Are you leveraging the SAP Credit Risk Analyzer to its full potential?
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.
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.
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 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:
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:
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.
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.
Are you leveraging the SAP Credit Risk Analyzer to its full potential?
In brief:
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.
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.
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.
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.
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.
Are you leveraging the SAP Credit Risk Analyzer to its full potential?
The final article from Zanders on the DRM model presents the lifecycle of the DRM model over a single hedge accounting period and the prospective and retrospective assessments that are required to be carried out to ensure that the entity is correctly mitigating its interest rate risk for the assets/liabilities designated for the Current Net Open Risk Position (CNOP). The cycle will be illustrated by Scenario 1C taken from Agenda Paper 4A – May 20231. This is a relatively simple example, more complex ones can be found within the staff paper.
Figure 1: DRM Cycle
The first three steps are related to the prospective assessment in the DRM model cycle.
The use of the prospective assessment is to ensure that the model is being used to mitigate interest rate risk and achieve the target profile that is set out in the RMS. The RMS should include the following:
Step 1: The entity decides on the securities to be hedged and calculates the net open risk position (from an outstanding notional perspective) per time bucket.
In the example below the company has floating and fixed exposures. The business in this case has a five-year fixed mortgage starting in 20x2 which is fully funded by a five-year floating rate liability. The focus period is 20x2 (start of the hedge accounting period) to 20x3 (end of the hedge accounting period) and so the first period 20x1 has been removed. The entity manages its entity-level interest rate risk for a 5-year time horizon, based on notional exposure in ∆NII and has decided to set the TP to be +/-EUR 500 in each of the repricing periods. Below we present the total fixed and total floating exposures from the product defined above). The individual breakdown of the fixed and floating is not required as each exposure is hedged as a total. The exposure are positions at year end.
Table 1: CNOP of the Entity with yearly buckets
Step 2: The entity will calculate the RMI based on the designated derivatives. The entity decided to mitigate 80% of the risk through the use of the following derivatives (existing and new). Please note that is a combination of derivatives from all the derivatives available in the books:
This leads to the designated derivatives with the following exposures:
Table: Exposures of the Designated Derivatives
The exposures of the designated derivatives can then be compared to the CNOP as shown below:
Table 2: Exposures of the CNOP and Designated Derivatives
As the entity manages its interest rate risk based on ∆NII, the RMI focuses on the floating exposure.
The prospective test is performed by comparing the CNOP and Designated Derivatives exposures at each time bucket to see whether this moves the residual risk inside the TP (+/- EUR 500) set out within the RMS and not providing an over-hedge position. In this case the residual risk will be 0 (80% of CNOP versus DD exposures) and so the prospective assessments pass for all the time buckets.
Table 3: Prospective test
Step 3: Benchmark derivatives (hypothetical derivatives) are constructed based on the RMI calculated above.
Table 4: Benchmark Derivatives created for the Fixed and Floating Exposures
Retrospective (end of the hedge accounting period)
The following steps are related to the retrospective assessment of the DRM model.
The IASB requires a retrospective assessment, to check that risks have been mitigated, as well as a future capacity assessment for each period2. This is to ensure the company is correctly mitigating its interest rate risk, ensuring the CNOP sits within their TP and to quantify the potential misalignment arising from unexpected changes (during the hedge accounting period).
Step 4: The entity updates the CNOP with the latest ALM information (note that new business is excluded from the updated CNOP).
In this example, the financial asset was repaid fully at the end of 20x5. The revised expectation is that it will be partially repaid per end 20x4 and the rest repaid end 20x5.
Table 5: Updated CNOP
Step 5: The potential misalignment due to unexpected changes is calculated. The new CNOP is compared to the RMI that was set in Step 2. Misalignments can occur due to:
Table 6: Updated CNOP
Table 7: Determining the effect of unexpected changes
If there are misalignments and the entity breaches the retrospective assessment, meaning that it has been over-mitigating its risk, the benchmark derivatives will need to be revised. One way in which this can be achieved is through the creation of additional benchmark derivatives which can represent the misalignment occurring. These will be based on the prevailing benchmark interest rates.
Therefore, for this example, the entity will construct two additional benchmark derivatives to represent these changes:
Table 8: Additional Benchmark Derivatives
Step 6: The hedge accounting adjustments are calculated, and the DRM model outputs are required to be booked3:
The table below presents the EUR booking figures for this example. Figures are for the period 20x2 to 20x3.
The underlying items block represents the interest rate paid/received for the financial asset and financial liability for the period.
The designated derivative block presents the fair value movement of the designated derivatives for the period and the realised cash flow (net interest rate paid or received) on these instruments (trading income).
The DRM adjustment block presents the fair value movement of the benchmark derivatives for the period and the realised cash flow on these instruments (trading income).
BS represents a balance sheet account when IS represents an income statement account.
Table 9: Booking figures
Table 10: Booking figures calculation
Step 7: The last step is the future capacity assessment which was introduced by the IASB in February 2023 and is still under development so the final implementation of this is still to be released. This step is used to replace the previous retrospective assessment that compared the CNOP sensitivity to the TP. The IASB have yet to release more information on the methodology. The example shown does not assume that the future capacity assessment is carried out.
The IASB plans to publish an exposure draft by 2025 and so companies start thinking about their process for onboarding the DRM model in their accounting process. The DRM model introduces a range of changes to the hedge accounting framework and the transition process will not be an easy switch. Therefore, companies need to ensure that they have a clear and concise implementation plan to ensure a smooth transition. Involvement from stakeholders from across the company such as (IT, Front Office, Risk, Accounting, Treasury) is required to ensure the project is implemented correctly and in time.
Transitioning to the new DRM model can be difficult due to the dynamic nature of the model, especially with a more complex balance sheet. Zanders can provide a wide range of expertise to support in the onboarding of the DRM model into your company’s hedging and accounting. We have supported various clients with hedge accounting– including impact analyses, derivative pricing and model validation, and are familiar with the underlying challenges. Zanders can manage the whole project lifecycle from strategizing the implementation, alignment with key stakeholders and then helping design and implement the required models to successfully carry out the hedge accounting at every valuation period.
For further information, please contact Pierre Wernert, or Alexander Oldroyd.
Are you leveraging the SAP Credit Risk Analyzer to its full potential?
The Basel IV reforms published in 2017 will enter into force on January 1, 2025, with a phase-in period of 5 years. These are probably the most important reforms banks will go through after the introduction of Basel II. The reforms introduce changes in many areas. In the area of credit risk, the key elements of the banking package include the revision of the standardized approach (SA), and the introduction of the output floor.
In this article, we will analyse in detail the recent updates made to real estate exposures and their impact on capital requirements and internal processes, with a particular focus on collateral valuation methods.
Lending for house purchases is an important business for banks. More than one-third of bank loans in the EU are collateralised with residential immovable property. The Basel IV reforms introduce a more risk-sensitive framework, featuring a more granular classification system.
The new reforms aim for banks to diminish the advantages gained from using the Internal Ratings-Based (IRB) model. All financial institutions that calculate capital requirements with the IRB approach are now required to concurrently use the standardized approach. Under the Standardized Approach, financial institutions have the option to choose from two methods for assigning risk weights: the whole-loan approach and the split-loan approach.
A significant change introduced by the reforms concerns collateral valuation. Previously, the framework allowed banks to determine the value of their real estate collateral based on either the market value (MV) concept or the mortgage lending value (MLV) concept. The revised framework no longer differentiates between these two concepts and introduces new requirements for valuing real estate for lending purposes by establishing a new definition of value. This aims to mitigate the impact of cyclical effects on the valuation of property securing a loan and to maintain more stable capital requirements for mortgages. Implementing an independent valuation that adheres to prudent and conservative criteria can be challenging and may result in significant and disruptive changes in valuation practices.
To reduce the impact of cyclical effects on the valuation of property securing a loan and to keep capital requirements for mortgages more stable, the regulator has capped the valuation of the property, so that it cannot for any reason be higher than the one at origination, unless modifications to that property unequivocally increase its value. Regulators have high expectations for accounting for environmental and climate risks, which can influence property valuations in two ways. On the one hand, these risks can trigger a decrease in property value. On the other hand, they can enhance value, as modifications that improve a property's energy performance or resilience to physical risks - such as protection and adaptation measures for buildings and housing units - may be considered value-increasing factors.
Based on our experience, we specialize in assisting financial institutions with various aspects of Basel IV reforms, including addressing challenges such as limited data availability, implementing new modelling approaches, and providing guidance on interpreting regulatory requirements.
For further information, please contact Marco Zamboni.
Are you leveraging the SAP Credit Risk Analyzer to its full potential?
In the second instalment of the Zanders series on the DRM model, the Risk Management Strategy (“RMS”) and the DRM process are introduced and with it the new concepts that the IASB have established. The RMS sets out how an entity will manage its interest rate risk, which is the basis of every other part of the DRM model. The IASB has laid out the following expectations for a company’s RMS1:
Changes to the RMS that result in a change in the target profile (“TP”), lead to a discontinuation of the hedge2. The IASB will further deliberate on when the discontinuation occurs and whether such changes lead to discontinuation of the model at a future date3.
The overall aim of the model is to compare the target profile (“TP”) with the current net open positions (“CNOP”) and thereby produce a risk mitigation intention (“RMI”), which represents the amount of risk that the entity intends to mitigate through the use of designated derivatives. The IASB has tentatively decided that each separate currency should have its own DRM model.
Below a figure of the DRM process can be found that shows how the different components of the model relate to each other. In the following sections a detailed explanation will be provided for each of these elements.
Figure 1: DRM process
As part of the RMS the entity is required to define the target risk metric. The company cannot change this metric for each period and must stick to the metric specified within the RMS. However, the RMS can specify the use of a different metric over different future time horizons. E.g. the company’s RMS could be to stabilise NII for the first three years on notional exposure and then the present value using PV01 for the following years.
The first step in implementing the model is to decide on the assets and liabilities that should be hedged through the DRM framework. The eligible assets and liabilities are currently:
Furthermore, the IASB has imposed the following criteria on the eligible assets/liabilities that can be designated in the CNOP4, 5; an asset/liability is only eligible if all the criteria are met:
| No. | Eligibility criteria for the Assets/Liabilities as hedged items |
| 1 | The effect of credit risk does not dominate the changes in expected future cashflows. |
| 2 | Future transactions must be highly probable except in the case of transactions that are the reinvestment or refinancing of existing financial assets/liabilities6. |
| 3 | Items already designated in a hedge accounting relationship are not eligible. |
| 4 | Items must be managed on a portfolio basis for interest rate risk management purposes. |
Table 2: Criteria for Assets & Liabilities
An asset/liability is eligible for the CNOP if all the above criteria are met. The IASB has explored other eligible assets/liabilities and have concluded that assets/liabilities that are FVOCI7 are recommended to be eligible while the ones that are FVPL8 were not recommended to be eligible. Equity was deemed not to be eligible for designation in the CNOP. Since the DRM model is still under review, the eligible assets/liabilities could change before the draft is finalised. Therefore, we advise companies to stay up to date with the latest information.
The Target Profile (TP) is linked to a company’s RMS. It sets the risk limits on the CNOP, before risk mitigation actions can be initiated. When the company assesses the risk over different time buckets, it needs to be consistent with the company’s RMS. All of this should be clearly documented within the company’s RMS. The TP should be set at the time when the hedge relationship is designated. The company can also take action to mitigate risks even before the limits are breached. Stakeholders have raised concerns regarding the granularity for the TP. Therefore, the IASB will conduct further research in this area to identify a common principle to be used universally for the allocation of risk limits for the TP.9
The Risk Mitigation Intention (RMI) is a calculated metric based on the company's efforts, through the use of derivatives, to reduce its CNOP for each period to align with the TP outlined in the RMS. Once the RMI is set, it cannot be changed retrospectively. When an entity is deciding on its RMI the following should be considered10:
Stakeholders have been concerned that they may not be able to faithfully mitigate the risk with market traded instruments due to liquidity. E.g. there may be little liquidity for a nine-year interest rate swap to hedge an asset that reprices in nine years in the CNOP. Therefore, the IASB has tentatively stated that an entity could use a 10-year swap for a 9-year hedge. Then in the model the RMI is set to be 0 for the 10th year and the benchmark derivative matures on the 9th year. Therefore, the misalignment due to the extra year for the designated derivative would be reported in the profit and loss11.
Designated derivatives are the instruments that mitigate interest risk for the company. These are entered into with external counterparties. They are also used to evidence the RMI that a company is taking. The full list of designated derivatives has not been set, it is expected it will contain interest rate swaps (including basis swaps), forward starting swaps and forward rate agreements12. In Staff Paper 4C – July 2023, the AISB recommended that non-linear derivatives, except for net written options, are eligible as designated derivatives.
Benchmark derivatives (BD) are based on the same concepts as IFRS 9’s hypothetical derivatives. These are used to measure the efficacy of the hedging. The benchmark derivatives are based on the following specified characteristics13:
Transitioning to the new DRM model can be difficult due to the dynamic nature of the model, especially with a more complex balance sheet. Zanders can provide a wide range of expertise to support in the onboarding of the DRM model into your company’s hedging and accounting. We have successfully supported various clients with hedge accounting– including impact analyses, derivative pricing and model validation, and are familiar with the underlying challenges. Zanders can manage the whole project lifecycle from strategizing the implementation, alignment with key stakeholders and then helping design and implement the required models to successfully carry out the hedge accounting at every valuation period. As the deadline is quickly approaching it would benefit entities to start assessing the key characteristics of the DRM model in order to understand how to change their current framework to the new one.
For further information, please contact Pierre Wernert, or Alexander Oldroyd.
Are you leveraging the SAP Credit Risk Analyzer to its full potential?
The current standards for hedge accounting present significant challenges for financial institutions engaged in dynamically hedging their portfolios. The corresponding type of hedging accounting, known as “macro fair value hedge accounting”, is covered under IAS 39; however, the regulations fall short as they are unable to accurately reflect an organization’s risk management strategies in its financial reporting. In some instances, companies cannot apply hedge accounting as their hedge is deemed to be ineligible unless they perform some form of proxy hedging strategies. To address these issues, the international Accounting Standards Board (“IASB”) have introduced the Dynamic Risk Management (“DRM”) approach, which is intended to offer a more effective method for entities to apply macro hedging.
The current timeline by the IASB is for a first draft to be released in 2025. This article forms the first in a series of three that will delve into the DRM model, explore its improvements over the current regulations and provide a demonstration of a practical implementation of the current proposal. The insights provided within this series, are Zanders’ understanding drawn from the discussion papers that the IASB has released and so the information is subject to change before the publication of the draft in 2025.
The IASB is aiming for the DRM model to allow readers of the financial statement to gain the following insights:
Within the May 2022 Staff Paper1, the IASB staff have identified a list of deficiencies of the current IAS 39 and IFRS 9 standards. The main limitations identified were:
| Number | Area | Description of limitation |
| 1 | Closed Portfolios | The current regulations are designed for “closed portfolios” and requires the direct linkage of hedged items with a hedge. This causes problems as currently an “open portfolio” would be viewed as a set of multiple “closed portfolios”, each with short periodic lifespans. This leads to challenges, as any “open portfolio” hedge relationships need to be tracked individually and its hedge adjustments amortized accordingly. |
| 2 | Risk Management on a net basis | Generally, entities will manage their exposures to interest rate risk on a net basis. However, currently hedges need to be managed on a gross basis. This means that interest rate risk management can be incorrectly represented to achieve the accounting requirements. |
| 3 | Dual character of net interest rate risk position | The repricing risk of the net interest rate risk position arises from a combination of variable and fixed-rate exposures. The economic mismatch has both fair value and cash flow variability when interest rates change, and entities try to mitigate both aspects economically. However, the current hedge accounting requirements state that the hedging relationship must be designated as either a fair value hedge with the fixed rate item or as a cash flow hedge with the variable item. |
| 4 | Demand deposits | Under the current regulations demand deposits cannot be hedged by banks as, from an accounting perspective, the fair value is constant. Since banks are unable to apply hedge accounting to demand deposits, they cannot accurately portray their risk management within the financial statements. |
The next two articles in this series will provide a comprehensive exploration of the DRM model and introduce the new concepts that the IASB has proposed. The next article offers a breakdown of the Risk Management Strategy (“RMS”) within the DRM model, how it factors into a company’s overarching strategy for managing their interest rate risk. It will cover the new concepts that the IASB have established. The third and final article in this series will provide an overview of the DRM cycle as well as an example taken from the IASB of how the DRM model would be applied in practice for a singular accounting period. Stay tuned!
Transitioning to the new DRM model can be difficult due to the dynamic nature of the model, especially with a more complex balance sheet. Zanders can provide a wide range of expertise to support in the onboarding of the DRM model into your company’s hedging and accounting. We have supported various clients with hedge accounting– including impact analyses, derivative pricing and model validation, and are familiar with the underlying challenges. Zanders can manage the whole project lifecycle from strategizing the implementation, alignment with key stakeholders and then helping design and implement the required models to successfully carry out the hedge accounting at every valuation period.
For further information, please contact Pierre Wernert, or Alexander Oldroyd.
Preventing problematic debt situations or increase access to finance after default recovery?
In countries worldwide, associations of credit information providers play a crucial role in registering consumer-related credits. They are mandated by regulation, operate under local law and their primary aim is consumer protection. The Dutch Central Credit Registration Agency, Stichting Bureau Krediet Registratie (BKR), has reviewed the validity of the credit registration period, especially with regards to the recurrence of payment problems after the completion of debt restructuring and counseling. Since 2017, Zanders and BKR are cooperating in quantitative research and modeling projects and they joined forces for this specific research.
In the current Dutch public discourse, diverse opinions regarding the retention period after finishing debt settlements exist and discussions have started to reduce the duration of such registrations. In December 2022, the four biggest municipalities in the Netherlands announced their independent initiative to prematurely remove registrations of debt restructuring and/or counseling from BKR six months after finalization. Secondly, on 21 June 2023, the Minister of Finance of the Netherlands published a proposal for a Credit Registration System Act for consultation, including a proposition to shorten the retention period in the credit register from five to three years. This proposition will also apply to credit registrations that have undergone a debt rescheduling.
The Dutch Central Credit Registration Agency, Stichting Bureau Krediet Registratie (BKR) receives and manages credit registrations and payment arrears of individuals in the Netherlands. By law, a lender in the Netherlands must verify whether an applicant already has an existing loan when applying for a new one. Additionally, lenders are obligated to report every loan granted to a credit registration agency, necessitating a connection with BKR. Besides managing credit data, BKR is dedicated to gathering information to prevent problematic debt situations, prevent fraud, and minimize financial risks associated with credit provision. As a non-profit foundation, BKR operates with a focus on keeping the Dutch credit market transparent and available for all.
BKR recognizes that the matter concerning the retention period of registrations for debt restructuring and counseling is fundamentally of societal nature. Many stakeholders are concerned with the current discussions, including municipalities, lenders and policymakers. To foster public debate on this matter, BKR is committed to conducting an objective investigation using credit registration data and literature sources and has thus engaged Zanders for this purpose. By combining expertise in financial credit risk with data analysis, Zanders offers unbiased insights into this issue. These data-driven insights are valuable for BKR, lawmakers, lenders, and municipalities concerning retention periods, payment issues, and debt settlements.
The Dutch Central Credit Registration Agency, Stichting Bureau Krediet Registratie (BKR) receives and manages credit registrations and payment arrears of individuals in the Netherlands. By law, a lender in the Netherlands must verify whether an applicant already has an existing loan when applying for a new one. Additionally, lenders are obligated to report every loan granted to a credit registration agency, necessitating a connection with BKR. Besides managing credit data, BKR is dedicated to gathering information to prevent problematic debt situations, prevent fraud, and minimize financial risks associated with credit provision. As a non-profit foundation, BKR operates with a focus on keeping the Dutch credit market transparent and available for all.
The research aims to gain a deeper understanding of the recurrence of payment issues following the completion of restructuring credits (recidivism). The information gathered will aid in shaping thoughts about an appropriate retention period for the registration of finished debt settlements. The research includes both qualitative and quantitative investigations. The qualitative aspect involves a literature study, leading to an overview of benchmarking, key findings and conclusions from prior studies on this subject. The quantitative research comprises data analyses on information from BKR's credit register.
The literature review encompassed several Dutch and international sources that discuss debt settlements, credit registrations, and recidivism. There is limited research published on recidivism, but there are some actual cases where retention period are materially shortened or credit information is deleted to increase access to financial markets for borrowers. Removing information increases information asymmetry, meaning that borrower and lender do not have the same insights limiting lenders to make well-informed decisions during the credit application process. The cases in which the retention period was shortened or negative credit registrations were removed demonstrate significant consequences for both consumers and lenders. Such actions led to higher default rates, reduced credit availability, and increased credit costs, also for private individuals without any prior payment issues.
In the literature it is described that historical credit information serves as predictive variable for payment issues, emphasizing the added value of credit registrations in credit reports, showing that this mitigates the risk of overindebtedness for both borrowers and lenders.
BKR maintains a large data set with information regarding credits, payment issues, and debt settlements. For this research, data from over 2.5 million individuals spanning over 14 years were analyzed. Transforming this vast amount of data into a usable format to understand the payment and credit behavior of individuals posed a challenge.
The historical credit registration data has been assessed to (i) gain deeper insights into the relationship between the length of retention periods after debt restructuring and counseling and new payment issues and (ii) determine whether a shorter retention period after the resolution of payment issues negatively impacts the prevention of new payment issues, thus contributing to debt prevention to a lesser extent.
The premature removal of individuals from the system of BKR presented an additional challenge. Once a person’s information is removed from the system, their future payment behavior can no longer be studied. Additionally, the group subject to premature removal (e.g. six months to a year) after a debt settlement registration constitutes only a small portion of the population, making research on this group challenging. To overcome these challenges, the methodology was adapted to assess the outflow of individuals over time, such that conclusions about this group could still be made.
The research provided BKR with several interesting conclusions. The data supported the literature that there is difference in risk for payment issues between lenders with and without debt settlement history. Literature shows that reducing the retention period increases the access to the financial markets for those finishing a debt restructuring or counseling. It also increases the risk in the financial system due to the increased information asymmetry between lender and borrower, with several real-life occasions with
increased costs and reduced access to lending for all private individuals. The main observation of the quantitative research is that individuals who have completed a debt rescheduling or debt counseling face a higher risk of relapsing into payment issues compared to those without debt restructuring or counseling history. An outline of the research report is available on the website of BKR.
The collaboration between BKR and Zanders has fostered a synergy between BKR's knowledge, data, and commitment to research and Zanders' business experience and quantitative data analytical skills. The research provides an objective view and quantitative and qualitative insights to come to a well informed decision about the optimal registration period for the credit register. It is up to the stakeholders to discuss and decide on the way forward.
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Are you leveraging the SAP Credit Risk Analyzer to its full potential?
Early October, the Basel Committee on Banking Supervision (BCBS) published a report[1] on the 2023 banking turmoil that involved the failure of several US banks as well as Credit Suisse. The report draws lessons for banking regulation and supervision which may ultimately lead to changes in banking regulation as well as supervisory practices. In this article we summarize the main findings of the report[2]. Based on the report’s assessment, the most material consequences for banks, in our view, could be in the following areas:
The BCBS report first describes in some detail the events that led to the failure of each of the following banks in the spring of 2023:
While each failure involved various bank-specific factors, the BCBS report highlights common features (with the relevant banks indicated in brackets).
Of the four failed banks, only Credit Suisse was subject to the LCR requirements of the Basel III Accord, in relation to which the BCBS report includes the following observations:
Although not part of the BCBS report, these observations could lead to modifications to the LCR regulation in the future.
With respect to supervisory practices, the BCBS report identifies various lessons learned and raises a few questions, divided into four main areas:
1. Bank’s business models
2. Bank’s governance and risk management
3.Liquidity supervision
4. Supervisory judgment
In addition, the BCBS report identifies various potential enhancement to the design and implementation of bank regulation in four main areas:
1. Liquidity standards
2. IRRBB
3. Definition of regulatory capital
4. Application of the Basel framework
The BCBS report identifies common shortcomings in bank risk management practices and governance at the four banks that failed during the 2023 banking turmoil and summarizes key take-aways for bank supervision and regulation.
The identified shortcomings in bank risk management include gaps in the management of traditional banking risks (interest rate, liquidity and concentration risks), failure to appreciate the interrelation between individual risks, unsustainable business models driven by short-term incentives at the expense of appropriate risk management, poor risk culture, ineffective senior management and board oversight as well as a failure to adequately respond to supervisory feedback and recommendations.
Key take-aways for effective supervision include enforcing prompt action by banks in response to supervisory findings, actively monitoring and assessing potential implications of structural changes to the banking system, and maintaining effective cross-border supervisory cooperation.
Key lessons for regulatory standards include the importance of full and consistent implementation of Basel standards as well as potential enhancements of the Basel III liquidity standards, the regulatory treatment of interest rate risk in the banking book, the treatment of assets that are accounted for at amortised cost within regulatory capital and the role of additional Tier-1 capital instruments.
The BCBS report is intended as a starting point for discussion among banking regulators and supervisors about possible changes to banking regulation and supervisory practices. For those interested in engaging in discussions related to the insights and recommendations in the BCBS report, please feel free to contact Pieter Klaassen.
[1] Report on the 2023 banking turmoil (bis.org) (accessed on October 19, 2023)
[2] Although recognized as relevant in relation to the banking turmoil, the BCBS report explicitly excludes from its consideration the role and design of deposit guarantee schemes, the effectiveness of resolution arrangements, the use and design of central bank lending facilities and FX swap lines, and public support measures in banking crises.
Are you leveraging the SAP Credit Risk Analyzer to its full potential?
The severely changed market circumstances evidence that, despite all efforts, the impact on the IRRBB framework could not be fully foreseen. The challenge of certain banks to comply with one of the key regulatory metrics defined in the context of IRRBB, the SOT on EVE, illustrates this. Indeed, even if regularities are assumed, there are still several key model choices that turn out to materialize in today’s interest rate environment:
Figure 1: Development of 3M EURIBOR rate and 10Y swap rate (vs. 3M EURIBOR) and the corresponding 'Steepness'
Figure 2: Development of the yield curve over the period December 2021 to March 2023.
Having identified the above issues, the question arises as to what measures banks should consider. Roughly speaking, two categories of actions can be distinguished. The first category encompasses actions that resolve an inadequate reflection of the actual risk. Examples of such actions include:
The second category of actions addresses the actual, i.e., economic, risk position of bank. One could think of the following aspects that contribute to steering the EVE SOT within regulatory thresholds:
Zanders is thought leader in supporting banks with IRRBB-related topics. We enable banks to achieve both regulatory compliance and strategic risk goals, by offering support from strategy to implementation. This includes risk identification, formulating a risk strategy, setting up an IRRBB governance and framework, policy or risk appetite statements. Moreover, we have an extensive track record in IRRBB and behavioral models, hedging strategies, and calculating risk metrics, both from a model development as well as a model validation perspective.
Are you interested in IRRBB related topics? Contact Jaap Karelse, Erik Vijlbrief (Netherlands, Belgium and Nordic countries) or Martijn Wycisk (DACH region) for more information.
CEO Laurens Tijdhof explains the origins and importance of the Zanders group’s purpose.
Our purpose is to deliver financial performance when it counts, to propel organizations, economies, and the world forward.
Recently, we have embarked on a process to align more effectively what we do with the changing needs of our clients in unprecedented times. A central pillar of this exercise was an in-depth dialogue with our clients and business partners around the world. These conversations confirmed that Zanders is trusted to translate our deep financial consultancy knowledge into solutions that answer the biggest and most complex problems faced by the world's most dynamic organizations. Our goal is to help these organizations withstand the current macroeconomic challenges and help them emerge stronger. Our purpose is grounded on the above.
Laurens Tijdhof
Our purpose is a reflection of what we do now, but it's also about what we need to do in the future.
It reflects our ongoing ambition - it's a statement of intent - that we should and will do more to affect positive change for both the shareholders of today and the stakeholders of tomorrow. We don't see that kind of ambition as ambitious; we see it as necessary.
The Zanders’ purpose is about the future. But it's also about where we find ourselves right now - a pandemic, high inflation and rising interest rates. And of course, climate change. At this year's Davos meeting, the latest Disruption Index was released showing how macroeconomic volatility has increased 200% since 2017, compared to just 4% between 2011 and 2016.
So, you have geopolitical volatility and financial uncertainty fused with a shifting landscape of regulation, digitalization, and sustainability. All of this is happening at once, and all of it is happening at speed.
The current macro environment has resulted in cost pressures and the need to discover new sources of value and growth. This requires an agile and adaptive approach. At Zanders, we combine a wealth of expertise with cutting-edge models and technologies to help our clients uncover hidden risks and capitalize on unseen opportunities.
However, it can't be solely about driving performance during stable times. This has limited value these days. It must be about delivering performance despite macroeconomic headwinds.
For over 30 years, through the bears, the bulls, and black swans, organizations have trusted Zanders to deliver financial performance when it matters most. We've earned the trust of CFOs, CROs, corporate treasurers and risk managers by delivering results that matter, whether it's capital structures, profitability, reputation or the environment. Our promise of "performance when it counts" isn't just a catchphrase, but a way to help clients drive their organizations, economies, and the world forward.
Laurens Tijdhof
Navigating the current changing financial environment is easier when you've been through past storms. At Zanders, our global team has experts who have seen multiple economic cycles. For instance, the current inflationary environment echoes the Great Inflation of the 1970s. The last 12 months may also go down in history as another "perfect storm," much like the global financial crisis of 2008. Our organization's ability to help business and government leaders prepare for what's next comes from a deep understanding of past economic events. This is a key aspect of delivering performance when it counts.
The other side of that coin is understanding what's coming over the horizon. Performance when it counts means saying to clients, "Have you considered these topics?" or "Are you prepared to limit the downside or optimize the upside when it comes to the changing payments landscape, AI, Blockchain, or ESG?" Waiting for things to happen is not advisable since they happen to you, rather than to your advantage. Performance when it counts drives us to provide answers when clients need them, even if they didn't know they needed them. This is what our relationships are about. Our expertise may lie in treasury and risk, but our role is that of a financial performance partner to our clients.
Technology plays a critical role in both Treasury and Risk. Real-time Treasury used to be an objective, but it's now an imperative. Global businesses operate around the clock, and even those in a single market have customers who demand a 24/7/365 experience. We help transform our clients to create digitized, data-driven treasury functions that power strategy and value in this real-time global economy.
On the risk management front, technology has a two-fold power to drive performance. We use risk models to mitigate risk effectively, but we also innovate with new applications and technologies. This allows us to repurpose risk models to identify new opportunities within a bank's book of business.
We can also leverage intelligent automation to perform processes at a fraction of the cost, speed, and with zero errors. In today's digital world, this combination of next generation thinking, and technology is a key driver of our ability to deliver performance in new and exciting ways.
Laurens Tijdhof
In closing, our purpose is what drives each of us day in and day out, and it's critical because there has never been more at stake. The volume of data, velocity of change, and market volatility are disrupting business models. Our role is to help clients translate unprecedented change into sustainable value, and our purpose acts as our North Star in this journey.
Moreover, our purpose will shape the future of our business by attracting the best talent from around the world and motivating them to bring their best to work for our clients every day.
Laurens Tijdhof
In a continued effort to ensure we offer our customers the very best in knowledge and skills, Zanders has acquired Fintegral.