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Introduction Six-step guide How are financial transactions priced Common pitfalls Operating model

Transfer Pricing for financial transactions is no longer limited to finding an interest rate and adding it to an intercompany loan agreement.

In addition to supporting evidence for the final pricing, tax authorities are now examining the broader terms of the transaction itself, the credit rating assessment of the borrower (including consideration of any group support), the borrower's capacity to support the debt, and the allocation of financial risks.

For multinational groups, that creates a practical challenge. Treasury, Tax, and Finance need one approach that is economically sound, operationally workable, and defensible across jurisdictions.

This guide explains how to approach Transfer Pricing for financial transactions, including intercompany loans, cash pools, guarantees, and in-house banking arrangements.

What is Transfer Pricing for financial transactions?

Transfer Pricing for financial transactions determines whether financing arrangements between related entities reflect the terms that independent parties would have agreed under comparable circumstances.

The analysis can cover:

• Intercompany loans

• Cash pools

• Financial guarantees

• In-house bank services

• Debt and equity funding decisions

• Leases and other related-party financial instruments

The OECD Transfer Pricing Guidelines now formally address the Transfer Pricing aspects of such financial arrangements in Chapter X. Read the OECD guidance on financial transactions.

The central question is straightforward:

Would independent parties have entered into this transaction, with these terms, at this price?

Answering it requires more than a benchmark rate. It requires a clear view of the actual transaction and the commercial reality behind it.

Why financial transactions require a distinct approach

Financial transactions have characteristics that make them different from many other Transfer Pricing arrangements.

A loan, for example, creates a financing relationship between a lender and a borrower. The analysis must consider the borrower's ability to service the debt, the lender's risks, the amount of debt that could reasonably be raised, and the terms that would apply in the market.

A cash pool creates a different set of questions. The group may generate a financial benefit by centralizing liquidity, but that benefit needs to be identified and allocated consistently between participants.

A guarantee raises another issue. The guarantor may improve the borrower's financing terms, but the benefit is not necessarily equal to the full difference between the borrower's standalone and guaranteed borrowing costs.

These arrangements cannot be supported by a generic policy statement alone. The analysis needs to connect the facts, the economic rationale, the legal form of the arrangement, and the pricing itself.

The six-step financial-transactions analysis

1. Accurately delineate the transaction

Start with what the parties actually agreed and how they behave in practice. Does the legal form match the substance?

Review the economically relevant characteristics of the arrangement, including:

• Purpose of the financing

• Amount and currency

• Maturity and repayment profile

• Fixed or floating interest rate

• Security and collateral

• Seniority and ranking

• Covenants and other contractual terms

• Expected source of repayment

• Relationship between the lender and borrower

• Alternatives realistically available to each party

This step can reveal that the economic reality of the transaction does not align with the legal agreement. A balance described as a loan may behave like equity. A recurring current-account balance may operate like a longer-term financing arrangement. A guarantee may provide a benefit that differs from the parties' original assumption.

2. Assess debt capacity

Before pricing the debt, determine whether the borrower could reasonably support the amount of debt in the first place.

Debt capacity analysis asks questions such as:

• How much external debt could the borrower have raised?

• Would an independent lender have provided the full amount?

• What cash flows are available to service the debt?

• How does the proposed leverage compare with peers?

• Are the borrower's projected results sufficient to support repayment?

• Does the arrangement contain features more consistent with equity than debt?

An interest rate benchmark cannot correct an unrealistic debt amount. If the amount of debt is not arm's length, pricing the interest rate alone leaves a material part of the analysis unresolved.

Zanders' Transfer Pricing Solutions includes debt-capacity analysis based on peer analysis and cash-flow perspectives, alongside rating models and financial-transaction pricing tools.

Debt Capacity Analysis

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3. Determine the borrower's creditworthiness

The borrower's creditworthiness is one of the most important inputs into an intercompany loan analysis.

Depending on the facts, the analysis may consider:

• A standalone credit rating for the borrower

• The group's credit profile

• The effect of implicit group support

• The borrower's strategic importance to the group

• Country and industry risk

• Financial ratios and cash-flow forecasts

• Default risk and recovery assumptions

• The presence of guarantees or collateral

The appropriate approach depends on the transaction and the group's Transfer Pricing policy. The key requirement is consistency. The rating methodology should be explainable, repeatable, and aligned with the evidence available to the borrower and the group.

4. Select the most appropriate pricing method

The most appropriate method depends on the transaction, the available data, and the reliability of the result.

For intercompany loans, approaches may include:

• Comparable Uncontrolled Price analysis

• Yield-curve or credit-spread analysis

• Internal comparable transactions

• Cost-of-funds approaches

• Other methods where market data is limited or the transaction has unusual characteristics

The method-selection process should not simply state that one method was chosen. It should explain why the selected method is more reliable than the alternatives.

Chapter II of the OECD Transfer Pricing Guidelines details the need to select the most appropriate transfer pricing method, taking into account the strengths and weaknesses of each method in assessing the controlled transaction. The IRS documentation guidance also emphasizes the importance of supporting the selection and application of the method, considering relevant data, and explaining why alternative methods were rejected.

5. Benchmark the interest rate and other terms

Once the transaction has been delineated and the borrower's creditworthiness assessed, benchmark the interest rate pricing and terms.

Relevant factors may include:

• Currency

• Tenor

• Interest-rate type

• Credit rating

• Seniority

• Security

• Repayment profile

• Market conditions at the relevant date

• Comparable borrower and lender characteristics

A credible benchmarking exercise should make clear how the selected comparables relate to the actual transaction. It should also explain any adjustments made for differences in currency, maturity, rating, seniority, or other economically relevant factors.

The objective is not to find a number that fits a predetermined outcome. It is to establish a result that can be explained and defended.

6. Document, monitor, and update the analysis

Documentation should show how the conclusion was reached, not simply record the final interest rate.

A strong file typically connects:

• The group's Transfer Pricing policy

• The legal agreements

• The functional and risk analysis

• Debt-capacity conclusions

• Credit-rating methodology

• Comparable searches

• Pricing calculations

• Key assumptions

• Data sources

• Approval and governance steps

• Any changes made after the original analysis

The OECD Transfer Pricing Guidelines describe a general approach to Transfer Pricing Documentation in Chapter V. The IRS also explains that robust documentation can help taxpayers demonstrate the reasonableness of their method and make audits or reviews more efficient. Documentation also needs to remain aligned with the transactions that are actually taking place.

That is where many manual processes break down. The report may be correct when it is written, but the underlying loans, balances, rates, and agreements change over time. A sustainable process needs to connect pricing, administration, and documentation rather than treating them as separate exercises.

How are common financial transactions priced?

Intercompany loans

An intercompany loan analysis typically addresses:

1. Whether the arrangement should be treated as debt.

2. Whether the amount of debt is supportable.

3. What credit rating applies to the borrower.

4. What interest rate and terms independent parties would have agreed.

5. How the result should be documented and monitored.

Interest-rate benchmarking is important, but it is only one part of the analysis.

Cash pools

Cash pooling centralizes liquidity across multiple entities. The Transfer Pricing analysis needs to consider:

• The functions performed by the cash-pool leader

• The risks assumed by each participant

• The benefits created by centralization

• The appropriate allocation of those benefits

• The treatment of debit and credit positions

• The pricing of deposits, borrowings, and other balances

A cash pool should not be treated as a simple collection of bilateral loans if the economic arrangement is more complex.

Zanders' in-house bank and payments expertise combines Treasury operations with transparent, OECD-compliant administration of group financial transactions.

Financial guarantees

A guarantee may improve the borrower's access to financing or reduce the cost of external debt. The analysis should identify the benefit created and determine how that benefit should be priced.

Relevant questions include:

• What would the borrower's financing terms be without the guarantee?

• What does the guarantee change?

• What risks does the guarantor assume?

• Would an independent borrower have purchased a guarantee?

• How should the benefit be allocated between the parties?

The answer depends on the facts. A guarantee fee should not be determined using a generic percentage without analyzing the underlying transaction.

In-house banks

An in-house bank may provide centralized financing, payment, liquidity, and risk-management services to group entities.

The Transfer Pricing analysis should reflect the actual functions and risks of the in-house bank, including whether it acts as an intermediary, assumes financial risk, or provides a broader Treasury service to the group.

The operating model and the Transfer Pricing policy need to work together. If the policy describes one set of responsibilities but the in-house bank operates differently, the documentation becomes harder to defend.

Intra-Group Loans Transfer Pricing: H2 2026

The latest case law and regulatory developments you need to know to finish the year strong

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Common pitfalls in the Transfer Pricing for financial transactions

Focusing only on the interest rate

A rate benchmark does not resolve debt capacity, debt-versus-equity, contractual terms, or the allocation of cash-pool benefits (if relevant).

Treating agreements as evidence of arm's-length behavior

An agreement is an important source of evidence, but it does not replace analysis of what the parties actually do. The delineation of a transaction is a critical step in any transfer pricing analysis.

Applying a rating methodology inconsistently

Different entities may end up with different approaches to standalone ratings, group support, or implicit support. That creates avoidable governance and audit risk.

Using comparables without explaining their limitations

No comparable is perfect. The analysis should explain the relevant differences, how they affect the result, and whether adjustments are appropriate.

Producing documentation after the fact

If the analysis is recreated months later from incomplete data, it becomes more difficult to demonstrate how the decision was made at the time of the transaction.

Separating Tax and Treasury workflows

Tax may own the report while Treasury owns the loans, rates, and balances. Without a shared process, the documentation can drift away from operational reality.

Can the Transfer Pricing for financial transactions be automated?

Yes, but automation should support judgment rather than replace it.

A well-designed process can automate repetitive activities such as:

• Collecting transaction data

• Applying approved pricing policies

• Searching for relevant comparables

• Calculating rates and spreads

• Applying rating models

• Recording assumptions

• Generating reports

• Maintaining audit trails

• Monitoring portfolios across entities and jurisdictions

The value of automation comes from connecting and streamlining these steps in a single workflow. A tool that produces a report but does not reflect the actual loan portfolio leaves a significant gap. A tool that connects pricing, data, administration, and documentation can reduce manual work while improving consistency.

Zanders' intercompany loan Transfer Pricing software is designed for financial transactions and supports loan, cash pool, and guarantee pricing, credit rating analyses, comparable searches, and OECD Chapter X compliant reporting.

A practical operating model for multinational groups

The most robust transfer pricing approach is usually built around a shared framework between Tax, Treasury, and Finance.

Tax should define:

• Regulatory requirements

• Documentation standards

• Jurisdictional differences

• Audit and dispute considerations

• The governance model

Treasury should define:

• How financing is structured

• How loans and cash pools operate

• The relevant market and credit information

• How transactions change over time

• The operational requirements of the Treasury Management System

Finance should provide:

• Reliable entity and group financial data

• Cash-flow forecasts

• Balance-sheet information

• Accounting treatment

• Performance monitoring

The process is strongest when the three functions use one set of assumptions and one controlled source of data.

Make financial transactions defensible by design

The Transfer Pricing for financial transactions is not a single calculation. It is a connected process that starts with the economic reality of the transaction and ends with documentation that reflects what the group actually does.

The most defensible approach brings together debt capacity, creditworthiness, interest rate benchmarking, contractual terms, policy, administration, and ongoing monitoring.

For multinational groups, the strategic question is no longer whether these activities should be connected. It is how long the organization can afford to manage them through disconnected spreadsheets, manual searches, and separate Tax and Treasury workflows.

Explore Zanders' Transfer Pricing Solutions or get in touch to discuss how to strengthen your financial transactions process.

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Frequently asked questions

What is OECD Chapter X?

OECD Chapter X provides guidance on the Transfer Pricing aspects of financial transactions, including loans, cash pools, and financial guarantees. It helps groups and tax authorities apply the arm’s length principle more consistently to financial arrangements.

How do you price an intercompany loan?

First delineate the transaction, assess debt capacity, determine the borrower’s creditworthiness, select the most appropriate method, benchmark the interest rate and terms, and document the analysis. Pricing the interest rate without assessing the underlying debt may leave the analysis incomplete.Transfer Pricing for financial transactions is no longer limited to finding an interest rate and adding it to an intercompany loan agreement. In addition to supporting evidence for the final pricing, tax authorities are now examining the broader terms of the transaction itself, the credit rating assessment of the borrower (including consideration of any group support), the borrower’s capacity to support the debt, and the allocation of financial risks./

What credit rating should be used for an intercompany loan?

The answer depends on the borrower, the group, the transaction, and the support available. The analysis may consider a standalone rating, group support, implicit support, or a combination of factors. The methodology should be consistent and supported by evidence.

How often should intercompany loan pricing be reviewed?

The review cycle should reflect the group’s policy, the transaction terms, market changes, and material changes in the borrower’s creditworthiness or financial position. A loan portfolio should not be treated as static when the underlying risks and market conditions are changing.

Is Transfer Pricing software only for large groups?

Software provides value when a group manages multiple entities, jurisdictions, transactions, or complex financing structures. However, the use of effective software also provides value to smaller portfolios when manual processes create a disproportionate documentation burden or when the group needs stronger consistency and audit trails. Ultimately, software reduces the manual strain on teams and optimizes workflows.

Recent case law and regulatory developments provide useful guidance on what to expect in 2026 and on the approach that should be followed to mitigate the risk of challenges on intra-group loans.

Historically, tax authorities focused primarily on interest rate benchmarks when reviewing intra-group loans, cash pools and guarantees. Today, however, their analysis has become significantly broader and more sophisticated, extending to a range of interrelated factors such as contractual terms, debt capacity, and creditworthiness.

The sections below outline the key trends and risks shaping intra-group loan transfer pricing and highlight what multinational groups should address as part of their planning and compliance efforts for 2026.

Arm’s Length Terms & Conditions for Intra-Group Loans

Verifying that the terms and conditions of intra-group loans are consistent with how independent parties would contract remains a critical focus. In addition to establishing an arm’s length interest rate and the appropriate amount of debt (further explained below), it is also necessary to assess whether the other terms and conditions are at arm’s length. This involves considering the main features of the loan, and evaluating their impact on the risk profile of both the borrower and the lender, as well as on the arm’s length interest rate. Relevant terms that should be considered include currency, maturity, repayment schedule, and callability.

In 2025, courts emphasized that Transfer Pricing documentation must not only include a benchmark analysis but also a clear explanation of the contractual features agreed, especially for features such as subordination, maturity, interest structures, and repayment conditions. These features should align with the actual conduct of the parties and with the economic reality.

Multinationals have also seen a rise in challenges derived from discrepancies between the legal agreements drafted, the price applied between the entities involved, and the information presented in the Transfer Pricing report. A clear example is one-year loans that are automatically renewed, with the same price being applied and with no repayments being made, where tax authorities may reclassify them as longer-term arrangements, which typically carry a higher interest rate.

What to consider in 2026: It is important for multinational enterprises to carefully assess these terms and conditions before issuing a loan, as they will have a direct impact on the interest rate applied to the transaction. Drafting a comprehensive loan agreement that clearly outlines these terms, aligns with the conditions applied in practice, and is supported by a robust Transfer Pricing analysis is recommended to mitigate the risk of challenges by tax authorities.

Debt Capacity Analyses for Intra-Group Loans

Tax authorities are increasingly scrutinizing whether the amount of intra-group debt is economically justified and supported by a clear business purpose. They are also evaluating whether the debt aligns with arm’s length principles and serves a legitimate economic function consistent with the borrower’s overall business strategy.

A debt capacity analysis is often conducted to determine whether the borrower has the financial capacity to repay the loan and whether an unrelated party would provide a similar amount of financing under comparable conditions. If tax authorities consider that the amount of debt is excessive, adverse tax consequences could arise, such as the requalification of the debt as equity and/or the denial of a portion of the interest expense deduction.

Over the last two years, jurisdictions such as Germany and Australia introduced administrative guidelines formalizing debt capacity considerations. This trend has been further reinforced by case law in different countries over the past year. For example, in Luxembourg, a major hub for treasury companies and investment funds, the Luxembourg Administrative Court in 2025 issued a pivotal decision in case No. 50602C rejecting an automatic 85:15 debt-to-equity standard and holding that arm’s length analyses must be fact-specific and supported by data rather than mechanical ratios.

What to consider in 2026: Multinationals are expected to prepare robust debt capacity analyses for each borrower entity, demonstrating that independent lenders would extend a similar amount of debt. The debt quantum should be supported by financial projections, coverage ratios, and a documented business purpose, all included in the corresponding Transfer Pricing report.

Debt Capacity Analysis

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Credit Rating Analyses for Financial Transactions: Stand-Alone vs Group Rating

Both tax administrations, and subsequently, courts, are scrutinizing the credit rating approaches applied by taxpayers in the context of intra-group loans. Credit rating analyses are a core step when pricing intra-group loans, as the risk profile of the borrower has a material impact on the applicable interest rate.

While simplified blanket ratings across a group were once tolerated, tax authorities and courts are now emphasizing the importance of entity-specific ratings adjusted for implicit support.

In Belgium, on June 6, 2025, the Court of First Instance of Leuven clarified that credit ratings must be substantiated and not merely assumed based on group affiliation. Based on this ruling, the borrower should be assessed on a stand-alone basis, taking into account the impact of the new debt quantum on its financial position. Where implicit group support is considered, it must be properly substantiated through a thorough implicit support analysis and cannot be assumed by default our automatically applied.

In the Netherlands, the Court of Appeal of Amsterdam, in its judgment of 11 September 2025, addressed, among other topics, the guarantee fees applied by the taxpayer and rejected their payment, emphasizing the importance of factoring implicit support into the credit rating applied to the borrower. This once again highlights the relevance of a two-step process: first, the calculation of the stand-alone rating, and second, the adjustment of this rating for implicit support.

As this is a highly relevant topic, and both the lender’s jurisdiction (seeking a lower credit rating, which drives higher interest income) and the borrower’s jurisdiction (seeking a higher credit rating, which drives lower interest expense) have opposing incentives, multinationals need to have a robust and consistent process in place.

What to consider in 2026: Companies should apply a consistent methodology for credit rating analyses based on the principles and best practices set out in Chapter X of the OECD Transfer Pricing Guidelines. Where possible, this involves performing an individual credit rating analysis for each borrower, adjusted for group implicit or explicit support. In addition, this analysis should be properly documented in the Transfer Pricing report.

H2 2026 update: Credit rating determination is a contentious issue not only in Europe. In the US, the IRS’s position on implicit support is being tested in Eaton Corp v. Commissioner, No. 2608-23. This case is currently pending before the US Tax Court. Part of the case revolves around the borrower’s credit rating and whether implicit support has been appropriately considered. The IRS has separately signaled that they are considering issuing a new regulation that clarifies the impact of implicit group support on a borrower’s credit rating and the requirement that any such support be taken into account when determining the borrower credit rating.

Cash Pooling Structures and Synergy Allocation

Cash pooling structures remain an area of intense scrutiny by tax authorities worldwide. Cash pool Transfer Pricing analyses can be complex and time-consuming for a variety of reasons.

On the one hand, the participants’ accounts need to be priced on an arm’s length basis (considering the specific currency and the risk profile of each entity). This also includes identifying long-term structural balances and pricing them separately, where relevant. On the other hand, the cash pool leader needs to receive appropriate remuneration for the functions performed, risks assumed, and assets employed.

In addition, the OECD Transfer Pricing Guidelines state in paragraph 10.143 that: “The remuneration of the cash pool members will be calculated through the determination of the arm’s length interest rates applicable to the debit and credit positions within the pool. This determination will allocate the synergy benefits arising from the cash pool arrangement amongst the pool members and will generally be done once the remuneration of the cash pool leader has been calculated.”

Tax authorities are increasingly considering these recommendations, and this is becoming particularly relevant in jurisdictions where multinational enterprises have cash-rich pool entities, as tax authorities may expect that a portion of the synergies generated is allocated to them.

Finally, the involvement of multiple countries adds further complexity, as local jurisdiction-specific interpretations of the OECD Transfer Pricing Guidelines may arise. Over the past year, for example, a notable case was issued by the Spanish Supreme Court on July 15, 2025 (ruling 3721/2025). In its decision, the Court rejected the asymmetry of interest rates depending on whether they related to deposits made by the Spanish subsidiary or to amounts received by it as a loan, and emphasized that the remuneration of the leading entity must be consistent with its functions as a mere treasury centralization entity. This highlights the importance of following a coherent methodology based on the OECD Transfer Pricing Guidelines in order to support the position adopted in the event of a challenge by local tax authorities.

What to consider in 2026: Multinationals with cash pooling structures, especially where the amounts involved are material, should perform a Transfer Pricing analysis in line with the best practices set out in the OECD Transfer Pricing Guidelines. This includes: 1) pricing the debit and credit positions of the participants, identifying structural balances; 2) calculating the synergy benefits generated by the structure; and 3) allocating these synergies between the cash pool leader and the participants.

H2 2026 update: A related development from Poland reinforces the need for granular treatment of cash pools, albeit from a documentation angle. The Director of Poland’s National Revenue Information recently published an individual tax ruling noting that settlements within a physical cash pooling structure can constitute separate controlled transactions for each participant, with debit and credit positions assessed individually rather than netted when testing the local TP documentation threshold. It’s a useful reminder that documentation obligations require the same jurisdiction-by-jurisdiction rigor as pricing itself. Proactive participant-level reporting can help with tracking both withdrawal and deposit positions accurately and ensure that you have the necessary documentation in place.

Preference for Internal CUP Comparables

The Comparable Uncontrolled Price (CUP) method has long sat at the top of the OECD’s method hierarchy for intercompany transactions, including financial transactions. Specifically, when available, internal comparable arrangements are the favored starting point for any transfer pricing analysis.

While the use of an internal CUP is preferrable, in practice, many taxpayers use external CUPs to price their intercompany financing arrangements. Some jurisdictions appear to be formally affirming the preferred process outlined by the OECD: review any existing internal arrangements and apply these as part of the transfer pricing analysis if there is appropriate comparability between the tested transaction and the internal CUP arrangement. Where possible, application of both an external CUP and an internal CUP will ensure even stronger support for a transfer pricing position. The use of both approaches effectively corroborates one another.

Belgium’s Court of First Instance in Antwerp delivered a judgement on 16 June 2025, signalling the court’s preference for the use of internal comparables (the taxpayer’s own external financing) over external market data where both are available. The taxpayer had financed an acquisition partly through an external bank loan and partly through a subordinated shareholder loan, which was priced using external market data. The Belgian tax authority challenged this approach, contending that the taxpayer’s own external bank loan should have been used as the comparable arrangement on which to determine an arm’s length interest rate for the subordinated shareholder loan. The court ultimately agreed with the tax authority and upheld the approach to apply the internal CUP method. While the court ruled in favor of the use of an internal comparable, it did also note that prudent application of such an arrangement does still entail ensuring an appropriate degree of comparability and factoring in any adjustments as needed.

This is not a new rule so much as a court holding taxpayers to an existing standard and aligning with OECD guidance.

What to consider in H2 2026: Multinationals should proactively assess their external financing arrangements across the group to identify potential internal comparables that could be used to price their intercompany financing arrangements. Robust comparability assessments should also be carried out to ensure that any internal comparable is indeed appropriate for use in pricing an intercompany arrangement. Where a potential internal CUP exists but is not used, the rationale for its exclusion should be documented in the Transfer Pricing report. The use of both an internal CUP and an external CUP presents as the most robust support for a pricing determination of an intercompany financing arrangement.

Key Takeaways for Intra-Group Loans Transfer Pricing in 2026

• Tax authorities are moving beyond interest rate benchmarking and increasingly focusing on the full arm’s length characterization of intra-group loans, including contractual terms and economic substance.
• Debt capacity analyses are becoming increasingly important, as evidenced by recent regulatory developments and case law.
• Credit rating analyses should be performed on a stand-alone basis and adjusted for implicit or explicit group support, in line with the OECD Transfer Pricing Guidelines.
• Cash pooling arrangements require careful allocation of synergies between participants and the cash pool leader based on functions, risks, and realistic alternatives. Careful documentation at the leader and at the individual participant level will provide an additional level of support for cash pooling positions.
• Robust and consistent transfer pricing documentation remains essential to mitigate the risk of challenges in an environment of heightened scrutiny.
• Where an appropriate comparable external financing arrangement already exists within the group, courts are increasingly expecting taxpayers to consider its use in pricing intercompany arrangements.

Zanders Transfer Pricing Solution  

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

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

With over eight years of experience and trusted by more than 100 multinational corporations, our platform is the market-leading solution for compliance with OECD Transfer Pricing Guidelines for intra-group loans, guarantees, and cash pooling arrangements.

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Our clients trust us because we provide: 

• Transparent and high-quality embedded intercompany credit rating models. 
• A pricing model based on an automated search for comparable transactions. 
• Automatically generated, 40-page OECD-compliant Transfer Pricing reports. 
• Debt capacity analyses to support the quantum of debt. 
• Cash Pool Solution which calculates the synergies generated by the structure.
• Legal documentation aligned with the Transfer Pricing analysis. 
• Benchmark rates, sovereign spreads, and bond data included in the subscription. 
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As part of SAP's ongoing product development efforts, Zanders once again participated in a dedicated on-site testing initiative during the SAP 2608 Partner Summit for Treasury and Payment Solutions, gaining early insight into a range of developments across Cash Management and Treasury.

These developments span a mix of established capabilities, evolving functionality, and innovations that highlight SAP's broader direction in treasury.

How to interpret these developments

To support understanding, the developments can be viewed across different levels of maturity:

• Established capabilities that are already usable or broadly available

• Evolving capabilities where scope and functionality are still expanding

• Emerging innovations that indicate SAP's future direction

What are the key benefits for the clients

• Better cash visibility through improved intraday insights and multi-account views

• Reduced manual effort via automation and template-driven processes

• Stronger governance and control across treasury operations

• Greater transparency in bank fees and interest validation

• Improved scalability and standardization across entities

• Preparation for more data-driven and automated treasury processes

Overall, these developments support a shift towards more efficient, controlled, and scalable treasury operations.

Bank Account Management & Cash Management Developments

Short Term Cash Positioning

SAP continues to enhance Short-Term Cash Positioning (STCP) with a strong focus on usability. (The core SAP app used by treasury teams to monitor daily cash positions.)

The introduction of flexible hierarchy allows treasury teams to analyze multiple bank accounts within a single structure, including intraday balances. This simplifies cross-account analysis, although highlighting negative balances is not yet supported.

Further improvements in intraday balance integration provide a clearer distinction between real-time and end-of-day positions, supported by enhanced visibility in the Manage Bank Account Balances app. This enables more effective liquidity monitoring throughout the day.

Bank Fee Analysis

Enhancements in Bank Fee Analysis are designed to reduce reliance on manual processes and improve scalability.

Key improvements include:

• Template-based bulk maintenance of fee conditions

• Built-in validation and error handling

• Automatic refresh of impacted fee items

These changes strengthen data consistency and reduce operational effort. However, value realization depends on data readiness, particularly the availability of ISO 20022 camt.086 files (standard bank fee reports) and clear ownership of fee condition management.

SAP is also exploring enhancements such as support for non-standard formats and AI-supported condition recognition.

Cash Register concept

SAP's evolving Cash Register concept introduces a central processing layer where cash-relevant data from different source components, such as Accounting, Treasury, and Logistics, is collected and stored before further processing.

This concept addresses a key challenge in current system landscapes, where cash data is often fragmented across multiple processes and applications. By centralizing the data at an earlier stage, SAP aims to increase coherence, transparency, and control over how cash flows are derived and reported.

When activated, this adds an additional step before cash data flows into FQM_FLOW (the central table used by SAP Cash Management to store and report all cash-relevant data), which results in a more structured and controlled flow of information.

From a business perspective, this approach:

• enables more consistent classification of cash flows

• supports integration across complex or multi-system environments

• improves traceability for analysis and audit purposes

• provides a stronger foundation for forecasting, analytics, and future AI use cases

This represents a structural shift in how cash data is processed and requires organizations to assess how it fits within their current design.

Interest calculation and verification

SAP is developing capabilities for automated interest calculation and verification based on daily bank account balances.

This helps organizations:

• validate bank interest charges

• improve transparency

• reduce manual reconciliation effort

The capability is especially relevant for organizations managing multiple accounts with different interest conditions.

However, withholding tax is currently not supported, which may limit full end-to-end reconciliation.

AI-assisted Bank Relationship Management

SAP is introducing AI-assisted capabilities for bank relationship management, available in newer SAP environments.

By using natural language interaction, users can retrieve and analyze banking data more easily, without navigating multiple reports or systems.

This supports:

• faster access to relevant insights

• reduced manual data gathering

• more efficient preparation of bank relationship reviews

While available, the scope of this capability is still evolving, and organizations should assess how it aligns with their data setup and governance requirements.

Treasury Upgrades

Governance and control

SAP is strengthening governance through enhanced Business Partner-level authorization, including a four-eye approval principle.

This improves control over sensitive treasury master data and helps reduce the risk of unauthorized changes.

Financial transaction templates and AI support (beta)

Enhancements in Treasury & Risk Management (TRM) introduce financial transaction templates supported by AI.

Capabilities include:

• Template creation from model transactions

• AI-driven transaction creation based on user input

This is particularly relevant for repetitive processes such as intercompany loans or bank-to-bank transfers.

This functionality is currently in beta and requires further validation.

Additional Treasury developments

Additional improvements include:

• Mirror transaction creation for automated replication of flows

• Support for using functional currency as a valuation currency (aligned with IAS 21 accounting standards)

This is particularly relevant for organizations managing financial data across multiple currencies. A key prerequisite remains that no existing financial postings are present before activation.

Zanders' perspective

Based on early testing and discussions with SAP, these developments confirm a clear direction towards more standardized, automated, and control-driven treasury processes.

Several enhancements, particularly in cash visibility, bank fee management, and governance, are already relevant for organizations looking to improve efficiency and strengthen control frameworks.

At the same time, newer capabilities such as AI-supported transaction creation and AI-assisted banking insights should be introduced gradually, starting with targeted use cases and expanding as the functionality matures

From a Cash Management perspective, current innovations appear to focus primarily on selected areas such as Short-Term Cash Positioning, AI capabilities, and new concepts like Cash Register.

At the same time, incremental improvements in widely used applications, such as Cash Flow Analyzer, Manage Cash Pools (V2), and Cash Position Today, were not a key focus during the testing phase, which may be relevant for organizations relying on these tools in daily operations. It is expected that SAP will continue to enhance these applications alongside newer innovations.

A key success factor across all developments will be data readiness and process alignment. SAP continues to make meaningful progress in improving usability, automation, and governance within treasury. A balanced and pragmatic adoption approach will be key.

Why Treasury Teams Need Real-Time Liquidity Insight Now

Volatile markets, shifting rates, and tightening credit have made liquidity decisions both more frequent and more consequential. Treasurers and CFOs need a single, trusted view of cash, funding, and risk exposure, along with the ability to test 'what-if' scenarios before they commit. SAP Analytics Cloud (SAC) brings those capabilities together in one place, so teams can act decisively and justify their decisions to boards and auditors.

What Is SAP Analytics Cloud? A Guide for Treasury and Finance Teams

SAP Analytics Cloud is SAP's enterprise analytics and planning workspace. It provides a single environment for analytics, planning, and scenario modeling across finance and treasury use cases. It sits natively on top of your SAP S/4HANA landscape, and connects to other sources. This enables a consistent story of past performance, current position, and forward-looking outlook, all governed, secure, and audit-friendly.

Because it combines reporting, planning, and predictive analytics in a single platform, SAC is not limited to any one process. Treasury and finance teams can use it across liquidity, cash management, risk, and broader financial and operational planning, starting with the use cases that matter most and expanding from there.

SAP Analytics Cloud's key benefits for treasury teams:

• One source of truth: consolidate cash positions, forecasts, and exposures so everyone works from the same numbers.
• Reliable scenario planning: test funding strategies, FX moves, or working-capital initiatives and view the cash and covenant impacts in minutes.
• Board-ready storytelling: interactive pages replace static packs, leadership can drill from headline KPIs to underlying drivers live in the meeting.
• Controls and governance built in: role-based access, data lineage, and approval workflows support auditability without slowing down the business.
• Speed to value: start with high-impact use cases, then expand, with no need for a large overhaul.

SAP Analytics Cloud for Liquidity Planning: A Zanders Case Study

Zanders has delivered SAP Analytics Cloud in production for clients who want sharper, faster cash decisions. In one recent engagement for a multinational enterprise running SAP S/4HANA, treasury operated across many legal entities and currencies, so cash visibility was fragmented and forecasting was slow and manual. Zanders built a single, automated, self-service liquidity planning solution on top of the client's existing SAP investment, with no separate planning tool and no third-party data pipeline. In practice, that meant:

• Actuals from the system of record: actual cash flows and opening balances flow automatically from S/4HANA into SAC through standard SAP content, so planners always start from real, current positions instead of re-keying data.
• Actuals and planning in one place: imported actuals and planner-entered forecasts sit in a single model, with opening and closing balances that recalculate and roll forward automatically across the forecast horizon.
• Consolidated, multi-entity, multi-currency visibility: forecasts are produced per entity and currency, and consolidate accurately, giving treasury a group-wide view of cash, inflows, and outflows to guide investment and funding choices.
• A guided, one-click experience: planners select their entity through a simple prompt, see when data was last refreshed, and recalculate and roll balances with a single button. This keeps a multi-step process consistent and straightforward for finance users.
• Automation that runs itself: the daily cycle of import, recalculate, and roll-forward is scheduled to run automatically after the overnight S/4HANA reconciliation, with a manual refresh option for when actuals change during the day.
• Scope can expand over time: the solution began as a 13-week rolling cash-flow forecast and can be later extended to different planning cycles, as a cleanly isolated version, with no rebuild required. This shows how additional planning needs can be incorporated over time without redesigning the solution.

The Business Benefits of SAP Analytics Cloud in Treasury

Better dashboards are not the primary outcome. What changes is that finance, treasury, and the business work from one forecast, using the same numbers, the same assumptions, and the same logic behind each call.

• Surplus cash is easier to place: you can see which entities and currencies are holding more than they need, and whether to pool or invest it.
• Funding gets timed against the forecast: so drawdowns and repayments are not left to month-end guesswork. A forecast gap can be closed deliberately, through intercompany funding, a facility draw, or by holding back a discretionary payment.
• A "what-if" can be tested first: an FX move or a late receipt shows its cash and covenant impact before anyone commits.
• Every figure traces back to one governed source: so the call is quicker to agree and easier to explain to boards and auditors.

Risk and compliance comfort

SAC's governance features help reconcile numbers back to source systems, manage who sees what, and document approvals. In practice:

• Data lineage: every figure traces back to the S/4HANA postings behind it, so an auditor can follow a closing balance to source instead of rebuilding it in a spreadsheet.
• Role-based access: each user sees only the entities and currencies they are responsible for.
• Logged changes: every change to a forecast assumption records who made it and when.

That audit trail supports both internal control requirements and external assurance, reducing friction at quarter-end and year-end.

How to Implement SAP Analytics Cloud in Treasury: A Step-by-Step Rollout

A deliberate, value-first rollout consistently works best when:

• Starting where value is obvious: most organizations begin with short-term cash visibility and a rolling forecast.
• Bringing the business in early: co-designing the decision pages with treasury, FP&A, and key business units to secure adoption.
• Codifying the internal rules: making planning assumptions and thresholds explicit and version-controlled, so decisions are transparent and repeatable.
• Expanding deliberately: layering in working-capital analytics, FX risk overlays, and covenant monitoring once the core is trusted.
• Staying fit for purpose: as an independent advisor, Zanders scopes to your situation, builds on your existing architecture, designs for user adoption, and keeps the governance model explicit.

Getting the fundamentals right

SAP Analytics Cloud gives treasury and finance teams a single, governed source for cash, funding, and risk data, replacing fragmented reporting with one consistent view. Organizations that succeed with SAC typically start narrow, prove value on a focused use case, and expand deliberately once the core model is trusted. Zanders' experience across these implementations shows that the platform delivers the most value when it is built on existing SAP architecture rather than around it.

Compliance requirements, audit needs, and external factors such as embargoes and government-imposed sanctions are additional imperatives for corporates and financial institutions (FIs) looking to secure their end-to-end payment lifecycle. Protection must be in place from the moment a payment is triggered, or even before, until the payment reaches the intended recipient.

In a digitized world with increasing cybersecurity threats, it is becoming even more important for the payment process to be robust and supported by a strong technology infrastructure that provides security, speed, and efficiency.

What payment fraud challenges do corporates face?

The reality for many corporates is that they have multiple enterprise resource planning (ERP) systems, SAP or from other vendors, implemented over time, or multiple systems resulting from past M&A activity. As a result, these corporates often have numerous multi-banking relationships, different processes across the organization, and various systems or banking portals for making payments. In an ideal world, moving to a single system with focused banking relationships, centralized treasury management, and harmonized global processes is the end state every company wants to achieve. However, the journey to get there can be long.

How does SAP S/4HANA support payment centralization?

Companies using, or moving to, SAP as their primary ERP often aim for a single instance of the S/4 HANA landscape to create a single source of truth, though they may approach this in different ways. The journey is often long and complex. However, payment risk needs to be mitigated sooner rather than later.

Companies can adopt different strategies, such as 'Central Finance', 'Treasury First', or payment centralization through a Payment Factory (PF) solution, to enable quicker wins and improve security for the treasury and finance organization.

Advanced Payment Management Functionality

SAP introduced APM in 2019 to support payment centralization, visibility, and oversight for organizations using its systems. APM, together with In-House Bank, forms SAP's payment factory solution. SAP has continuously enhanced APM since its introduction, and the solution now includes relevant functionality, including anti-fraud measures, for users.

APM enables the centralization of payments and bank communication originating from any system, whether SAP or non-SAP, and facilitates:

• Data enrichment

• Data validations

• Conversion to bank-specific file formats where needed

• Batching, together with an approval mechanism through integration with SAP's Bank Communication Management option

• Secure single-channel communication with all banks, for example through SAP's Multi-Bank Connectivity, and

• Conversion or forwarding of bank statements to other systems, with connectivity to cash management

These measures enable treasury teams to gain central, near-real-time visibility of all outgoing payments, allowing corporate treasurers to put controls and checks in place through a robust payment approval mechanism.

A strong and auditable payment approval process governed by a unified system can help reduce payment fraud. However, payment approval alone is a relatively reactive mechanism and relies on human intervention, which can be time-consuming, labor-intensive, and prone to errors. At scale, this can increase the risk of some transactions being missed. A more advanced way to manage payment risk efficiently is through an exception-based process, where only the payments that require attention are routed for human review, while low-risk transactions are filtered through an automated rules engine that enables targeted focus on high-risk payments.

SAP offers different solutions that can be easily integrated with Advanced Payment Management to manage risks more effectively. Business Integrity Screening and Watch List Screening are examples of these solutions.

Why do corporates need SAP Business Integrity Screening (BIS)?

SAP Business Integrity Screening (BIS) is a solution that complements the payment engine of S/4 HANA, including the Advanced Payment Management (APM) function. BIS can be enabled on S/4 HANA. At a high level, it is a rules-based engine designed to detect anomalies and third-party risk. It uses data to predict and help prevent future occurrences of fraud risk.

By virtue of being on S/4 HANA, BIS can handle large volumes of payments and process them through real-time simulations. SAP BIS also integrates with different process areas, such as master data management, invoice processing, payment execution or payment runs, and APM for payments originating from other systems. This supports fraud prevention at a much earlier stage.

The below Figure 1 picture depicts a few of the features of BIS where a set of rules can be defined for different scenarios with certain SAP provided out-of-the-box rules, for example, identified risk factors might include:

• Supplier invoice and payment execution stages, like vendor invoices or bank accounts in high-risk countries

• One-time vendors

• Payments made too early

• Changes to vendor banking details just before a payment cycle

• Duplicate invoices

• Manual payments

BIS has a highly flexible detection and screening strategy for business partners where new rules can be added and it can make composite rule scenarios, resulting in an overall risk score being awarded. For example, a weighted score may be determined based on individual rules such as:

• Payment value banding

• Consecutive payments to the same beneficiary

• Beneficiary address in an 'at-risk' country

Using the power of S/4 HANA, every payment is processed through all the rules and strategies defined to detect anomalies as early as possible, with real-time alert mechanisms providing further security. Implementations can leverage out-of-the-box rules and create new rules based on internal knowledge to refine anti-fraud measures going forward. BIS also has powerful analytics through the SAP Analytics Cloud solution for evaluating the performance of each strategy and rule, enabling refinements to be made.

How does BIS integrate with APM?

For customers operating a single system environment, BIS was previously integrated with Payment Run functionality. With a multi-ERP Payment Factory landscape, BIS now integrates directly with APM. This means payments across the enterprise can be routed through screening for exception-based handling.

BIS combined with APM has two possibilities (as of writing this article):

• online screening for individual items

• or batch screening for larger volumes of payments

Rules can be set based on the size of payments as well, for example, low-value payments can be set for batch screening, while high-value transactions can be set for online screening.

In the current release BIS 1.5 (FPS00), there are pre-defined scenarios specifically for APM. These check recipient bank accounts, for example, in high-risk countries and so on, and business partner (payee) bona fides for sanction screening/embargo checks at the payment order/payment item level. Custom scenarios can be created, and further custom code enhancements built within SAP-provided enhancement points.

While screening online, APM payment orders are validated through BIS detection rules. Payments without any anomalies or risk scores below threshold are automatically approved and processed for further normal processing through APM outbound processing. Payments which are suspicious will be 'parked' in BIS for user intervention to either release the payment, remembering, it could be a false positive scenario, or for blocking.

Any blocked payment in BIS automatically moves the APM payment order to the Exception Handling queue within Advanced Payment Management for further processing, for example, taking corrective actions in source systems, validating internal processes, contacting the vendor, canceling/reversing a payment, and so on.

What is SAP Watch List Screening?

Watch List Screening is SAP's cloud-based subscription solution for restricted party or sanctioned party screening, as mandated by governments or governmental agencies such as the United Nations or the World Bank. The actual list of restricted or sanctioned parties is provided by a third party and is continuously updated and maintained by SAP in its cloud solution as part of the offering. When connected with APM through its native integration, any payment triggered or processed through APM is screened by SAP's Watch List Screening, using the recipient's details to perform compliance checks in real time. Any fallouts or exceptions are automatically captured or blocked. Further processing of blocked payments can be managed either manually or through rules. Watch List Screening can also be used to screen master data, such as business partners, at the time of creation or change.

How do APM, BIS, and Watch List Screening work together to prevent fraud?

There are different solutions available to address the specific needs of corporates across the payment lifecycle. A key first step is to centralize payments, where Advanced Payment Management can help. An important benefit of payment centralization in a corporate landscape is the opportunity to initiate centralized payment screening and fraud prevention using BIS.

The integration between BIS, Watch List Screening, and the APM Payment Factory enables effective payment fraud and sanction screening detection across the entire payment landscape. Adding Bank Communication Management for further approval control on an exception basis helps ensure a robust and automated payment process, with a strong focus on automated payment fraud prevention.

Once the payment process is secured, the next step is having secure connectivity to banks. This is where solutions like the SAP Multi-Bank Connectivity option can help.

Zanders supports corporates and financial institutions in designing and implementing SAP-based fraud prevention frameworks, including APM, BIS, and Watch List Screening. To discuss your current payment risk setup or explore next steps, contact us.

A SAP S/4HANA Treasury Business Partner (BP) is the master data object that represents a counterparty in a corporation's treasury transactions, including banks, financial institutions, and internal subsidiaries. Additionally, business partners are essential in SAP S/4HANA for recording information related to securities issues, such as shares and funds.

The SAP Treasury Business Partner (BP) serves as a fundamental treasury master data object, utilized for managing relationships with both external and internal counterparties across a variety of financial transactions; including FX, MM, derivatives, and securities. The BP master data encompasses crucial details such as names, addresses, contact information, bank details, country codes, credit ratings, Legal Entity Identifiers (LEIs), settlement information, authorizations, withholding tax specifics, and more. 

Treasury BPs are integral and mandatory components within other SAP Treasury objects, including financial instruments, cash management, in-house cash, and risk analysis. As a result, the proper design and accurate creation of BPs are pivotal to the successful implementation of SAP Treasury functionality. The creation of BPs represents a critical step in the project implementation plan. 

This section outlines the key considerations for professionally designing and maintaining BPs within SAP Treasury, and highlights the areas where consultants need to align with business users to ensure the smooth creation and maintenance of BPs.

Business Partners Structure

The structure of BPs may vary depending on a corporation's specific requirements. Below is the most common structure of treasury BPs: 

• Group BP represents a parent company, such as the headquarters of a bank group or corporate entity. Typically, this level of BP is not directly involved in trading processes, meaning no deals are created with this BP. Instead, these BPs are used to reflect credit ratings, support limit utilization in the credit risk analyzer, and enable reporting.
• Transactional BP represents a direct counterparty used for booking deals. Transactional BPs can be divided into two types: 
  • External BPs represent banks, financial institutions, and security issuers. 
  • Internal BPs represent subsidiaries of a company. 

Business Partners Naming Conventions

It is important to define a naming convention for the different types of BPs, and once defined, it is recommended to adhere to the blueprint design to maintain the integrity of the data in SAP. 

Group BP ID: Should have a meaningful ID that most business users can understand. Ideally, the IDs should be of the same length. For example: ABN AMRO Group = ABNAMR or ABNGRP, Citibank Group = CITGRP or CITIBNK. 

External BP ID: Should also have a meaningful ID, with the addition of the counterparty's location. For example: ABN AMRO Amsterdam – ABNAMS, Citibank London – CITLON, etc. 

Internal BP ID: The main recommendation here is to align the BP ID with the company code number. For example, if the company code of the subsidiary is 1111, then its BP ID should be 1111. However, it is not always possible to follow this simple rule due to the complexity of the ERP and SAP Treasury landscape. Nonetheless, this simple rule can help both business and IT teams find straightforward solutions in SAP Treasury. 

The length of the BP IDs should be consistent within each BP type. 

Treasury Business Partners' Maintenance

1- BP Creation

Business partners are created and maintained via the Fiori app Maintain Business Partner (BP), which is the standard entry point in S/4HANA. The classic transaction code BP (Maintain Business partners) remains available still for SAP GUI. During creation, various details are entered to establish the master data record, including basic information such as name, address, and contact details, as well as specific financial data such as bank account information, settlement instructions, WHT, authorizations, credit rating, and tax residency country.

Consider implementing an automated tool for creating Treasury BPs. LTMC Migration Cockpit has been deprecated, the current SAP standard option for newer SAP S/4HANA releases is the Fiori-based Migrate Your Data app, or SAP Master Data Governance (MDG) where centralized approval workflows are a priority. At Zanders, we have a pre-developed solution to create complex Treasury BPs in full, covering both SAP ECC and the latest S/4HANA releases, designed to remain clean-core compliant for clients on S/4HANA Cloud.

2- BP Amendment

Regular updates to BP master data are crucial to ensure accuracy. Changes to addresses, contact information, or payment details should be promptly recorded in SAP. SAP Joule now allows users to query duplicate flags conversationally during amendment, helping to identify inconsistencies before release.

3- BP Release

Treasury BPs must be validated before use. This validation is carried out in SAP through a release workflow procedure. We strongly recommend activating such a release workflow for BP creation and amendment, and assigning BP release to a person who is not authorized to create or amend BPs. BP amendments are often performed by the Back Office or Master Data team, while BP release is typically handled by Middle Office or an independent Master Data Governance team.

4- BP Hierarchies

Business partners can have relationships as described, and the system allows for the maintenance of these relationships, ensuring that accurate links are established between various entities involved in financial transactions. 

5- Alignment

During the Treasury BP design phase, it is important to consider that BPs will be utilized by other teams in a form of Vendors, Customers, or Employees. SAP AP/AR/HR teams may apply different conditions to a BP, which can have an impact on Treasury functions. For instance, the HR team may require bank details of employees to be hidden, and this requirement should be reflected in the Treasury BP roles. Additionally, clearing Treasury identification types or making AP/AR reconciliation GL accounts mandatory for Treasury roles could also be necessary.  

Transparent and effective communication, as well as clear data ownership, are essential in defining the design of the BPs. 

Conclusion 

The design and implementation of BPs require expertise and close alignment with treasury business users to meet all requirements and consider other SAP streams.

At Zanders, we have a strong team of experienced SAP consultants who can support you in designing BP master data, developing clean-core-compliant tools to create and amend BPs, and meeting strict treasury segregation of duties requirements as well as the client's IT rules and procedures.

This article analyzes different regions in Europe, namely DACH¹, the Nordics², the UK³ and the Netherlands⁴. It continues the annual review of how banks apply IFRS 9, building on our studies of 2023, 2024 and 2025. As in the previous studies, the expected credit loss (ECL) coverage ratios are considered, where the focus primarily lies on the Netherlands, compared with the other regions. The sample covers the largest banks that report IFRS 9 metrics in each region; this year's Dutch sample additionally includes Argenta. This year's study shows that ECL coverage ratios did not rise; if anything, they continued their slowly decreasing trend, while one could speak of considerable geopolitical turbulence. This raises the question of whether the apparent stability in coverage ratios tells the full story. If geopolitical uncertainty increased during the year, one would expect this to be reflected somewhere within the IFRS 9 framework. This article therefore examines whether signs of increased caution can still be found in macroeconomic assumptions, scenario weightings, and management overlays.

This article outlines observed practices in the annual reports; it does not assess whether current provision levels are adequate. IFRS 9 provides a common framework for expected credit losses but leaves banks with considerable room in how they apply it to their own portfolios. This year's results illustrate that variation clearly.

Coverage ratios: a soft continuation of the downward trend

The mean coverage ratio across all four regions declined slightly in 2025. In the Netherlands, this year's decrease was smaller than in the previous year, whereas in the other regions the decline was broadly similar or somewhat larger. The clearest driver of the decrease is the continued strength of Dutch house prices which lowers the modeled loss given default (LGD) on mortgages. The macroeconomic outlook plays a role too, but as the next section shows, its direction is now mixed across banks. Unlike the previous year, management overlays were not widely reduced in 2025.

Region	Mean coverage ratio 2023	Mean coverage ratio 2024	Mean coverage ratio 2025
Netherlands	0.647%	0.532%	0.497%
UK	2.119%	2.011%	1.858%
DACH	0.829%	0.822%	0.794%
Nordics	0.434%	0.543%	0.436%

Table 1: Mean ECL coverage ratio (total allowance / gross carrying amount) across banks per region.

A lower coverage ratio reflects a smaller allowance relative to the size of the loan book, that is, a lower expected loss per unit of exposure. This contrasts with the wider environment: the past year was marked by old and new conflicts in the Middle East, the continuing war in Ukraine and a wave of trade tensions, yet this was not reflected in higher aggregate coverage ratios. Where the geopolitical turbulence did come through was in some individual banks' overlays, discussed below, rather than in figures across the whole sector.

Macroeconomic scenarios: mixed outlooks, stable weights

Last year, the Annual IFRS 9 Study saw an improvement of the macroeconomic projections for most banks. The results of the macroeconomic variables that are used the most in the Netherlands are shown in this article. This year, the picture is mixed among the banks that disclose comparable forward-looking macroeconomic assumptions, as GDP rose at ABN AMRO, NIBC and Triodos, held flat at Van Lanschot Kempen and was lowered at ING and Rabobank. Projected unemployment indicates divergence as well: lower at ING and ASN, but higher at ABN AMRO, Rabobank and NN Bank. This year the divergence is therefore more pronounced as projections no longer move in broadly the same direction across banks. Rabobank is the clearest example, combining a lower growth assumption with a higher projected unemployment rate. Taken together, the picture is largely one of stability rather than a clear directional shift. The reported macroeconomic assumptions no longer move broadly in the same direction across banks, resulting in greater divergence than observed in previous years.

The expected credit loss (ECL) is a probability-weighted estimate across several macroeconomic scenarios, typically an upside, a base case and a downside.. One way in which banks reflect forward-looking uncertainty in this framework is through the weighting assigned to these scenarios. The table below shows that the net downside tilt (weight on the downside scenario minus the weight on the upside), measured based on the same banks for both years, changed only modestly between 2024 and 2025. The Netherlands and DACH became slightly less downside-oriented, while the UK and the Nordics saw small increases. These movements were driven by only a small number of banks, as most institutions left their scenario weights unchanged. Banks generally do not disclose the reasoning behind year-on-year changes in scenario weightings. Overall, the movements were limited, suggesting that banks largely kept their scenario weightings unchanged despite the uncertain geopolitical environment.

Region	Downside tilt 2024	Downside tilt 2025	Change (in percentage points)
Netherlands	13.33%	12.08%	-1.25pp
UK	17.33%	17.53%	+0.20 pp
DACH	23.33%	20.83%	-2.50 pp
Nordics	1.88%	3.13%	+1.25 pp

Table 2: Net downside tilt across banks reporting in both years, per region.

Management overlays: selective caution outside the model

While scenario weights remained broadly stable in 2025, management overlays tell a more mixed story. Coverage ratios continued to decline, but among Dutch banks that carry an overlay, the movement was not one-directional: ABN AMRO and ING reduced their overlays further while Rabobank, ASN Bank and NIBC increased theirs. Two of them disclose this out explicitly as a geopolitical buffer. Rabobank reports a management adjustment for geopolitical risk of EUR 128 million at the end of 2025, up from EUR 76 million a year earlier, while ASN holds EUR 5 million for geopolitical risks not captured in its models, out of a total management overlay of EUR 34 million. ABN AMRO, ING and NIBC instead capture such risks through their downside scenarios rather than through a separately labeled adjustment. NIBC's own overlay for instance is allocated to the Dutch housing market, climate and interest-only mortgages, not geopolitics.

Conclusion

The main observation of this year's study is that the uncertainty concerning macroeconomic tensions around the world only surfaced selectively. Coverage ratios continued their decline at a slower rate, supported in part by lower expected mortgage losses as Dutch house prices increased. At the same time, scenario weights showed little evidence of greater caution, while management overlays moved in different directions across banks. Some institutions, most notably Rabobank and ASN Bank, explicitly reported geopolitical risk within their management overlays, whereas others emphasized different risk drivers or reduced overlays altogether. The result is a picture in which caution remained present but was expressed unevenly and did not translate into higher aggregate coverage ratios.

What can Zanders offer?

The 2025 results show that approaches and outcomes still differ markedly between banks and regions. These differences stem from how models are applied, such as portfolio composition, SICR frameworks, the design and weighting of macroeconomic scenarios, and overlay practice, rather than from the standard itself. For any bank, it is worthwhile to assess whether its current IFRS 9 framework remains aligned with its expectations of future credit losses.

With a focus on the Dutch market and an active presence in the UK, DACH and Nordic regions, Zanders is in regular contact with many of the banks in this study, which positions us well to help benchmark results and support the validation or (re)development of IFRS 9 models.

Zanders guided research by students at Erasmus University Rotterdam into emerging modeling techniques for the Margin of Conservatism (MoC) categories, and the results point to real opportunities for banks. The research has shown promising new techniques, particularly for MoC categories A and B, and for the aggregation of MoC into a single total figure. In this article, we examine how each MoC category is defined, how banks approach them today, and what innovative techniques are available to improve the balance between conservatism and capital requirements.

Category A - Data & Methodology Deficiencies

MoC addresses situations where known issues in data or model design could bias risk estimates. For example, a bank might discover gaps or errors in historical data or realize that a simplifying assumption in the model (like a coarse segmentation or incomplete risk drivers) introduces a bias. According to EBA's definitions, MoC A should cover "data and methodological deficiencies" identified in the modeling or calibration process (EBA, 2017).

If you find a material deficiency in your data or method, you should first try to address it directly via Appropriate Adjustments (AA) to your data or model (e.g. imputing missing values). However, if some bias or shortcoming remains unaddressed, you then quantify a conservative add-on to compensate for the residual uncertainty. The aim is to offset any potential underestimation of risk caused by known issues.

Current Market Practices

Banks often rely on expert judgment and simple scenario analyses to set MoC A adjustments. For instance:

1- If certain older data is deemed less reliable, common practices are to:

• Benchmark model results using a cleaner subset of data (or external data) to gauge the impact. The difference might be added as MoC A.

• Model the upper confidence bound of the parameter estimate and consequently set the add-on equal to the distance between the point estimate and the upper bound.

2- In some instances, analysts may even apply a qualitative add-on to risk parameters based on their expert judgment, sometimes amounting to a flat increase or a scaling factor.

These simpler approaches are easy to communicate but can be somewhat simple and subjective, making it hard to defend the resulting values to model validation and the regulator.

Emerging Techniques

Recent research has helped to quantify MoC A more objectively. One such is the probabilistic quantification approach proposed by (Biche, 2022). They establish a closed-form expression that does not rely on an arbitrarily predefined significance level, to model uncertainty arising from missing values in the calibration sample. Instead, the Biche-approach defines a quantification of MoC A which is derived from a purely probabilistic perspective, increasing consistency across institutions.

Another direction we have explored is a framework based on influence functions, a statistical technique introduced in (Hampel, 1974), that examines how small changes in individual data points or model inputs affect the final estimate. By measuring influence on model output, one can identify where data issues or outliers might be biasing the estimates and then derive an MoC A to counteract that bias. This method allows the user to identify observations that drive MoC adjustments without repeated model re-estimation.

Conclusion

While market practices still lean on simple and judgment-driven approaches, these are increasingly challenged from a regulatory perspective. Emerging quantitative techniques offer a more structured and transparent way to capture identified biases, helping institutions move toward more unified MoC A calibration.

Category B – Changes in Environment & Policies

Category B MoC addresses structural breaks or shifts in legal- and/or macro-economic policy. These breaks between the past and the present (or expected future) would imply that your historical calibration data is (partially) out-of-date. In practice, such shifts can stem from any relevant changes to underwriting standards, risk appetite, collection and recovery policies, or any other source of additional uncertainty about the future. Even if your data and model are flawless, the future may not resemble the past.

Suppose your bank significantly tightened its lending standards, or a new economic regime (like a financial crisis or pandemic) emerged. Your PD, LGD or EAD models, built on the last decade of data, might now be too optimistic or not fully relevant for today's portfolio and/or economic state. Regulation requires you to ensure that if tomorrow's world differs from yesterday's, your risk estimates have a buffer to cover that gap. This is achieved through MoC B.

One key challenge is that Category B issues often overlap with Category C or even Category A. For instance, a structural break (Category B) usually reduces the usable data length, which in turn increases estimation error (Category C). Some banks attempt to model these overlaps explicitly. Nevertheless, modelling the co-movement of the different MoC categories is still a novel area for most banks. Section 5 covers this topic in more detail.

Current Market Practices

Banks usually handle MoC B by blending statistical tests and scenario analysis:

1- Some use structural break tests on historical default or loss data to detect if and when behavior changed (for example, a Chow test might reveal a significant shift in default rates after a certain year or policy change). If a break is detected, this flags a deficiency.

2- Others apply simple ratio comparisons (scaling factors): e.g. comparing post-change default rates to pre-change. If the model calibration sample underrepresents a recent adverse period, they might scale up PDs or LGDs by the observed difference (or by an expert-determined factor) to be conservative.

Emerging Techniques

Advanced frameworks propose quantifying MoC B via scenario modeling: first create scenarios that reflect new processes or extreme macroeconomic conditions, then measure how much PD, LGD or EAD might change under those scenarios. The weighted differences from the base forecast can serve as the MoC B. This is more dynamic and connects modeling directly to the business.

Conclusion

While current practices often rely on relatively simple diagnostics and scaling approaches, these can struggle to fully capture shifts. More advanced, scenario-based frameworks offer a forward-looking and economically grounded alternative, enabling banks to align their MoC B quantification more closely with evolving portfolio characteristics and external conditions.

Category C – General Estimation Error

Finally, Category C MoC addresses the inherent statistical uncertainty present in risk parameter estimates. Regulation states that MoC C "should reflect the dispersion of the distribution of the statistical estimator" (EBA, 2017). As econometric models are estimated on finite data samples, there will always be a margin of error surrounding the estimated PD, LGD, or EAD. This error, also referred to as noise, is most prevalent in low-default portfolios.

Current Market Practices

There is a spectrum of approaches that each have pros and cons; the most common techniques are summarized in Table 1.

Many banks choose a confidence level (α) and compute MoC C so that the final estimate corresponds to a high-end confidence bound. For example, for PD one could use a binomial distribution of default rates to determine that the upper bound of the 95% confidence interval is X% higher than the observed long-run average default rate (LRADR). MoC C is then set equal to the difference between the two.

A simplified approach is the k·σ ("k-sigma") method: choose a multiplier k for the standard error (σ) of the risk parameter, and set MoC C = k × σ. For example, if a PD's standard error is 0.2 percentage points, applying k=1.96 (approx. 95% confidence) leads to a MoC of 0.39 percentage points.

Emerging Techniques

Category C is the most well-developed MoC category as it relates to the general estimation uncertainty, present in every econometric model. Hence, many quantification techniques have already been developed and are already in use.

Conclusion

Unlike Categories A and B, MoC C is methodologically well established, leading to widespread use of standardized techniques such as confidence intervals and k‑sigma approaches. The key challenge therefore lies less in innovation and more in selecting appropriate confidence levels and ensuring a consistent level of conservatism across portfolios.

Technique	Pros	Cons
Parametric distribution	Assume defaults or losses follow a known statistical model. These often give higher, more stable MoCs and never result in zero or negative MoC.	Relies on model assumptions.
Empirical variance estimation	This is model-free and often simply uses the sample standard deviation of historical default rates.	Tends to give volatile results if data is scarce or correlated.
Resampling/Bootstrapping	Flexible (doesn't impose a parametric form) and ensures MoC is always positive.	Can be computationally heavy and may not improve accuracy if data is very limited.

Aggregation – Beyond Simple Summation

After estimating MoC A, B, and C, how should banks combine them?

The simplest (and, in regulatory landscape, the default) approach is to sum the margins over the categories: MoC_Total = MoC_A + MoC_B + MoC_C. This straightforward sum ensures a conservative outcome and is in line with: "Institutions should quantify the final MoC as the sum of MoC A, MoC B and MoC C" (EBA, 2017). But this conservative simplicity comes at a cost.

Key issues with simple summation

1- Double Counting: The categories are not guaranteed to be truly independent. For example, a structural break (Category B) often reduces the data sample, increasing the estimation error (Category C). Simply adding margins implicitly assumes worst-case alignment of all uncertainties, that every deficiency, future change, and statistical error all push the risk estimate in the same adverse direction simultaneously. However, in practice some uncertainties might overlap or partially offset.

2- Confidence Level Mismatch: If each margin is set to cover a high percentile of uncertainty, then summing them could yield a combined cushion far beyond that percentile. If MoC A and B each target an approx. 90% confidence level, adding them to MoC C could result in overall conservatism beyond 99.9%, which is more than intended. This excess conservatism means higher capital requirements.

Emerging Techniques

At Zanders, we are conducting ongoing research to explore methods to aggregate MoC more analytically. One idea is to treat overall MoC as a target confidence interval problem: define the desired overall safety level (e.g. 90% or 95% confidence that the true risk parameter is below [PD+MoC]) and then calculate MoC collectively rather than category by category. This might involve joint simulations or analytical combination of uncertainties:

1- Integrated Models: Simulate scenarios that incorporate data issues, policy changes, and statistical noise together. Compute the required MoC as the difference between the base estimate and a desired/high percentile of the simulated risk parameter distribution. This would inherently account for any interdependence between MoC A, B, and C factors (avoiding simple over-addition).

2- Variance-Covariance Aggregation: If one can estimate the variance contribution of each category and their correlations, the total uncertainty could be aggregated using statistical rules (like summing variances for independent factors, or more general formulas when not independent). Such approaches yield a smaller composite MoC than raw summation, aligning with a specified confidence level.

3- Calibration of k-Factor: Another approach (as suggested by some industry studies) is to adjust the k multiplier in the k·σ method to implicitly capture some effects of A and B. For instance, a higher k can be chosen in segments known to have Category A/B issues, rather than adding separate chunks.

Conclusion

These advanced methods can be complex to implement and justify. Regulators have so far preferred simplicity to ensure consistency across banks (hence the official recommendation of summation). The onus is on banks to demonstrate that any alternative approach is sound, transparent, and doesn't understate capital.

Toward a more calibrated Margin of Conservatism

The Margin of Conservatism is a crucial but challenging element of IRB credit risk modelling. It ensures a safety net for risk estimation by addressing known data issues (A), evolving business environments (B), and statistical uncertainties (C) that could otherwise lead to underestimation of risk. As we have seen, implementing MoC requires balancing rigor vs. simplicity: applying enough conservatism to satisfy regulatory standards and absorb model risk, but not so much that it overrules true risk differentiation or double counts uncertainties.

The state of practice is still evolving. Banks have adopted a variety of methods, from straightforward confidence interval calculations to the innovative use of influence functions, structural break analyses, and bootstrap simulations, each with its pros and cons. Meanwhile, supervisors, like the ECB, have pointed out inconsistencies between the way banks have implemented the MoC, encouraging them to refine their frameworks.

A key open question remains: what's the best way to aggregate different MoC components without introducing unintended excess conservatism? Forward-looking institutions are exploring ways to jointly model categories A, B, and C into a unified measure that respects their overlaps.

In the end, the MoC discipline forces modelers to explicitly confront model risk within Pillar 1 capital. By continuously improving how we quantify and aggregate these conservatism margins, the banking industry can meet regulatory expectations while preserving the integrity of their models. If you're an IRB modeler or validator, now is a great time to re-examine your MoC approach – ensuring it captures all relevant uncertainties without sinking your model's risk differentiation. Embracing rigorous yet practical techniques for MoC can turn a source of capital inflation into a well-calibrated guardrail for robust, credible credit risk models.

Are you interested in how you could leverage these methodologies to enhance your Credit Risk modeling approach? Contact Kyle Gartner, John de Kroon or Kasper Wijshoff for more information.

References

BCBS. (2004). International Convergence of Capital Measurement and Capital Standards. Bank for International Settlements.

Biche, E. (2022). A Probabilistic Approach to Quantify Margin of Conservatism for Data Deficiencies in a Calibration Sample for Credit Scoring Models.

EBA. (2017). Guidelines on PD estimation, LGD estimation and the treatment of defaulted exposures. European Banking Authority.

Hampel, F. (1974). The influence curve and its role in robust estimation.

In a more volatile, fast-moving environment, treasurers are expected to make faster, better-informed liquidity decisions. Yet many organizations still rely on end-of-day visibility, fragmented systems and manual processes. This gap creates both risk and opportunity. Real-time liquidity is emerging as a practical way to strengthen control, reduce inefficiencies and support better financial decision-making.

Why treasurers are rethinking liquidity

For many years, treasury operated in a relatively stable environment. Payment cycles were predictable, processes were structured, and end-of-day visibility was sufficient to support most liquidity decisions.

Today, that model is under pressure. Funding conditions can tighten quickly, business needs can shift suddenly, and critical payment flows often require faster response times. At the same time, liquidity is no longer confined to traditional business hours. In a 24/7 economy, money can move at any moment, creating both new opportunities and additional complexity. This shift is not only driven by uncertainty. It also reflects a broader transformation in how businesses operate. Speed, immediacy and availability have become the norm, and expectations of treasury have evolved accordingly.

As a result, treasury is taking on a broader role. Beyond managing payments and reporting balances, treasurers are increasingly expected to support funding decisions, capital allocation and overall business resilience. This growing responsibility requires more timely, accurate and actionable information. This evolution is also reflected in recent industry research, highlighting the increasing strategic importance of treasury within the CFO organization with 63% of survey respondents indicating that the priority of Treasury function within CFO Office has increased over the past 5-10 years.

However, many treasury functions still rely on limited visibility, fragmented banking and ERP landscapes, manual processes and static, backward-looking forecasts. To compensate for this, organizations often maintain significant precautionary cash buffers. While these buffers reduce risk and ensure operational continuity, they also trap liquidity and increase inefficiencies. In practice, capital is not always deployed where it creates most value.

Why is liquidity management changing?

The environment is becoming more supportive of a more dynamic approach to liquidity management. Instant payments are a clear example. As they become increasingly standard across the euro area, liquidity can move more quickly and more flexibly, with less reliance on cut-off times.

At the same time, improvements in connectivity are reshaping treasury operations. APIs, host-to-host solutions and integrated banking platforms make it easier to access timely and reliable data. Intraday reporting provides better visibility throughout the day, rather than only at the end of the day. Treasury systems have also evolved, with stronger forecasting and automation capabilities. While none of these developments alone fundamentally transforms treasury, together they create the opportunity to manage liquidity in a more pro-active and flexible way.

What real-time liquidity actually means

Real-time liquidity is not about making every treasury activity instantaneous. Its value lies in increased certainty and control when it matters most. In practice, this means having better visibility on cash positions during the day, knowing whether payments have been made, and being able to act without delay when needed. This allows treasury teams to move from reacting after events to actively steering outcomes.

The value becomes particularly clear in high-impact situations. Payroll, critical supplier payments, debt servicing or M&A-related transactions all require a high degree of confidence that liquidity is available at the right place and at the right moment. In these situations, delays or manual interventions are not just inefficient. They can create real risk.

At the same time, real-time liquidity is not purely a technology question. Data availability, internal processes, governance and system integration all play a key role in determining whether the potential benefits can be realized.

A pragmatic path forward: How can organizations implement real-time liquidity?

For most organizations, real-time liquidity is best approached as a gradual evolution rather than a one-off transformation. A first step is to identify where faster and more accurate liquidity insights would create the most value. This helps prioritize efforts and ensures a clear link with business needs. The next step is to strengthen the foundations. Improving visibility across accounts, ensuring timely access to payment status information and simplifying account structures are essential building blocks.

Equally important is the treasury operating model. Faster infrastructure only delivers value if decision-making processes and governance are aligned. Clear roles, responsibilities and escalation paths make it possible to translate improved visibility into effective action.

Finally, forecasting remains a key complementary capability. A forward-looking view allows treasury to anticipate liquidity needs, reduce reliance on buffers and make better use of available cash.

Rather than aiming for a large-scale transformation from the onset, many organizations benefit from focusing on a limited number of high-impact use cases. This makes it possible to demonstrate value quickly and build momentum over time.

What's next 

Real-time liquidity is a maturity journey rather than a destination, guided by business objectives and organizational complexity. It starts with better visibility and builds through greater confidence, stronger processes and improved control.

Value is created when better visibility builds confidence - confidence enables timely action and better decisions. Organizations that move early and pragmatically will not only manage volatility more effectively but also position treasury as a stronger strategic partner to the business.

Cash may be king again, but the real prize lies in what organizations can do with it when they are able to act in time.

This article was developed jointly by ING and Zanders, combining a banking perspective on real-time liquidity infrastructure with practical treasury transformation expertise.

ING Wholesale Banking serves large corporate clients, financial institutions, and governments across the globe, providing tailored solutions in areas such as lending, transaction services, and financial markets. With deep sector and sustainable finance expertise and people on the ground in many markets, ING Wholesale Banking supports its clients in navigating complex financial challenges and achieving their strategic objectives.

Zanders is a global advisory specialized in treasury management, financial risk management and treasury technology. It advises corporates, financial institutions and public sector organizations on treasury strategy, operating models, technology selection and implementation, and risk management.