Industry surveys show that FRTB may lead to a 60% increase in regulatory market risk capital requirements, placing significant pressure on banks. As regulatory market risk capital requirements rise, it is imperative that banks employ robust techniques to effectively understand and manage the drivers of capital. However, isolating these drivers can be challenging and time-consuming, often relying on inefficient and manual techniques. Capital attribution techniques provide banks with a solution by automating the analysis and understanding of capital drivers, enhancing their efficiency and effectiveness in managing capital requirements.

In this article, we share our insights on capital attribution techniques and use a simulated example to compare the performance of several approaches.

The benefits of capital attribution

FRTB capital calculations require large amounts of data which can be difficult to verify. Banks often use manual processes to find the drivers of the capital, which can be inefficient and inaccurate. Capital attribution provides a quantification of risk drivers, attributing how each sub-portfolio contributes to the total capital charge. The ability to quantify capital to various sub-portfolios is important for several reasons:

An overview of approaches

There are several existing capital attribution approaches that can be used. For banks to select the best approach for their individual circumstances and requirements, the following factors should be considered:

• Full Allocation: The sum of individual capital attributions should equal the total capital requirements,
• Accounts for Diversification: The interactions with other sub-portfolios should be accounted for,
• Intuitive Results: The results should be easy to understand and explain.

In Table 1, we summarize the above factors for the most common attribution methodologies and provide our insights on each methodology.

Table 1: Comparison of common capital attribution methodologies.

Comparison of approaches: A simulated example

To demonstrate the different performance characteristics of each of the allocation methodologies, we present a simulated example using three sub-portfolios and VaR as a capital measure. In this example, although each of the sub-portfolios have the same distribution of P&Ls, they have different correlations:

• Sub-portfolio B has a low positive correlation with A and a low negative correlation with C,
• Sub-portfolios A and C are negatively correlated with each other.

These correlations can be seen in Figure 1, which shows the simulated P&Ls for the three sub-portfolios.

Figure 1: Simulated P&L for the three simulated sub-portfolios: A, B and C.

The capital allocation results are shown below in Figure 2. Each approach produces an estimate for the individual sub-portfolio capital allocations and the sum of the sub-portfolio capitals. The dotted line indicates the total capital requirement for the entire portfolio.

Figure 2: Comparison of capital allocation methodologies for the three simulated sub-portfolios: A, B and C. The total capital requirement for the entire portfolio is given by the dotted line.

Zanders’ verdict

From Figure 2, we see that several results do not show this attribution profile. For the Standalone and Scaled Standalone approaches, the capital is attributed approximately equally between the sub-portfolios. The Marginal and Scaled Marginal approaches include some estimates with negative capital attribution. In some cases, we also see that the estimate for the sum of the capital attributions does not equal the portfolio capital.

The Shapley method is the only method that attributes capital exactly as expected. The Euler method also generates results that are very similar to Shapley, however, it allocates almost identical capital in sub-portfolios A and C.  

In practice, the choice of methodology is dependent on the number of sub-portfolios. For a small number of sub-portfolios (e.g. attribution at the level of business areas) the Shapley method will result with the most intuitive and accurate results. For a large number of sub-portfolios (e.g. attribution at the trade level), the Shapley method may prove to be computationally expensive. As such, for FRTB calculations, we recommend using the Euler method as it is a good compromise between accuracy and cost of computation.

Conclusion

Understanding and implementing effective capital attribution methodologies is crucial for banks, particularly given the increased future capital requirements brought about by FRTB. Implementing a robust capital attribution methodology enhances a bank's overall risk management framework and supports both regulatory compliance and strategic planning. Using our simulated example, we have demonstrated that the Euler method is the most practical approach for FRTB calculations. Banks should anticipate capital attribution issues due to FRTB’s capital increases and develop reliable attribution engines to ensure future financial stability.

For banks looking to anticipate capital attribution issues and potentially mitigate FRTB’s capital increases, Zanders can help develop reliable attribution engines to ensure future financial stability. Please contact Dilbagh Kalsi (Partner) or Robert Pullman (Senior Manager) for more information.

With recent volatility in financial markets, firms need increasingly faster pre-trade and risk calculations to react swiftly to changing markets. Traditional computing methods for these calculations, however, are becoming prohibitively expensive and slow to meet the growing demand. GPU computing has recently garnered significant interest, with advances in the fields of advanced machine learning techniques and generative AI technologies, such as ChatGPT. Financial institutions are now looking at gaining an edge by using GPU computing to accelerate their high-dimensional and time-critical computing challenges. 

The MVA Computing Challenge 

The timely computation of MVA is essential for pre-trade and post-trade modelling of bilateral and cleared trading. Providing an accurate measure of future margin requirements over the lifetime of a trade requires the frequent revaluation of derivatives with a large volume of intensive nested Monte Carlo simulations. These simulations need to span a high-dimensional space of trades, time steps, risk factors and nested scenarios, making the calculation of MVA complex and computationally demanding. This is further complicated by the need for an increasing frequency of intra-day risk calculations, due to recent market volatility, which is pushing the limits of what can be achieved with CPU-based computing.  

An Introduction to GPU Computing 

GPU computing utilizes graphics processing units, which are specifically designed to handle large volumes of parallel calculations. This capability makes them ideal for solving programming challenges that benefit from high levels of parallelization and data throughput. Consequently, GPUs can offer substantial benefits over traditional CPU-based computing, thanks to their architectural differences, as outlined in the table below. 

A comparison of the typical capabilities of enterprise-level hardware for CPUs and GPUs.

It is because of these architectural differences that CPUs and GPUs excel in different areas: 

• CPUs  feature fewer but more powerful cores, optimized for general-purpose computing with complex, branching instructions. They excel in performing serial calculations with high single-core performance. 

• GPUs consist of a large number of less powerful cores and with higher memory bandwidth. This makes them ideal for handling large volumes of parallel calculations with high throughput. 

Solving the MVA Computational Challenge with GPU Computing 

The requirement to calculate large volumes of granular simulations makes GPU computing especially well-suited to solving the MVA computational challenge. The use of GPU computing can lead to significant improvements in performance for not only MVA but a range of problems in finance, where it is not uncommon to see improvements in calculation speed of 10 – 100x. This performance increase can be harnessed in several ways: 

• Speed: The high throughput of GPUs provides results more quickly, providing faster risk calculations and insights for decision-making, which is particularly important for pre-trade calculations. 

• Throughput: GPUs can more quickly and efficiently process large calculation volumes, providing institutions with more peak computing bandwidth, reducing workloads on CPU-grids that can be used for other tasks. 

• Accuracy: With greater parallel processing capabilities, the accuracy of models can be improved by using more sophisticated algorithms, greater granularity and a larger number of simulations. As illustrated below, the difference in the number of Monte Carlo simulations that can be achieved by GPUs in the same time as CPUs can be significant. 

The difference in the number of Monte Carlo paths than can be simulated in the same time between an equivalent enterprise-level CPU and GPU.

Case Study: Our approach to accelerating MVA with GPUs 

To illustrate the impact of GPU computing in a real situation, we present a case study of our work accelerating MVA calculations for a major bank. 

Challenge: A large investment bank was seeking to improve the performance of their pre-trade MVA for more timely calculations. This was challenging as they needed to compute their MVA exposures over long time horizons, with a large number of paths. Even with a sensitivity-based approach, this process took close to 10 minutes using a single-threaded CPU calculation. 

Solution: Zanders analyzed the solution and identified several bottlenecks. We developed and optimized a GPU-accelerated solution to ensure efficient GPU utilization, parallelizing the calculations across scenarios and risk factors.  

Performance: Our GPU implementation improved MVA calculation speed by 51x. Improving calculation time from just under 10 minutes to 10 seconds. This significant increase in speed enabled more timely and frequent assessments and decisions on MVA. 

Our Recommendation: A strategic approach to GPU computing implementations 

There are significant benefits to be achieved with the use GPU computing. However, there are some considerations to ensure an effective use of resources: 

We work with firms to develop bespoke solutions to meet their high-performance computing needs. Zanders can help in all aspects of GPU computing implementation, from initial design to the analysis, development and optimization of your GPU computing implementation. 

Conclusion 

GPU computing offers significant improvements in the speed and efficiency of financial calculations, typically boosting calculation speeds by factors of 10-100x. This enables financial institutions to manage their risk more effectively, including the computationally demanding calculations of MVA. By replacing CPU-based calculations with GPU computing, banks can dramatically improve their capacity to process greater volumes of calculations with higher frequency. As financial markets continue to evolve, GPU computing will play an increasingly vital role in their calculation infrastructure.

To find out more on how GPU computing can enhance your institution's risk management processes, please contact Steven van Haren (Director) or Mark Baber (Senior Manager). 

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.

The implementation update covers observations, recommendations and supervisory tools to enhance the assessment of IRRBB risks for institutions and supervisors.¹ Main topics include non-maturing deposit (NMD) behavioral assumptions, complementary dimensions to the SOT NII, the modeling of commercial margins for NMDs in the SOT NII, as well as hedging strategies.  

Some key highlights and takeaways from the results of sample institutions as per Q4 2023: 

• Large dispersion across behavioral assumptions on NMDs is observed. The significant volume of NMDs as part of EU banks’ balance sheets, differences in behavior between customer / product groups and developments in deposit volume distributions, however, underline the need for more solid and aligned modeling. The EBA hence suggests NMD modeling enhancements and recommends (1) banks to consider various risk factors related to the customer, institution and market profile, as well as (2) a supervisory toolkit to monitor parameters / risk factors. Segmentation and peer benchmarking, (reverse) stress testing as well as (combining) expert judgment and historical data are paramount in this regard. The recommendations spark banks to reevaluate forward looking approaches, as shifting deposit dynamics render calibration solely based on historical data insufficient. Establishing a thorough expert judgment governance including backtesting is vital in this respect. Moreover, assessing and substantiating how a bank’s modeling relates to the market is more important than ever. 

• Next to the NII SOT that serves as a metric to flag outlier institutions from an NII perspective, the EBA proposes additional dimensions to be considered by supervisors. These dimensions, which aim to reflect internal NII metrics, must complement the assessment and enhance the understanding of IRRBB exposures and management. The proposed dimensions include (1) market value changes of fair value instruments, (2) interest rate sensitive fees/commissions & overhead costs, and (3) interest rate related embedded losses and gains. It is important to note that it is not intended to introduce new limits or thresholds associated with these dimensions. 

• Given concerns and dispersion regarding the modeling of commercial margins for NMDs in the NII SOT (38% of sample institutions assumed constant commercial margins versus the remainder not applying constant margins), the EBA now provided additional guidance on the expected approach. They recommend institutions to align the assumptions with those in their internal systems, or apply a constant spread over the risk-free rate when not available. Key considerations include the current spread environment, the context of zero or negative interest rates and lags in pass-through. The EBA’s clarification indicates that banks are allowed to apply a non-constant spread. This serves as an opportunity for banks still applying constant ones, as using non-constant spreads enhances the ability to quantify NII risk under an altering interest rate environment. 

• Hedging practices vary significantly across institutions, although hedging instruments (i.e. interest rate swaps) to manage open IRRBB positions are aligned. Hedging strategies have significantly contributed to meeting regulatory requirements, with all institutions meeting the SOT EVE as per Q4 2023, compared to 42% that would not have complied if hedges were disregarded. For the SOT NII, however, 13% of the sample institutions would have been considered outliers if this regulatory measure had been applied in Q4 2023 (versus 21% when disregarding hedges). This result shows that it is key for banks to find a balance between value and earnings stability, and apply hedging strategies accordingly. As compliance with SOTs must be ensured under all circumstances, stressed client behavior and market dynamics must be accounted for. 

In the upcoming years, the EBA will continue monitoring the impact of the IRRBB regulatory package, focusing on NMD modeling, hedging strategies, and potential scope extensions to commercial margin modeling. It will also assess Pillar 3 disclosure practices and track key regulatory elements such as the 5-year cap on NMD repricing maturity and Credit Spread Risk in the Banking Book (CSRBB)-related aspects. Additionally, the EBA will contribute to the International Accounting Standards Board’s (IASB's) Dynamic Risk Management (DRM) project and evaluate the impact of recalibrated shock scenarios from the Basel Committee. 

The EBA publication triggers banks to take action on the four topics outlined above, as well as on hedge accounting (DRM) in the near future. Zanders has extensive relevant experience, and supported on:  

• Numerous NMD topics, including modeling, validation and benchmarking. Furthermore, we published a series of whitepapers regarding NMD modeling concepts and approaches, deposit rate dynamics, forward looking perspectives and migration dynamics in deposits that is particularly relevant following this EBA publication. 

• Drafting an IRRBB strategy, advising on coupon stripping and developing a hedging strategy, thereby carefully balancing value and NII risks (SOT EVE / NII). 

• Validating a hedge accounting framework, developing a hedge account model and hedge accounting outsourcing. Zanders moreover held a survey on DRM as well as published an extensive series of articles on the DRM project of the IASB and its implications for banks. 

Contact Jaap Karelse, Erik Vijlbrief (Netherlands, Belgium and Nordic countries) or Martijn Wycisk (DACH region) for more information.

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).

Accurately attributing changes in counterparty credit exposures is essential for understanding risk profiles and making informed decisions. However, traditional approaches for exposure attribution often pose significant challenges, including labor-intensive manual processes, calculation uncertainties, and incomplete analyses.  

In this article, we discuss the issues with existing exposure attribution techniques and explore Zanders’ automated approach, which reduces workloads and enhances the accuracy and comprehensiveness of the attribution. 

Our approach to attributing changes in counterparty credit exposures 

The attribution of daily exposure changes in counterparty credit risk often presents challenges that strain the resources of credit risk managers and quantitative analysts. To tackle this issue, Zanders has developed an attribution methodology that efficiently automates the attribution process, improving the efficiency, reactivity and coverage of exposure attribution. 

Challenges in Exposure Attribution 

Credit risk managers monitor the evolution of exposures over time to manage counterparty credit risk exposures against the bank’s risk appetite and limits. This frequently requires rapid analysis to attribute the changes to exposures, which presents several challenges: 

Zanders’ approach: an automated approach to exposure attribution 

Our methodology resolves these problems with an analytics layer that interfaces with the risk engine to accelerate and automate the daily exposure attribution process. The results can also be accessed and explored via an interactive web portal, providing risk managers and senior management with the tools they need to rapidly analyze and understand their risk. 

Key features and benefits of our approach 

Zanders’ approach provides multiple improvements to the exposure attribution process. This reduces the workloads of key risk teams and increases risk coverage without additional overheads. Below, we describe the benefits of each of the main features of our approach. 

Zanders Recommends 

An automated attribution of exposures empowers banks teams to better understand and handle their counterparty credit risk. To make the best use of automated attribution techniques, Zanders recommends that banks: 

• Increase risk scope: The increased efficiency of attribution should be used to provide a more comprehensive and granular coverage of the exposures of counterparties, sectors and regions. 

• Reduce quant utilization: Risk managers should use automated dashboards and analytics to perform their own exposure investigations, reducing the workload of quantitative risk teams. 

• Augment decision making: Risk managers should utilize dashboards and analytics to ensure they make more timely and informed decisions. 

• Proactive monitoring: Automated reports and monitoring should be reviewed regularly to ensure risks are tackled in a proactive manner. 

• Increase information transfer: Dashboards should be made available across teams to ensure that information is shared in a transparent, consistent and more timely manner. 

Conclusion

The effective management of counterparty credit risk is a critical task for banks and financial institutions. However, the traditional approach of manual exposure attribution often results in inefficient processes, calculation uncertainties, and incomplete analyses. Zanders' innovative methodology for automating exposure attribution offers a comprehensive solution to these challenges and provides banks with a robust framework to navigate the complexities of exposure attribution. The approach is highly effective at improving the speed, coverage, and accuracy of exposure attribution, supporting risk managers and senior management to make informed and timely decisions. 

For more information about how Zanders can support you with exposure attribution, please contact Dilbagh Kalsi (Partner) or Mark Baber (Senior Manager).

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.

Below we share the most significant aspects for Credit Risk and related challenges. In the coming weeks we will share separate articles to cover areas related to Market Risk, Net Interest Income & Expenses and Operational Risk. 

The final methodology, along with the requirements introduced by the CRR3 poses significant challenges on the execution of the Credit Risk stress testing. Earlier we provided details on this topic and possible impacts on stress testing results, see our article: “Implications of CRR3 for the 2025 EU-wide stress test” Regarding the EBA 2025 stress test we view the following 5 points as key areas of concern: 

1- The EBA stress test requires different starting points; actual and restated CRR3 figures. This raises requirements in data management, reporting and implementation of related processes.  

2- The EBA stress test requires banks to report both transitional and fully loaded results under CRR3; this requires the execution of additional calculations and implementation of supporting data processes. 

3- The changes in classification of assets require targeted effort on the modelling side, stress test approach and related data structures. 

4- Implementation of the Standardized Approach output floor as part of the stress test logic. 

5- Additional effort is needed to correctly align Pillar 1 and Pillar 2 models, in terms of development, implementation and validation. 

At Zanders, we specialize in risk advisory and our consultants have participated in every single EU wide stress testing exercise, as well as a few others going back to the initial stress tests in 2009 following the Great Financial Crisis. We can support you throughout all key stages of the stress testing exercise across all areas to ensure a successful submission of the final templates. 

Based on the expertise in Stress Testing we have gained over the last 15 years, our clients benefit the most from our services in these areas: 

• Full gap analysis against latest set of requirements 

• Review, design and implementation of data processes & relevant data quality controls 

• Alignment of Pillar 2 models to Pillar 1 (including CCR3 requirements) 

• Design, implementation and execution of stress testing models 

• Full automation of populating EBA templates including reconciliation and data quality checks. 

Contact us for more information about how we can help make this your most successful run yet. Reach out to Martijn de Groot, Partner at Zanders.

Across the whole of Europe, banks apply different techniques to model their IFRS9 Expected Credit Losses on a best estimate basis. The diverse spectrum of modelling techniques raises the question: what can we learn from each other, such that we all can improve our own IFRS 9 frameworks? For this purpose, Zanders hosted a webinar on the topic of IFRS 9 on the 29^th of May 2024. This webinar was in the form of a panel discussion which was led by Martijn de Groot and tried to discuss the differences and similarities by covering four different topics. Each topic was discussed by one  panelist, who were Pieter de Boer (ABN AMRO, Netherlands), Tobia Fasciati (UBS, Switzerland), Dimitar Kiryazov (Santander, UK), and Jakob Lavröd (Handelsbanken, Sweden).

The webinar showed that there are significant differences with regards to current IFRS 9 issues between European banks. An example of this is the lingering effect of the COVID-19 pandemic, which is more prominent in some countries than others. We also saw that each bank is working on developing adaptable and resilient models to handle extreme economic scenarios, but that it remains a work in progress. Furthermore, the panel agreed on the fact that SICR remains a difficult metric to model, and, therefore, no significant changes are to be expected on SICR models.

Covid-19 and data quality

The first topic covered the COVID-19 period and data quality. The poll question revealed widespread issues with managing shifts in their IFRS 9 model resulting from the COVID-19 developments. Pieter highlighted that many banks, especially in the Netherlands, have to deal with distorted data due to (strong) government support measures. He said this resulted in large shifts of macroeconomic variables, but no significant change in the observed default rate. This caused the historical data not to be representative for the current economic environment and thereby distorting the relationship between economic drivers and credit risk. One possible solution is to exclude the COVID-19 period, but this will result in the loss of data. However, including the COVID-19 period has a significant impact on the modelling relations. He also touched on the inclusion of dummy variables, but the exact manner on how to do so remains difficult.

Dimitar echoed these concerns, which are also present in the UK. He proposed using the COVID-19 period as an out-of-sample validation to assess model performance without government interventions. He also talked about the problems with the boundaries of IFRS 9 models. Namely, he questioned whether models remain reliable when data exceeds extreme values. Furthermore, he mentioned it also has implications for stress testing, as COVID-19 is a real life stress scenario, and we might need to think about other modelling techniques, such as regime-switching models.

Jakob found the dummy variable approach interesting and also suggested the Kalman filter or a dummy variable that can change over time. He pointed out that we need to determine whether the long term trend is disturbed or if we can converge back to this trend. He also mentioned the need for a common data pipeline, which can also be used for IRB models. Pieter and Tobia agreed, but stressed that this is difficult since IFRS 9 models include macroeconomic variables and are typically more complex than IRB.

Significant Increase in Credit Risk

The second topic covered the significant increase in credit risk (SICR). Jakob discussed the complexity of assessing SICR and the lack of comprehensive guidance. He stressed the importance of looking at the origination, which could give an indication on the additional risk that can be sustained before deeming a SICR.

Tobia pointed out that it is very difficult to calibrate, and almost impossible to backtest SICR. Dimitar also touched on the subject and mentioned that the SICR remains an accounting concept that has significant implications for the P&L. The UK has very little regulations on this subject, and only requires banks to have sufficient staging criteria. Because of these reasons, he mentioned that he does not see the industry converging anytime soon. He said it is going to take regulators to incentivize banks to do so. Dimitar, Jakob, and Tobia also touched upon collective SICR, but all agreed this is difficult to do in practice.

Post Model Adjustments

The third topic covered post model adjustments (PMAs). The results from the poll question implied that most banks still have PMAs in place for their IFRS 9 provisions. Dimitar responded that the level of PMAs has mostly reverted back to the long term equilibrium in the UK. He stated that regulators are forcing banks to reevaluate PMAs by requiring them to identify the root cause. Next to this, banks are also required to have a strategy in place when these PMAs are reevaluated or retired, and how they should be integrated in the model risk management cycle. Dimitar further argued that before COVID-19, PMAs were solely used to account for idiosyncratic risk, but they stayed around for longer than anticipated. They were also used as a countercyclicality, which is unexpected since IFRS 9 estimations are considered to be procyclical. In the UK, banks are now building PMA frameworks which most likely will evolve over the coming years.

Jakob stressed that we should work with PMAs on a parameter level rather than on ECL level to ensure more precise adjustments. He also mentioned that it is important to look at what comes before the modelling, so the weights of the scenarios. At Handelsbanken, they first look at smaller portfolios with smaller modelling efforts. For the larger portfolios, PMAs tend to play less of a role. Pieter added that PMAs can be used to account for emerging risks, such as climate and environmental risks, that are not yet present in the data. He also stressed that it is difficult to find a balance between auditors, who prefer best estimate provisions, and the regulator, who prefers higher provisions.

Linking IFRS 9 with Stress Testing Models

The final topic links IFRS 9 and stress testing. The poll revealed that most participants use the same models for both. Tobia discussed that at UBS the IFRS 9 model was incorporated into their stress testing framework early on. He pointed out the flexibility when integrating forecasts of ECL in stress testing. Furthermore, he stated that IFRS 9 models could cope with stress given that the main challenge lies in the scenario definition. This is in contrast with others that have been arguing that IFRS 9 models potentially do not work well under stress. Tobia also mentioned that IFRS 9 stress testing and traditional stress testing need to have aligned assumptions before integrating both models in each other.

Jakob agreed and talked about the perfect foresight assumption, which suggests that there is no need for additional scenarios and just puts a weight of 100% on the stressed scenario. He also added that IFRS 9 requires a non-zero ECL, but a highly collateralized portfolio could result in zero ECL. Stress testing can help to obtain a loss somewhere in the portfolio, and gives valuable insights on identifying when you would take a loss. 

Pieter pointed out that IFRS 9 models differ in the number of macroeconomic variables typically used. When you are stress testing variables that are not present in your IFRS 9 model, this could become very complicated. He stressed that the purpose of both models is different, and therefore integrating both can be challenging. Dimitar said that the range of macroeconomic scenarios considered for IFRS 9 is not so far off from regulatory mandated stress scenarios in terms of severity. However, he agreed with Pieter that there are different types of recessions that you can choose to simulate through your IFRS 9 scenarios versus what a regulator has identified as systemic risk for an industry. He said you need to consider whether you are comfortable relying on your impairment models for that specific scenario.

This topic concluded the webinar on differences and similarities across European countries regarding IFRS 9. We would like to thank the panelists for the interesting discussion and insights, and the more than 100 participants for joining this webinar.

Interested to learn more? Contact Kasper Wijshoff, Michiel Harmsen or Polly Wong for questions on IFRS 9.

Whether a corporate operates through a decentralized model, shared service center or even global business services model, identifying which invoices a customer has paid and in some cases, a more basic "who has actually paid me" creates a drag on operational efficiency. Given the increased focus on working capital efficiencies, accelerating cash application will improve DSO (Days Sales Outstanding) which is a key contributor to working capital. As the industry adoption of ISO 20022 XML continues to build momentum, Zanders experts Eliane Eysackers and Mark Sutton provide some valuable insights around why the latest industry adopted version of XML from the 2019 ISO standards maintenance release presents a real opportunity to drive operational and financial efficiencies around the reconciliation domain.   

A quick recap on the current A/R reconciliation challenges

Whilst the objective will always be 100% straight-through reconciliation (STR), the account reconciliation challenges fall into four distinct areas:

1. Data Quality

• Partial payment of invoices.
• Single consolidated payment covering multiple invoices.
• Truncated information during the end to end payment processing.
• Separate remittance information (typically PDF advice via email).

2. In-country Payment Practices and Payment Methods

• Available information supported through the in-country clearing systems.

• Different local clearing systems – not all countries offer a direct debit capability.

• Local market practice around preferred collection methods (for example the Boleto in Brazil).

• ‘Culture’ – some countries are less comfortable with the concept of direct debit collections and want full control to remain with the customer when it comes to making a payment.

3. Statement File Format

• Limitations associated with some statement reporting formats – for example the Swift MT940 has approximately 20 data fields compared to the ISO XML camt.053 bank statement which contains almost 1,600 xml tags.

• Partner bank capability limitations in terms of the supported statement formats and how the actual bank statements are generated. For example, some banks still create a camt.053 statement using the MT940 as the data source. This means the corporates receives an xml MT940!

• Market practice as most companies have historically used the Swift MT940 bank statement for reconciliation purposes, but this legacy Swift MT first mindset is now being challenged with the broader industry migration to ISO 20022 XML messaging.

4. Technology & Operations

• Systems limitations on the corporate side which prevent the ERP or TMS consuming a camt.053 bank statement.

• Limited system level capabilities around auto-matching rules based logic.

• Dependency on limited IT resources and budget pressures for customization.

• No global standardized system landscape and operational processes.

How can ISO20022 XML bank statements help accelerate and elevate reconciliation performance?

At a high level, the benefits of ISO 20022 XML financial statement messaging can be boiled down into the richness of data that can be supported through the ISO 20022 XML messages. You have a very rich data structure, so each data point should have its own unique xml field.  This covers not only more structured information around the actual payment remittance details, but also enhanced data which enables a higher degree of STR, in addition to the opportunity for improved reporting, analysis and importantly, risk management.

Enhanced Data

• Structured remittance information covering invoice numbers, amounts and dates provides the opportunity to automate and accelerate the cash application process, removing the friction around manual reconciliations and reducing exceptions through improved end to end data quality.
• Additionally, the latest camt.053 bank statement includes a series of key references that can be supported from the originator generated end to end reference, to the Swift GPI reference and partner bank reference.
• Richer FX related data covering source and target currencies as well as applied FX rates and associated contract IDs. These values can be mapped into the ERP/TMS system to automatically calculate any realised gain/loss on the transaction which removes the need for manual reconciliation.
• Fees and charges are reported separately, combined a rich and very granular BTC (Bank Transaction Code) code list which allows for automated posting to the correct internal G/L account.
• Enhanced related party information which is essential when dealing with organizations that operate an OBO (on-behalf-of) model. This additional transparency ensures the ultimate party is visible which allows for the acceleration through auto-matching.
• The intraday (camt.052) provides an acceleration of this enhanced data that will enable both the automation and acceleration of reconciliation processes, thereby reducing manual effort. Treasury will witness a reduction in credit risk exposure through the accelerated clearance of payments, allowing the company to release goods from warehouses sooner. This improves customer satisfaction and also optimizes inventory management. Furthermore, the intraday updates will enable efficient management of cash positions and forecasts, leading to better overall liquidity management.

Enhanced Risk Management

• The full structured information will help support a more effective and efficient compliance, risk management and increasing regulatory process. The inclusion of the LEI helps identify the parent entity. Unique transaction IDs enable the auto-matching with the original hedging contract ID in addition to credit facilities (letters of credit/bank guarantees).

In Summary

The ISO 20022 camt.053 bank statement and camt.052 intraday statement provide a clear opportunity to redefine best in class reconciliation processes. Whilst the camt.053.001.02 has been around since 2009, corporate adoption has not matched the scale of the associated pain.001.001.03 payment message. This is down to a combination of bank and system capabilities, but it would also be relevant to point out the above benefits have not materialised due to the heavy use of unstructured data within the camt.053.001.02 message version.

The new camt.053.001.08 statement message contains enhanced structured data options, which when combined with the CGI-MP (Common Global Implementation – Market Practice) Group implementation guidelines, provide a much greater opportunity to accelerate and elevate the reconciliation process. This is linked to the recommended prescriptive approach around a structured data first model from the banking community.

Finally, linked to the current Swift MT-MX migration, there is now agreement that up to 9,000 characters can be provided as payment remittance information. These 9,000 characters must be contained within the structured remittance information block subject to bilateral agreement within the cross border messaging space. Considering the corporate digital transformation agenda – to truly harness the power of artificial intelligence (AI) and machine learning (ML) technology – data – specifically structured data, will be the fuel that powers AI. It’s important to recognize that ISO 20022 XML will be an enabler delivering on the technologies potential to deliver both predictive and prescriptive analytics. This technology will be a real game-changer for corporate treasury not only addressing a number of existing and longstanding pain-points but also redefining what is possible.