Automatically generated, 40-page OECD-compliant Transfer Pricing reports.  

• Debt capacity analyses to support the quantum of debt.  

• Legal documentation aligned with the Transfer Pricing analysis.  

• Benchmark rates, sovereign spreads, and bond data included in the subscription.  

• Expert support from our Transfer Pricing specialists.  

• Quick and easy onboarding—completed within a day!  

Learn more, and discover the key compliance challenges for intra-group loan transfer pricing in 2025. 

After a prolonged period of stable low (and at points even negative) interest rates, 2022 saw the return of rising rates, prompting Dutch digital bank, Knab, to appoint Zanders to reevaluate and reinforce the bank’s approach to risk.

The evolution of Knab

Founded in 2012 as the first fully digital bank in The Netherlands, Knab offers a suite of online banking products and services to support entrepreneurs both in their business and private needs.

“It's an underserved client group,” says Tom van Zalen, Knab’s Chief Risk Officer. “It's a nice niche as there is a strong need for a bank that really is there for these customers. We want to offer products and services that are really tailored to the specific needs of those entrepreneurs that often don’t fit the standard profile used in the market.”

Over time, the bank’s portfolio has evolved to offer a broad suite of online banking and financial services, including business accounts, mortgages, accounting tools, pensions and insurance. However, it was Knab’s mortgage portfolio that led them to be exposed to heightened interest rate risk. Mortgages with relatively long maturities command a large proportion of Knab’s balance sheet. When interest rates started to rise in 2022, increasing uncertainty in prepayments posed a significant risk to the bank. This emphasized the importance of upgrading their risk models to allow them to quantify the impact of changes in interest rates more accurately.

“With mortgages running for 20 plus years, that brings a certain interest rate risk,” says Tom. “That risk was quite well in control, until in 2022 interest rates started to change a lot. It became clear the risk models we were using needed to evolve and improve to align with the big changes we were observing in the interest rate environment—this was a very big thing we had to solve.”

In addition, in the background at around this time, major changes were happening in the ownership of the bank. This ultimately led to the sale of Knab (as part of Aegon NL) to a.s.r. in October 2022 and then to Bawag in February 2024. Although these transactions were not linked to the project we’re discussing here, they are relevant context as they represent the scale of change the bank was managing throughout this period, which added extra layers of complexity (and urgency) to the project.

A team effort

In 2022, Zanders was appointed by Knab to develop an Interest Rate Risk in the Banking Book (IRRBB) Roadmap that would enable them to navigate the changes in the interest rate environment, ensure regulatory compliance across their product portfolio and generally provide them with more control and clarity over their ALM position.  As a first stage of the project, Zanders worked closely with the Knab team to enhance the measurement of interest rate risk. The next stage of the project was then to develop and implement a new IRRBB strategy to manage and hedge interest rate risk more comprehensively and proactively by optimizing value risk, earnings risk and P&L. 

“The whole model landscape had to be redeveloped and that was a cumbersome and extensive process,” says Tom. “Redevelopment and validation took us seven to eight months. If you compare this to other banks, that sort of execution power is really impressive.”

The swiftness of the execution is the result of the high priority awarded to the project by the bank combined with the expertise of the Zanders team.

Zanders brings a very special combination of experts. Not only are they able to challenge the content and make sure we make the right choices, but they also bring in a market practice view. This combination was critical to the success of the execution of this project.

Tom van Zalen, Knab’s Chief Risk Officer.

Clarity and control

Armed with the new IRRBB infrastructure developed together with Zanders, the bank can now measure and monitor the interest rate risks in their product portfolio (and the impact on their balance sheet) more efficiently and with increased accuracy. This has empowered Knab with more control and clarity on their exposure to interest rate risk, enabling them to put the right measures in place to mitigate and manage risk effectively and compliantly.

“The model upgrade has helped us to reliably measure, monitor and quantify the risks in the balance sheet,” says Tom. “With these new models, the risk that we measure is now a real reflection of the actual risk. This has helped us also to rethink our approach on managing risk.”

The success of the project was qualified by an on-site inspection by the Dutch regulator, De Nederlandsche Bank (DNB), in April 2024. With Zanders supporting them, the Knab team successfully complied with regulatory requirements, and they were also complimented on the quality of their risk organization and management by the on-site inspection team.

Lasting impact

The success of the IRRBB Roadmap and the DNB inspection have really emphasized the extent of changes the project has driven across the bank’s processes. This was more than modelling risk, it was about embedding a more calculated and considered approach to risk management into the workings of the bank.

“It was not just a consultant flying in, doing their work and leaving again, it was really improving the bank,” says Tom. “If we look at where we are now, I really can say that we are in control of the risk, in the sense that we know where it is, we can measure it, we know what we need to do to manage it. And that is, a very nice position to be in.”

For more information on how Zanders can help you enhance your approach to interest rate risk, contact Erik Vijlbrief.

Over the past year, the interest rates on intercompany financial transactions have come under closer examination by tax authorities.  This intensified scrutiny stems from a mix of factors, including evolving regulations, more sophisticated audit procedures, the need from governments to boost revenue, and of course, high-interest-rate environment.  

As a result, these transactions are now being assessed with greater depth and rigor than ever before. Historically, tax authorities focused  on interest rate benchmarks as the primary point of analysis. However, their attention has now widened significantly to cover a range of interrelated considerations.  

Below is a brief overview of the key trends and areas attracting the most scrutiny in today’s landscape, highlighting what multinationals should pay attention to in 2025: 

Arm’s length T&Cs 

In the past years, tax authorities are closely examining the terms and conditions of intra-group debt, scrutinizing the pricing of loans and the effects of increased leverage.  

Accordingly, it is critical to ensure that the loan’s terms and conditions reflect the arm’s length standards and align with the actual economic substance of the transaction. This includes evaluating whether a hypothetical independent borrower, under similar conditions, could and would obtain a comparable loan.  

In addition to establishing an arm’s length interest rate and the appropriate amount of debt (further explained below), it is also necessary to assess whether the other terms and conditions are at arm’s length. This involves considering the main features of the loan—such as currency, maturity, repayment schedule, and callability—and evaluating their impact on the risk profile of both the borrower and the lender, as well as on the arm’s length interest rate. 

In this regard, tax authorities may challenge intra-group loans that do not include a maturity date, have an excessively long maturity (e.g., over 25 years), or lack a repayment schedule, since third-party loans would generally include these provisions.. They might also challenge situations where the actual conduct of the parties does not reflect the terms and conditions outlined in the loan agreement. For example, if the parties apply a different maturity or repayment schedule than the one initially agreed upon—without amending the legal documentation, which often happens in a dynamic intra-group financing environment—this could prompt further scrutiny from tax authorities. 

As a result, it is important for multinational enterprises to carefully consider these terms and conditions before issuing a loan, as they will have a direct impact on the interest rate applied in the transaction. Drafting a comprehensive loan agreement that clearly outlines these terms, aligns with the conditions applied in practice, and is supported by a robust Transfer Pricing analysis is recommended to mitigate the risk of challenges by tax authorities. 

Debt Capacity Analysis 

One of the most important terms and conditions that must meet arm’s length standards is the so-called quantum of debt (i.e. nominal amount of the loan extended). Tax authorities are increasingly scrutinizing whether the amount of intra-group debt is economically justified and supported by a clear business purpose. They also evaluate whether the debt aligns with arm’s length principles and serves a legitimate economic function consistent with the borrower’s overall business strategy. 

A debt capacity analysis is often conducted to determine whether the borrower has the financial capacity to repay the loan and whether an unrelated party would provide a similar amount of financing under comparable conditions. 

While many jurisdictions have long required this type of analysis in practice, Germany has taken a step further by formalizing this requirement under its 2024 Growth Opportunities Act. This was further clarified on December 12, 2024, when the German Federal Ministry of Finance issued administrative principles providing specific guidance on financing relationships under the new Transfer Pricing provisions. According to these principles, the debt capacity test hinges on two key criteria:  

(i) a credible expectation that the debtor can meet its obligations (e.g., interest payments and principal repayments),  

and (ii) a commitment to provide financing for a defined period.  

As a result, multinational enterprises are expected to robustly justify the level of debt assumed by their subsidiaries, particularly for entities operating in Germany. 

Credit Rating Analyses 

Tax authorities are increasing their focus on credit rating analyses. While simplified approaches, such as applying a uniform credit rating across all subsidiaries, were once more widely accepted, current practices favour a more detailed entity by entity evaluation. This involves first assigning a stand-alone credit rating to the individual borrower and then adjusting it to account for any implicit or explicit group support. 

In this context, Swiss tax authorities published last year a Q&A addressing various Transfer Pricing topics. In the section on financial transactions, they emphasized a clear preference for the bottom-up approach described above. This aligns closely with the OECD Transfer Pricing Guidelines and is consistent with the prevailing practices in most jurisdictions. 

In contrast, the administrative principles issued in Germany appear to take a different direction. According to the new rules, the arm’s-length nature of the interest rate for cross-border intercompany financing arrangements must generally be determined based on the group’s credit rating and external financing conditions. However, taxpayers are allowed to demonstrate that an alternative rating better aligns with the arm’s-length principle. 

This new approach diverges not only from the OECD guidelines but also from previous case law established by the German Federal Tax Court. As a result, several questions arise regarding how these rules will be applied in practice by German tax authorities. For instance, it remains unclear whether this approach will constitute a strict obligation or whether flexibility will be granted. Additionally, concerns exist about the burden of proof placed on taxpayers when opting for the bottom-up approach recommended by the OECD Transfer Pricing Guidelines. 

Cash Pool Synergy Distribution 

Tax authorities are increasingly aligning with Chapter X of the OECD Guidelines when evaluating cash pooling arrangements, with particular attention to the distribution of synergies among pool participants. 

According to the OECD Transfer Pricing Guidelines (Section C.2.3.2, paragraph 10.143), synergy benefits should generally be allocated to pool members by determining arm’s length interest rates that reflect each participant’s contributions and positions within the pool (e.g., debit or credit). 

Historically, the focus of tax authorities was primarily on the pricing methodologies— ensuring that both deposit and withdrawal margins were set at arm’s length. However, there is now a growing emphasis on how synergy benefits are distributed among participants. This is especially significant in jurisdictions where participants make substantial contributions to the pool balance. According to the OECD guidelines, these participants should benefit from the synergies generated by the pool through more favourable financing terms.  

To address these requirements and reduce the risk of disputes over cash pool structures, a three-step approach is recommended: 

1- Price the credit and debit positions of the participants. 

2- Calculate the synergy benefits generated within the structure. 

3- Allocate these benefits between the Cash Pool Leader and participants by adjusting the price applied to the participants.  

By following this approach, multinationals can ensure compliance with OECD guidelines and mitigate the likelihood of challenges from tax authorities. 

Zanders Transfer Pricing Solution  

As tax authorities intensify their scrutiny, it is essential for companies to carefully adhere to the recommendations outlined above.  

Does this mean additional time and resources are required? Not necessarily.  

Technology provides an opportunity to minimize compliance risks while freeing up valuable time and resources. The Zanders Transfer Pricing Suite is an innovative, cloud-based solution designed to automate the transfer pricing compliance of financial transactions.  

With over seven years of experience and trusted by more than 80 multinational corporations, our platform is the market-leading solution for intra-group loans, guarantees, and cash pool transactions. 

Our clients trust us because we provide: 

• Transparent and high-quality embedded intercompany rating models. 

• A pricing model based on an automated search for comparable transactions. 

• Automatically generated, 40-page OECD-compliant Transfer Pricing reports. 

• Debt capacity analyses to support the quantum of debt. 

• Legal documentation aligned with the Transfer Pricing analysis. 

• Benchmark rates, sovereign spreads, and bond data included in the subscription. 

• Expert support from our Transfer Pricing specialists. 

• Quick and easy onboarding—completed within a day! 

Introduction: Faster, smarter, and future-proof 

In the fast-paced financial industry , speed and accuracy are paramount. Banks are tasked with the complex calculation of XVAs (‘X-Value Adjustments’) on a daily basis, which often involve computationally expensive Monte Carlo simulations. These calculations, while crucial, can become a bottleneck, slowing down decision-making processes and affecting efficiency. What if there is a faster and smarter way to handle these calculations? In this article, we explore a revolutionary approach that uses neural networks to drastically accelerate XVA calculations, promising significant speed-ups without sacrificing accuracy. 

The traditional approach: Monte Carlo simulations and their limitations 

Traditionally, banks have relied on Monte Carlo simulations to calculate XVAs. These simulations involve numerous complex scenarios, requiring substantial computational power and time. Imagine running simulations endlessly, with every tick of the clock translating to computing expenses. The problem? Time and resources. These calculations must be repeated daily, leading to significant delays and costs, potentially hindering your bank's responsiveness and decision-making agility. 

Despite bringing precision, this traditional method poses challenges. Given that the rates offered by banks do not fluctuate dramatically within days, repeating these extensive simulations seems redundant. This redundancy leads us to seek a solution that can deliver both speed and efficiency, paving the way for innovation. 

A new era: Leveraging Neural Networks for speed and efficiency 

 Enter neural networks—an innovative technology that promises a solution to the Monte Carlo conundrum. By training these networks on Monte Carlo simulations conducted early in the week, such as on a Monday, the model can predict outcomes for the rest of the days. This approach sidesteps the need to perform cumbersome computations daily. 

Here’s how it works: The neural network learns from initial data, absorbing patterns and information that remain relatively constant through the week. This enables it to approximate net present value calculations with astonishing speed and accuracy. A practical example? Our integration of this technique into the Open-source Risk Engine resulted in a remarkable 600% increase in speed when assessing interest rate swap exposure in a stable market. 

Benefits of our solution: Integration and acceleration 

• Seamless Integration: Our solution can be seamlessly integrated with any existing systems, as long as they provide net present value outputs for some simulations. 

• Scalability with GPUs: Neural network calculations can harness the parallel processing power of GPUs, exponentially increasing inference speed. Imagine every inference equating to calculations for numerous trades simultaneously. 

• Feasibility and Reliability: With approximation of net present values being a commonly accepted practice in finance, this approach is both feasible and reliable for banks striving for rapid insights. 

Zanders Recommends: A strategic approach to implementation 

At Zanders, we believe in empowering banks with cutting-edge technology that aligns with their growth ambitions. Here is what we recommend: 

1- Assessment Phase: Evaluate the current computational model and identify areas that can benefit from the implementation of neural networks. 

2- Pilot Programs: Start with small-scale implementations to address specific bottlenecks and measure impact. 

3- Utilize GPUs: Leverage the parallelization capabilities of GPUs not just for neural networks but also for Monte Carlo simulations themselves, if needed. 

4- Continuous Improvement: Regularly update neural network models to ensure accuracy as market conditions evolve. 

Our extensive experience with high-performance computing, particularly the use of GPUs for parallelization, positions us as a trusted partner for banks navigating this transformation journey. 

Expertise spotlight: High-Performance Computing and AI solutions 

 In addition to revolutionizing XVA calculations, Zanders offers robust high-performance computing solutions that maximize the capabilities of GPUs across various applications, including Monte Carlo simulations. Our expertise also extends into AI technologies such as chatbots, where we implement and validate models, ensuring banks remain at the forefront of innovation. 

Conclusion: Embrace the future of banking technology 

 As the financial world evolves, so must the technologies that drive it. By leveraging neural networks, banks can achieve unprecedented speed and efficiency in XVA calculations, providing them with the agility needed to navigate today's dynamic markets. Now is the time to embrace a solution that is not only faster but smarter. At Zanders, we're ready to guide you through this transformation. Get in touch with Steven van Haren to learn how we can elevate your XVA calculations and ensure your bank stays competitive in an ever-changing financial landscape.

In the high-stakes world of Private Equity (PE), where exceptional returns are non-negotiable, value creation strategies have evolved far beyond financial engineering. Today, operational improvements, including in treasury and financial risk management, are required to yield high-quality returns. Among these, FX risk management often flies under the radar but holds significant untapped potential to protect and drive value for portfolio companies (PCs). In this article, we explore the importance of identifying and managing FX risks and suggest various quick wins to unlock value for portfolio companies.

The Untapped Potential of FX Risk Management in Value Creation 

PCs operating across multiple countries frequently lack a cohesive treasury and financial risk management approach. For example, bolt-on acquisitions often lead to fragmented teams, processes, systems and banking structures, while exposure to an increasing number of currencies creates financial risk that often remains invisible to central teams. This complexity is exacerbated by ad hoc and localized FX hedging practices, where PCs may not have access to competitive FX rates from their banking partners or access to a multi-bank FX dealing platform. 

For PE firms, FX risk often represents a hidden drain on EBITDA and cash flow. FX mismanagement can erode margins and impact portfolio company value. Hence the importance of uncovering financial and operational inefficiencies and building streamlined processes to manage FX exposures effectively. Proper FX risk management, which goes beyond hedging by means of financial instruments, not only mitigates financial risk but directly contributes to value creation by reducing cash flow volatility, reducing costs, increasing control, and increasing transparency. 

In this simplified example, a private equity-owned manufacturing firm, focused on expansion into emerging markets, was losing millions annually due to unmanaged foreign exchange (FX) exposures. The culprit? Decentralized treasury processes, idle bank balances in multiple currencies, and hidden FX risks within operational flows. The firm can address and manage these inefficiencies by using FX forward contracts to lock in exchange rates for future transactions and employing centralized treasury technology to monitor and control FX exposures across all operations. By addressing the inefficiencies, the firm reduced financial losses, stabilized its margins, and reinvested savings from FX gains into growth initiatives.

Quick Wins in FX Risk Management 

In your search of value creation, we suggest two potential quick wins to unlock PC value.  

Enhance Exposure Visibility 

Check whether your PCs operate with a clear understanding of their FX exposure landscape. Conducting a quick scan early in the investment lifecycle should identify, amongst others: 

• Where exposures are originated (e.g., revenues, costs, intercompany transactions) and if there are natural hedging possibilities. 

• Idle cash balances or loans in nonfunctional currencies, which create FX volatility. 

• The potential impact of these exposures on financial results through FX risk quantification. 

Private equity sponsors can facilitate the creation of a centralized treasury function that i) establishes a policy and process for FX risk management, ii) implements an FX dealing platform for efficient and competitive FX trading with banks, iii) monitors balances to reduce cash balances in non-functional currencies, and iv) implements netting arrangements to streamline intercompany payments and minimize cross-border transactions. 

Hidden FX Risk Discovery 

Business practices, such as allowing customers to pay in multiple currencies or a pricing agreement based on currency conversions, often lead to hidden FX risks and are a common pain point which is overlooked. For instance, a PC may receive customer payments in USD but agree to link the actual payable amount to the EUR/USD exchange rate, creating an implicit EUR exposure that impacts margins and cash flow.  

To address hidden FX risks, a private equity sponsor can help portfolio companies achieve a quick win by conducting a thorough analysis of their pricing models and operational agreements to identify implicit currency exposures, then implementing (soft) hedging techniques, such as adjusting pricing strategies to match revenue and cost currencies, renegotiating contracts with suppliers and customers to align payment terms, and utilizing natural hedging opportunities like balancing currency inflows and outflows, thereby minimizing net exposure before deciding to resort to financial instruments. 

In summary, as illustrated by the above quick wins, streamlining treasury processes can yield: 

• Hard dollar savings: Reduced FX costs by accessing competitive spreads. 

• Soft dollar savings: Enhanced decision-making through better visibility on exposures and reduced operational complexity. 

Consider this: A PE-owned retail chain expanded into international markets and faced profit erosion due to unmanaged FX risks and fragmented treasury processes. The sponsor conducted a quick scan to map exposures, uncovering mismatched revenue and expense currencies, a scattered landscape of bank accounts with idle balances, and operational inefficiencies. Hidden FX risks, such as supplier pricing tied to EUR/USD rates and uncoordinated customer payment options in multiple currencies, were also identified. Leveraging these insights, the sponsor centralized FX management by consolidating bank accounts, aligning supplier contracts with revenue streams to create natural hedges, and introducing competitive trading for FX transactions. They also established internal multilateral netting to streamline intercompany settlements, reducing FX costs by 20%.  

Measurable Results 

Integrating exposure identification and quantification, hidden risk discovery, and treasury process optimization into a single strategy enables PE firms to achieve more stable margins, cost savings, improved cash flow predictability and liberates capital for reinvestment. Furthermore, a proactive approach to FX risk management provides improved transparency for decision-making and LP reporting and strengthens financial resilience against market volatility. By embedding these robust treasury and financial risk management practices, PE sponsors can unlock hidden potential, ensuring their portfolio companies are not only protected but also positioned for sustainable growth and profitable exits.

Conclusion 

In the dynamic world of private equity, optimizing FX risk management for internationally operating PCs is a crucial strategy for safeguarding and enhancing portfolio value. Reflect on your current FX risk strategies and identify potential areas for improvement. Are there invisible exposures or inefficiencies limiting your portfolio’s growth? Take the initiative today - evaluate your FX risk management practices and make the necessary refinements to unlock substantial value for your portfolio companies. Embrace the opportunity to drive significant improvements in their financial resilience and overall performance. 

If you're interested in delving deeper into the benefits of strategic treasury management for private equity firms, please contact Job Wolters. 

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 

The ECB Banking Supervision has identified deficiencies in effective risk data aggregation and risk reporting (RDARR) as a key vulnerability in its planning of supervisory priorities for the 2023-25 cycle and has developed a comprehensive, targeted supervisory strategy for the upcoming years. ​

Banks are expected to step up their efforts and improve their capabilities in Risk Data Aggregation and Risk Reporting as Risk Data Architectures and supporting IT infrastructures are insufficient for most of the Financial Institutions. Hence, RDARR principles are expected to become more and more important during Internal Model Investigations and OnSite Inspections by the ECB.

In May 2024, the ECB published the Guide on effective risk data aggregation and risk reporting to ensure effective processes are in place to identify, manage, monitor and report the risks the institutions are or might be exposed to. With it, the ECB details its minimum supervisory expectations for a set of priority topics that have been identified as necessary preconditions for effective RDARR.

The ECB identifies seven priority areas, considered important prerequisites for robust governance arrangements and effective processes for identifying, monitoring and reporting tasks. The scope of application of these principles is reporting, Key Internal Models and other important models (e.g., IFRS9):

Relevance of BCBS 239 

RDARR represents the implementation of the principles outlined in BCBS 239, which was published by the Basel Committee on Banking Supervision (BCBS) in 2013. BCBS 239 is essential to maintain regulatory compliance, mitigate risks, and drive data-driven decision-making. Non-compliance can result in significant financial penalties, reputational damage, and increased scrutiny from regulatory bodies. Therefore, BCBS 239 is a crucial framework that enhances financial stability by setting robust standards for risk data aggregation and reporting. Its principles encourage institutions to embrace data-driven practices, ensuring resilience, transparency, and efficiency. While challenges such as legacy infrastructure, data quality, and evolving risks persist, banks can overcome these hurdles through strategic investment in governance, technology, and data-driven culture to build end-to-end data transparency. 

Zanders’ view on supervisory planning 

We believe the following 5 topics of the RDARR principles are of major importance for financial institutions: 

Establishing an effective program to review and address these topics, considering the nature, size, scale and complexity of each financial institution, will facilitate alignment with the ECB’s expectations. 

Zanders experience on RDARR implementation 

Data extends beyond being merely a technical database; it is a fundamental component of an organization’s strategic framework. Data-driven organizations are not defined solely by their technological solutions, but by the data culture across the entire organization. At Zanders, we have assisted clients in developing data strategies aligned with RDARR principles and supported the implementation of future-proof data utilization, including the integration of advanced tools such as AI. 

One critical observation is that organizations must urgently address key questions regarding their data: What governance structures are currently in place? Are roles and responsibilities within this governance framework clearly defined? Is the governance being effectively implemented as planned? What training, guidance, and support do employees require? Are data definitions and requirements consistently aligned across all stakeholders? When undertaking such an extensive program, institutions must carefully consider whether a top-down or bottom-up approach will be most effective. In the case of RDARR, success necessitates a comprehensive, dual-directional approach that fosters change across all levels. 

If you are unsure about your compliance with BCBS 239 and RDARR requirements, contact us today to ensure alignment with best practices.

In today's dynamic economic landscape, optimizing portfolio composition to fortify against challenges such as inflation, slower growth, and geopolitical tensions is ever more paramount. These factors can significantly influence consumer behavior and impact loan performance. Navigating this uncertain environment demands banks adeptly strike a delicate balance between managing credit risk and profitability.

Why does managing your risk reward matter?

Quantitative techniques are an essential tool to effectively optimize your portfolio’s risk reward profile, as this aspect is often based on inefficient approaches.

Existing models and procedures across the credit lifecycle, especially those relating to loan origination and account management, may not be optimized to accommodate current macro-economic challenges.

Figure 1: Credit lifecycle.

Current challenges facing banks

Some of the key challenges banks face when balancing credit risk and profitability include:

Our approach to optimizing your risk reward profile

Our optimization approach consists of a holistic three step diagnosis of your current practices, to support your strategy and encourage alignment across business units and processes.

The initial step of the process involves understanding your current portfolio(s) by using a variety of segmentation methodologies and metrics. The second step implements the necessary changes once your primary target populations have been identified. This may include reassessing your models and strategies across the loan origination and account management processes. Finally, a new state-of-the-art Early Warning System (EWS) can be deployed to identify emerging risks and take pro-active action where necessary.

A closer look at redefining your target populations

With the proliferation of advanced data analytics, banks are now better positioned to identify profitable, low-risk segments. Machine Learning (ML) methodologies such as k-means clustering, neural networks, and Natural Language Processing (NLP) enable effective customer grouping, behavior forecasting, and market sentiment analysis.

Risk-based pricing remains critical for acquisition strategies, assessing segment sensitivity to different pricing strategies, to maximize revenue and reduce credit losses.

Figure 2: In the illustration above, we can visually see the impact on earnings throughout the credit lifecycle driven by redefining the target populations and application of different pricing strategies.

In our simplified example, based on the RAROC metric applied to an unsecured loans portfolio, we take a 2-step approach:

1- Identify target populations by comparing RAROC across different combinations of credit scores and debt-to-income (DTI) ratios. This helps identify the most capital efficient segments to target.

2- Assess the sensitivity of RAROC to different pricing strategies to find the optimal price points to maximize profit  over a select period - in this scenario we use a 5-year time horizon.

Figure 3: The top table showcases the current portfolio mix and performance, while the bottom table illustrates the effects of adjusting the pricing and acquisition strategy. By redefining the target populations and changing the pricing strategy, it is possible to reallocate capital to the most profitable segments whilst maintaining within credit risk appetite. For example, 60% of current lending is towards a mix of low to high RAROC segments, but under the new proposed strategy, 70% of total capital is allocated to the highest RAROC segments.

Uncovering risks and seizing opportunities

The current state of Early Warning Systems

Many organizations rely on regulatory models and standard risk triggers (e.g., no. of customers 30 day past due, NPL ratio etc.) to set their EWS thresholds. Whilst this may be a good starting point, traditional models and tools often miss timely deteriorations and valuable opportunities, as they typically use limited and/or outdated data features.

Target state of Early Warning Systems

Leveraging timely and relevant data, combined with next-generation AI and machine learning techniques, enables early identification of customer deterioration, resulting in prompt intervention and significantly lower impairment costs and NPL ratios.

Furthermore, an effective EWS framework empowers your organization to spot new growth areas, capitalize on cross-selling opportunities, and enhance existing strategies, driving significant benefits to your P&L.

Figure 4: By updating the early warning triggers using new timely data and advanced techniques, detection of customer deterioration can be greatly improved enabling firms to proactively support clients and enhance the firm’s financial position.

Discover the benefits of optimizing your portfolios

Discover the benefits in optimizing your portfolios’ risk-reward profile using our comprehensive approach as we turn today’s challenges into tomorrow’s advantages. Such benefits include:

Conclusion

In today's rapidly evolving market, the need for sophisticated credit risk portfolio management is ever more critical. With our comprehensive approach, banks are empowered to not merely weather economic uncertainties, but to thrive within them by striking the optimal risk-reward balance. Through leveraging advanced data analytics and deploying quantitative tools and models, we help institutions strategically position themselves for sustainable growth, and comply with increasing regulatory demands especially with the advent of Basel IV. Contact us to turn today’s challenges into tomorrow’s opportunities.

For more information on this topic, contact Martijn de Groot (Partner) or Paolo Vareschi (Director).

However, CCR remains an essential element in banking risk management, particularly as it converges with valuation adjustments. These changes reflect growing regulatory expectations, which were further amplified by recent cases such as Archegos. Furthermore, regulatory focus seems to be shifting, particularly in the U.S., away from the Internal Model Method (IMM) and toward standardised approaches. This article provides strategic insights for senior executives navigating the evolving CCR framework and its regulatory landscape.

Evolving trends in CCR and XVA

Counterparty credit risk (CCR) has evolved significantly, with banks now adopting a closely integrated approach with valuation adjustments (XVA) — particularly Credit Valuation Adjustment (CVA), Funding Valuation Adjustment (FVA), and Capital Valuation Adjustment (KVA) — to fully account for risk and costs in trade pricing. This trend towards blending XVA into CCR has been driven by the desire for more accurate pricing and capital decisions that reflect the true risk profile of the underlying instruments/ positions.

In addition, recent years have seen a marked increase in the use of collateral and initial margin as mitigants for CCR. While this approach is essential for managing credit exposures, it simultaneously shifts a portion of the risk profile into contingent market and liquidity risks, which, in turn, introduces requirements for real-time monitoring and enhanced data capabilities to capture both the credit and liquidity dimensions of CCR. Ultimately, this introduces additional risks and modelling challenges with respect to wrong way risk and clearing counterparty risk.

As banks continue to invest in advanced XVA models and supporting technologies, senior executives must ensure that systems are equipped to adapt to these new risk characteristics, as well as to meet growing regulatory scrutiny around collateral management and liquidity resilience.

The Internal Model Method (IMM) vs. SA-CCR

In terms of calculating CCR, approaches based on IMM and SA-CCR provide divergent paths. On one hand, IMM allows banks to tailor models to specific risks, potentially leading to capital efficiencies. SA-CCR, on the other hand, offers a standardised approach that’s straightforward yet conservative. Regulatory trends indicate a shift toward SA-CCR, especially in the U.S., where reliance on IMM is diminishing.

As banks shift towards SA-CCR for Regulatory capital and IMM is used increasingly for internal purposes, senior leaders might need to re-evaluate whether separate calibrations for CVA and IMM are warranted or if CVA data can inform IMM processes as well.

Regulatory focus on CCR: Real-time monitoring, stress testing, and resilience

Real-time monitoring and stress testing are taking centre stage following increased regulatory focus on resilience. Evolving guidelines, such as those from the Bank for International Settlements (BIS), emphasise a need for efficiency and convergence between trading and risk management systems. This means that banks must incorporate real-time risk data and dynamic monitoring to proactively manage CCR exposures and respond to changes in a timely manner.

CVA hedging and regulatory treatment under IMM

CVA hedging aims to mitigate counterparty credit spread volatility, which affects portfolio credit risk. However, current regulations limit offsetting CVA hedges against CCR exposures under IMM. This regulatory separation of capital for CVA and CCR leads to some inefficiencies, as institutions can’t fully leverage hedges to reduce overall exposure.

Ongoing BIS discussions suggest potential reforms for recognising CVA hedges within CCR frameworks, offering a chance for more dynamic risk management. Additionally, banks are exploring CCR capital management through LGD reductions using third-party financial guarantees, potentially allowing for more efficient capital use. For executives, tracking these regulatory developments could reveal opportunities for more comprehensive and capital-efficient approaches to CCR.

Leveraging advanced analytics and data integration for CCR

Emerging technologies in data analytics, artificial intelligence (AI), and scenario analysis are revolutionising CCR. Real-time data analytics provide insights into counterparty exposures but typically come at significant computational costs: high-performance computing can help mitigate this, and, if coupled with AI, enable predictive modelling and early warning systems. For senior leaders, integrating data from risk, finance, and treasury can optimise CCR insights and streamline decision-making, making risk management more responsive and aligned with compliance.

By leveraging advanced analytics, banks can respond proactively to potential CCR threats, particularly in scenarios where early intervention is critical. These technologies equip executives with the tools to not only mitigate CCR but also enhance overall risk and capital management strategies.

Strategic considerations for senior executives: Capital efficiency and resilience

Balancing capital efficiency with resilience requires careful alignment of CCR and XVA frameworks with governance and strategy. To meet both regulatory requirements and competitive pressures, executives should foster collaboration across risk, finance, and treasury functions. This alignment will enhance capital allocation, pricing strategies, and overall governance structures.

For banks facing capital constraints, third-party optimisation can be a viable strategy to manage the demands of SA-CCR. Executives should also consider refining data integration and analytics capabilities to support efficient, resilient risk management that is adaptable to regulatory shifts.

Conclusion

As counterparty credit risk re-emerges as a focal point for financial institutions, its integration with XVA, and the shifting emphasis from IMM to SA-CCR, underscore the need for proactive CCR management. For senior risk executives, adapting to this complex landscape requires striking a balance between resilience and efficiency. Embracing real-time monitoring, advanced analytics, and strategic cross-functional collaboration is crucial to building CCR frameworks that withstand regulatory scrutiny and position banks competitively.

In a financial landscape that is increasingly interconnected and volatile, an agile and resilient approach to CCR will serve as a foundation for long-term stability. At Zanders, we have significant experience implementing advanced analytics for CCR. By investing in robust CCR frameworks and staying attuned to evolving regulatory expectations, senior executives can prepare their institutions for the future of CCR and beyond thereby avoiding being left behind.

Within the field of financial risk management, professionals strive to develop models to tackle the complexities in the financial domain. However, due to the ever-changing nature of financial variables, models only capture reality to a certain extent. Therefore, model risk - the potential loss a business could suffer due to an inaccurate model or incorrect use of a model - is a pressing concern. This article explores model risk in prepayment models, analyzing various approaches to quantify and forecast this risk. 

There are numerous examples where model risk has not been properly accounted for, resulting in significant losses. For example, Long-Term Capital Management was a hedge fund that went bankrupt in the late 1990s because its model was never stress-tested for extreme market conditions. Similarly, in 2012, JP Morgan experienced a $6 billion loss and $920 million in fines due to flaws in its new value-at-risk model known as the ‘London Whale Trade’.  

Despite these prominent failures, and the requirements of CRD IV Article 85 for institutions to develop policies and processes for managing model risk,¹ the quantification and forecasting of model risk has not been extensively covered in academic literature. This leaves a significant gap in the general understanding and ability to manage this risk. Adequate model risk management allows for optimized capital allocation, reduced risk-related losses, and a strengthened risk culture.  

This article delves into model risk in prepayment models, examining different methods to quantify and predict this risk. The objective is to compare different approaches, highlighting their strengths and weaknesses.  

Definition of Model Risk

Generally, model risk can be assessed using a bottom-up approach by analyzing individual model components, assumptions, and inputs for errors, or by using a top-down approach by evaluating the overall impact of model inaccuracies on broader financial outcomes. In the context of prepayments, this article adopts a bottom-up approach by using model error as a proxy for model risk, allowing for a quantifiable measure of this risk. Model error is the difference between the modelled prepayment rate and the actual prepayment rate. Model error occurs at an individual level when a prepayment model predicts a prepayment that does not happen, and vice versa. However, banks are more interested in model error at the portfolio level. A statistic often used by banks is the Single Monthly Mortality (SMM). The SMM is the monthly percentage of prepayments and can be calculated by dividing the amount of prepayments for a given month by the total amount of mortgages outstanding. 

Using the SMM, we can define and calculate the model error as the difference between the predicted SMM and the actual SMM: 

The European Banking Authority (EBA) requires financial institutions when calculating valuation model risk to set aside enough funds to be 90% confident that they can exit a position at the time of the assessment. Consequently, banks are concerned with the top 5% and lowest 5% of the model risk distribution (EBA, 2016, 2015). ² Thus, banks are interested in the distribution of the model error as defined above, aiming to ensure they allocate the capital optimally for model risk in prepayment models.  

Approaches to Forecasting Model Risk 

By using model error as a proxy for model risk, we can leverage historical model errors to forecast future errors through time-series modelling. In this article, we explore three methods: the simple approach, the auto-regressive approach, and the machine learning challenger model approach.

Simple Approach

The first method proposed to forecast the expected value, and the variance of the model errors is the simple approach. It is the most straightforward way to quantify and predict model risk by analyzing the mean and standard deviation of the model errors. The model itself causes minimal uncertainty, as there are just two parameters which have to be estimated, namely the intercept and the standard deviation.

The disadvantage of the simple approach is that it is time-invariant. Consequently, even in extreme conditions, the expected value and the variance of model errors remain constant over time.

Auto-Regressive Approach

The second approach to forecast the model errors of a prepayment model is the auto-regressive approach. Specifically, this approach utilizes an AR(1) model, which forecasts the model errors by leveraging their lagged values. The advantage of the auto-regressive approach is that it takes into account the dynamics of the historical model errors when forecasting them, making it more advanced than the simple approach.

The disadvantage of the auto-regressive approach is that it always lags and that it does not take into account the current status of the economy. For example, an increase in the interest rate by 200 basis points is expected to lead to a higher model error, while the auto-regressive approach is likely to forecast this increase in model error one month later.

Machine Learning Challenger Model Approach                           

The third approach to forecast the model errors involves incorporating a Machine Learning (ML) challenger model. In this article, we use an Artificial Neural Network (ANN). This ML challenger model can be more sophisticated than the production model, as its primary focus is on predictive accuracy rather than interpretability. This approach uses risk measures to compare the production model with a more advanced challenger model. A new variable is defined as the difference between the production model and the challenger model.

Similar to the above approaches, the expected value of the model errors is forecasted by estimating the intercept, the parameter of the new variable, and the standard deviation. A forecast can be made and the difference between the production model and ML challenger model can be used as a proxy for future model risk.

The advantage of using the ML challenger model approach is that it is forward looking. This forward-looking method allows for reasonable estimates under both normal and extreme conditions, making it a reliable proxy for future model risk. In addition, when there are complex non-linear relationships between an independent variable and the prepayment rate, an ML challenger can be more accurate. Its complexity allows it to predict significant impacts better than a simpler, more interpretable production model. Consequently, employing an ML challenger model approach could effectively estimate model risk during substantial market changes.

A disadvantage of the machine learning approach is its complexity and lack of interpretability. Additionally, developing and maintaining these models often requires significant time, computational resources, and specialized expertise.

Conclusion 

The various methods to estimate model risk are compared in a simulation study. The ML challenger model approach stands out as the most effective method for predicting model errors, offering increased accuracy in both normal and extreme conditions. Both the simple and challenger model approach effectively predicts the variability of model errors, but the challenger model approach achieves a smaller standard deviation. In scenarios involving extreme interest rate changes, only the challenger model approach delivers reasonable estimates, highlighting its robustness. Therefore, the challenger model approach is the preferred choice for predicting model error under both normal and extreme conditions.

Ultimately, the optimal approach should align with the bank’s risk appetite, operational capabilities, and overall risk management framework. Zanders, with its extensive expertise in financial risk management, including multiple high-profile projects related to prepayments at G-SIBs as well as mid-size banks, can provide comprehensive support in navigating these challenges. See our expertise here.

Ready to take your IRRBB strategy to the next level?

Zanders is an expert on IRRBB-related topics. We enable banks to achieve both regulatory compliance and strategic risk goals by offering support from strategy to implementation. This includes risk identification, formulating a risk strategy, setting up an IRRBB governance and framework, and policy or risk appetite statements. Moreover, we have an extensive track record in IRRBB [EU1] and behavioral models such as prepayment models, hedging strategies, and calculating risk metrics, both from model development and model validation perspectives.

Contact our experts today to discover how Zanders can help you transform risk management into a competitive advantage. Reach out to: Jaap Karelse, Erik Vijlbrief, Petra van Meel, or Martijn Wycisk to start your journey toward financial resilience.