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 

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.