Response to consultation on draft Implementing Technical Standards overhauling the EBA resolution planning reporting framework

Question 1: Are the instructions and templates clear to the respondents?

Potential Challenges:

The clarity of instructions and templates is essential to avoid inconsistent interpretation across institutions, yet the complexity of non-financial risk reporting can make this challenging. Vague or broad templates may lead to varying levels of detail and accuracy, affecting the resolution authorities’ ability to assess risks uniformly.

Risk Accounting Contribution:

The RASB suggests that using Risk Units (RUs) as a standardized, additive metric could improve clarity by providing a single risk measure across all entities and reporting templates. This standardization could address issues arising from complex or ambiguous templates, ensuring that non-financial risks are reported in a consistent, clear format. However, we note that the initial implementation of RUs may require guidance to ensure that institutions can effectively integrate this new metric.

Question 2: Do the respondents need further clarification to understand which of the minimum reporting obligations would apply to their specific profile (Resolution entity, Liquidation entity, RLE, non-institution…?

Potential Challenges:

Differentiating reporting obligations by profile (e.g., resolution entities, RLEs) may cause confusion, especially for entities falling between standard categories. Such ambiguity could result in either over-reporting or under-reporting, with compliance risks.

Risk Accounting Contribution:

Risk Accounting's entity-level risk quantification enables precise mapping of non-financial risks to each profile, reducing ambiguity over minimum reporting obligations. By adopting RUs at an entity-specific level, institutions can readily align their reporting obligations with EBA’s requirements based on their specific profile, thereby minimizing over-reporting without sacrificing data accuracy.

Question 3: Do the respondents identify any discrepancies between these templates and instructions and the determination of the requirements set out in the underlying regulation?

Potential Discrepancies and Challenges

A close review of the templates and instructions in Annex II reveals areas where there may be minor misalignments or ambiguities when compared to the underlying regulatory requirements. Specifically:

Granular Liability Data Reporting (Templates Z 02.00 through Z10.07):

The templates call for both outstanding and carrying amounts for certain liabilities, which could introduce interpretative challenges regarding which liabilities should be reported under each data point. This requirement may not fully align with certain regulatory interpretations where only one measure, typically the carrying amount, is required. Additionally, categorizing liabilities by both maturity and inclusion/exclusion from bail-in could add complexity without providing additional regulatory value in all contexts.

Definition of Relevant Legal Entities (RLEs) and Critical Functions (Templates Z 01.01, Z 07.01, FUNC 1):

The requirement to apply different thresholds (e.g., 2% or EUR 5 billion) to determine which entities qualify as RLEs introduces potential inconsistencies with the broader regulatory definition, which may be more flexible in allowing entity-specific judgments. Similarly, the instructions for mapping critical functions by onboarding capacity in Z 07.01 could benefit from clearer criteria to ensure that institutions assess critical functions consistently across different operational setups.

Substitutability of Financial Market Infrastructures (FMIs) (Templates Z 09.01 to Z 09.04):

While the templates emphasize substitutability of FMI providers, the regulatory guidelines on this topic are somewhat flexible, allowing institutions to determine substitutability based on operational context. The templates, however, could benefit from enhanced instructions that specify criteria for assessing whether an FMI has viable substitutes, aligning more closely with a principles-based regulatory approach.

Potential Risk Accounting Contribution

The Risk Accounting framework can support institutions in addressing these potential discrepancies by offering a standardized, quantifiable measure (RUs) that consistently applies across data categories and entity types. For example:

• Granular Liability Data: By using RUs to quantify non-financial risks associated with various liability types, institutions can better meet the EBA’s demand for detailed liability data without redundantly segmenting liabilities into carrying and outstanding amounts.
• RLE and Critical Functions Definitions: The RU framework allows for entity-specific non-financial risk measurement, which can aid in assessing RLE status and onboarding capacity for critical functions more consistently.
• FMI Substitutability: Through RUs, institutions can quantify dependencies on FMI services, providing a risk-adjusted measure that facilitates more straightforward assessments of FMI substitutability, even in complex operational settings.

While Risk Accounting can support regulatory alignment, the RASB suggests that minor clarifications to the templates and instructions may still be beneficial to fully harmonize with the underlying regulations.

▪ specify which element(s) of the proposal trigger(s) that particularly high cost of compliance

Potential Challenges:

Expanded Scope of Granular Liability Reporting (Templates Z 02.00 to Z10.07)

Compliance Cost Impact: The requirement to provide both outstanding and carrying amounts, along with maturity segmentation and bail-in eligibility, demands extensive data gathering and reconciliation. For many institutions, particularly smaller or mid-sized firms, these granular reporting demands could require substantial system upgrades or manual processing to ensure alignment with Annex II templates. Furthermore, differentiating intragroup liabilities versus external liabilities on this level may require customized reporting capabilities, adding to compliance costs.

Entity-Level Reporting for Relevant Legal Entities (RLEs) (Template Z 01.01)

Compliance Cost Impact: Lowering the RLE threshold to 2% or EUR 5 billion could result in more entities qualifying as RLEs, which expands the scope of reporting across additional legal entities within larger groups. This expanded entity-level reporting obligation increases the complexity of data collection, aggregation, and maintenance. Institutions may need to implement more detailed data management frameworks to monitor RLE thresholds consistently, creating ongoing compliance costs, particularly for those with varied entity structures.

Detailed Mapping of Critical Functions and Onboarding Capacity (Templates Z 07.01 to Z 07.04)

Compliance Cost Impact: The templates for critical functions require not only a comprehensive assessment of each function’s onboarding capacity but also detailed mapping to legal entities and core business lines. This requirement demands specialized operational data on critical functions, which may not be readily available in many institutions' existing systems. The need to assess onboarding capacity for each critical function further adds complexity, as institutions may need to evaluate each function’s capacity in multiple hypothetical scenarios, thus increasing costs associated with data analysis and validation.

Financial Market Infrastructure (FMI) Substitutability (Templates Z 09.01 to Z 09.04)   

Compliance Cost Impact: The need to assess substitutability of each FMI provider introduces a high compliance burden due to the operational and strategic data required to identify viable alternatives. Institutions may need to perform detailed scenario analyses to determine if alternative providers can feasibly replace each FMI, which could require significant resources and may necessitate consultation with external risk or market analysts to confirm substitutability.

Potential Risk Accounting Contribution:

The Risk Accounting framework can help mitigate some of these costs by the provided standardized metric (RUs) that simplifies data aggregation across these expanded reporting requirements:

• Liability Reporting: RUs provide a unified approach to measuring non-financial risks tied to liabilities, reducing the need for duplicative data entry on carrying versus outstanding amounts.
• Entity-Level Reporting: By applying RUs at the entity level, Risk Accounting facilitates easy identification and reporting of entities that meet the RLE threshold, helping to streamline expanded entity-level reporting.
• Critical Function Mapping: Using RUs to quantify non-financial risks related to critical functions and onboarding capacity allows for a structured approach to meet these requirements, reducing manual data processing efforts.
• FMI Substitutability: Quantifying dependencies on FMI providers with RUs supports scenario-based assessments, which can provide a consistent framework to evaluate substitutability while reducing the need for complex external assessments.

However, while Risk Accounting can alleviate some reporting burdens, initial implementation costs associated with adapting the RU framework to the EBA’s requirements may be a consideration, especially for institutions with limited capacity for system upgrades.

▪ explain the nature/source of the cost (i.e. explain what makes it costly to comply with this particular element of the proposal) and specify whether the cost arises as part of the implementation, or as part of the on-going compliance with the reporting requirements

• Expanded Scope of Granular Liability Reporting (Templates Z 02.00 to Z10.07)
  • Nature and Source of Cost: This element requires extensive data collection, segmentation, and reconciliation, as liabilities must be broken down by carrying versus outstanding amounts, maturity, and bail-in eligibility. The challenge lies in aligning accounting systems to capture these granular data points consistently across multiple entities and reconciling them with the EBA’s templates.
  • Implementation Cost: High initial costs are likely due to necessary upgrades in data architecture and reporting systems to enable detailed liability tracking.
  • Ongoing Compliance Cost: Recurring costs include regular data aggregation, validation, and reconciliation, as institutions must continuously monitor and categorize liabilities to ensure accuracy across multiple breakdowns.
• Entity-Level Reporting for Relevant Legal Entities (RLEs) (Template Z 01.01)
  • Nature and Source of Cost: Lowering the RLE threshold from 5% to 2% or introducing an absolute threshold of EUR 5 billion increases the number of entities requiring RLE designation and tracking. This expanded scope requires institutions to frequently assess entity structures to identify which entities meet the new RLE criteria.
  • Implementation Cost: High initial investment in data infrastructure is often required to expand monitoring across more entities. This includes setting up systems that automatically track and flag entities as they meet the threshold.
  • Ongoing Compliance Cost: Ongoing costs are significant due to the need for continuous monitoring, threshold checks, and reporting for newly qualified RLEs, which may involve manual intervention and frequent adjustments to reporting practices.
• Detailed Mapping of Critical Functions and Onboarding Capacity (Templates Z 07.01 to Z 07.04)
  • Nature and Source of Cost: Compliance here requires extensive data gathering on critical functions’ capacity, with detailed mapping to legal entities and core business lines. Institutions may need to assess capacity not only under current conditions but also under hypothetical stress scenarios, adding complexity to the data analysis.
  • Implementation Cost: Initial costs stem from the setup of data analytics tools and methodologies to measure and model onboarding capacity, especially where these capabilities do not exist.
  • Ongoing Compliance Cost: Recurring expenses include periodic updates to the capacity models, scenario testing, and reporting. Additionally, ongoing data collection and validation are required to ensure that capacity assessments reflect current operational conditions.
• Financial Market Infrastructure (FMI) Substitutability (Templates Z 09.01 to Z 09.04)
  • Nature and Source of Cost: This requirement to determine FMI substitutability demands operational and strategic analyses of dependencies on each FMI provider, along with viability assessments of alternative providers. Institutions often lack readily available data to assess whether FMIs have viable substitutes, necessitating manual analysis or consultation with external experts.
  • Implementation Cost: High initial costs may include developing a framework to assess substitutability, which could involve hiring specialized staff or consultants.
  • Ongoing Compliance Cost: Recurring costs involve periodic review and update of substitutability assessments, including scenario analyses to validate that alternatives remain viable. Institutions may also incur costs related to engaging with external experts for objective assessments of substitution options.

▪ offer suggestions on alternative ways to achieve the same/a similar result with lower cost of compliance for you

To achieve similar outcomes with a reduced compliance cost, the Risk Accounting method could offer alternative approaches for each high-cost element in the EBA proposal. Here are specific suggestions leveraging Risk Units (RUs) to streamline reporting, reduce redundancy, and simplify data collection without sacrificing the regulatory objectives.

Alternative Approach for Granular Liability Reporting

Current Requirement: Institutions must report liabilities in detail, segmented by carrying and outstanding amounts, maturity, and bail-in eligibility, which demands extensive reconciliation and data segmentation across entities.

Alternative Suggestion:

• Leverage RUs for Standardized Risk Aggregation: Instead of duplicating data by segmenting liabilities across multiple criteria, institutions could apply RUs to capture non-financial risks associated with liability exposures in aggregate. RUs could standardize the reporting process by providing a unified view of risk, mapped against liabilities without requiring separate carrying versus outstanding amounts.
• Introduce a Simplified Aggregation Layer: By consolidating similar liability categories and aggregating RUs based on risk significance (rather than on maturity and bail-in eligibility), institutions can reduce reporting complexity and effort. This approach maintains data granularity for high-risk liabilities but allows low-risk items to be reported more simply, resulting in fewer data points and lower ongoing compliance costs.

Alternative Approach for Entity-Level RLE Threshold Reporting

Current Requirement: With the lowered RLE threshold, institutions must continuously monitor a larger number of entities, assessing which qualify as RLEs based on a 2% or EUR 5 billion threshold.

Alternative Suggestion:

• Risk-Based Monitoring System Using RUs: Institutions could implement a risk-based monitoring system where RUs are used as a proxy for monitoring entity-level exposure. Only entities with elevated RU totals (indicating higher non-financial risk) would trigger further RLE threshold checks, allowing institutions to focus their resources on high-risk entities rather than a full-scale review of every subsidiary.
• Threshold Calibration with Risk Data: By combining RUs with financial thresholds, institutions can create a calibrated threshold that better reflects both financial size and risk significance. This approach can reduce the frequency of assessments, as only those entities approaching or exceeding combined risk and financial thresholds would require detailed RLE evaluation.

3. Alternative Approach for Mapping Critical Functions and Onboarding Capacity

Current Requirement: Mapping critical functions to legal entities and core business lines, including assessments of onboarding capacity, requires detailed data and scenario modeling to simulate onboarding capacity under different conditions.

Alternative Suggestion:

• Use RUs to Set Baseline Capacity Metrics: Instead of extensive scenario modeling, institutions could establish baseline onboarding capacity metrics in RUs. By quantifying non-financial risk exposure related to each critical function, institutions can track capacity changes and flag functions with elevated RUs for more detailed analysis, reserving scenario modeling for cases with significant risk increases.
• Dynamic Capacity Adjustment through RU Trends: Institutions could use trends in RU data over time to dynamically adjust onboarding capacity reporting, focusing on functions where RUs indicate growing risk. This method simplifies ongoing compliance by aligning reporting frequency with risk trends, avoiding the need to re-evaluate onboarding capacity for all functions regularly.

Alternative Approach for FMI Substitutability Assessments

Current Requirement: Institutions must assess the substitutability of each FMI provider, which can be resource-intensive due to the qualitative and strategic nature of these assessments.

Alternative Suggestion:

• Dependency Risk Scoring Using RUs: Institutions could use RUs to develop a dependency risk score for each FMI, which captures non-financial risks associated with FMI reliance. Providers with low dependency risk scores would not require in-depth substitutability analysis, allowing institutions to focus resources on high-dependency FMIs.
• Standardized Substitutability Criteria Based on RU Thresholds: Institutions could adopt standardized substitutability criteria based on RU thresholds. For instance, if an FMI’s dependency exceeds a certain RU threshold, a more detailed substitutability assessment is triggered. This approach minimizes ad hoc analyses by setting clear, quantifiable triggers, reducing both implementation and recurring compliance costs.

Overall Cost-Reducing Advantages of These Alternatives

By focusing on risk-based reporting with RUs as a centralized metric, these alternatives offer multiple cost-saving benefits:

• Reduced Data Duplication: Aggregating non-financial risks using RUs eliminates the need to report the same data across multiple breakdowns (e.g., carrying/outstanding amounts, maturity, and bail-in eligibility), simplifying compliance.
• Targeted Compliance Effort: Risk-based thresholds allow institutions to prioritize high-risk entities and functions for more detailed reporting, lowering ongoing costs and ensuring that compliance efforts align with risk significance.
• Streamlined Scenario Modeling: Using RUs as a proxy for onboarding capacity and FMI substitutability reduces reliance on exhaustive scenario testing, focusing compliance activities on areas of elevated risk, which can decrease the manual burden of recurring assessments.

These suggestions offer cost-effective methods for achieving the EBA’s resolution planning objectives while leveraging the efficiency and scalability of the Risk Accounting framework. By minimizing redundant data collection and targeting high-risk areas, these alternatives help institutions maintain compliance with lower resource demands, contributing to sustainable, risk-aligned reporting practices.

i. How does this change impact your organisation’s ability to report resolution data in a timely manner while still retaining data quality?

The shift in submission date by one month poses specific challenges for institutions that impact both their ability to meet reporting deadlines and maintain high data quality. These challenges arise due to condensed timelines for data aggregation, reconciliation, and quality assurance, which are critical steps in the resolution reporting process:

1. Reduced Time for Data Aggregation and Reconciliation
   • Impact: Institutions typically rely on a series of data collection cycles from various operational and financial systems to compile and verify resolution data. The shortened timeline leaves less room for aggregating and validating data across business lines and entities, which could increase the likelihood of errors or incomplete submissions.
   • Risk: With limited time to validate inputs and ensure accuracy, institutions may experience data inconsistencies, particularly for complex data points such as intragroup exposures and liability structures.
2. Increased Pressure on Year-End Data Integration
   • Impact: The original April 30 deadline allowed institutions to leverage year-end data more effectively, as post-closing adjustments could be integrated into final resolution reports. Moving the deadline to March 31 reduces the window for integrating these adjustments, potentially requiring institutions to submit reports based on unaudited or provisional data.
   • Risk: Submitting provisional data without the opportunity for full reconciliation could compromise data accuracy and necessitate resubmissions, further straining compliance resources.
3. Challenges with Non-Financial Risk Data Collection
   • Impact: Non-financial risk data, such as critical functions and FMI dependencies, often require manual input or bespoke data extraction from operational systems, which takes additional time to gather, validate, and convert to the format required by Annex II templates. The shorter deadline could lead to incomplete data on these aspects if operational teams are unable to expedite these complex processes.
   • Risk: Incomplete or delayed non-financial risk data could hinder the overall quality and comparability of the final submission, particularly in areas that require judgment or qualitative assessment (e.g., critical function capacity).

Potential Risk Accounting Contribution

The Risk Accounting framework offers tools and practices that can support institutions in meeting these challenges posed by the earlier submission deadline, particularly in terms of maintaining data quality despite the compressed timeline:

• Continuous Data Updating through Risk Units (RUs):
  By integrating RUs into financial and operational systems, Risk Accounting enables real-time or near-real-time risk tracking. This ongoing tracking allows institutions to update resolution data continuously, reducing the need for a last-minute aggregation push. With RUs, institutions can prepare high-quality data in advance and perform timely quality assurance on non-financial risk data, minimizing last-minute adjustments and ensuring that data submissions reflect recent adjustments or updates.
• Improved Consistency across Data Points:
  The RU metric provides a standardized basis for non-financial risk data, ensuring consistency in aggregating critical data points such as entity-level risk exposures, liability segmentation, and critical function capacity. This consistency helps institutions meet reporting requirements under tighter deadlines without sacrificing accuracy or coherence in complex data points.
• Reduced Reliance on Manual Adjustments:
  By systematizing risk quantification across entities and functions, the Risk Accounting framework reduces the need for extensive manual adjustments, particularly for qualitative or judgment-based data points like FMI dependencies or critical function onboarding capacity. Institutions can automate aspects of these assessments within the RU framework, streamlining the process and reducing the risk of errors associated with manual handling.

i. Do you have any comment on the changes in the definition of the RLE threshold, including the absolute threshold of 5 billion EUR?

Potential Challenges:

Lowering the RLE threshold from 5% to 2% or introducing an absolute threshold of EUR 5 billion could significantly increase the number of entities within large, complex organizations that qualify as RLEs.

This change could lead to a substantial increase in compliance costs and operational complexity for organizations that would need to monitor, aggregate, and report risk data across a larger number of legal entities.

For organizations already stretched in managing non-financial risk data, this expanded requirement might exacerbate resource constraints and create challenges in maintaining data quality.

Potential Risk Accounting Contribution:

The Risk Accounting framework, with its standardized RU metric, supports the assessment of non-financial risks across multiple entities, regardless of scale or threshold adjustments.

By adopting RUs at the entity level, institutions can readily assess and monitor which entities meet the revised threshold criteria without requiring extensive additional systems.

This framework facilitates the aggregation of non-financial risk data across entities, allowing for accurate, streamlined reporting of RLE status and risk exposure. However, the RASB recognizes that entities new to RLE reporting might require additional resources and initial guidance to fully implement the RU approach effectively in line with EBA’s updated requirements.

i. Do you identify any issues with expanding the scope of Z01.01 to all entities in the group, bearing in mind that this report would only be requested at the level of the Group?

Expanding the scope of Z01.01 to include all entities within a group introduces several operational, data management, and compliance challenges, particularly for large, complex groups with diverse operational footprints.

While reporting at the group level has potential benefits for transparency and regulatory oversight, it may also impose significant burdens on institutions due to increased data requirements, systems integration issues, and the need for consistent reporting across jurisdictions.

Below is a detailed examination of the issues institutions may face, along with specific recommendations to mitigate these challenges.

Key Challenges with Expanding the Scope of Z01.01 to All Group Entities

• Increased Data Collection and Management Burden
  • Challenge: Including all entities in the group significantly increases the volume of data that institutions must gather, standardize, and report in Z01.01. For large groups with complex structures, tracking data across numerous subsidiaries, branches, and joint ventures can be resource-intensive and may strain existing data management capabilities.
  • Impact: Institutions may face higher operational costs associated with data collection, especially for entities with limited system integration. Additionally, the need for accurate data consolidation could increase the risk of reporting delays and errors.
  • Recommendation: Introduce a materiality threshold for including entities, where only entities above a certain size (e.g., based on assets, revenue, or exposure level) need to be reported in detail. This threshold would help institutions focus on reporting the most significant entities, reducing the data burden while still providing regulators with meaningful group-level information.
• Challenges in Achieving Consistency Across Jurisdictions
  • Challenge: For multinational groups, the reporting requirements of Z01.01 may differ from local regulatory frameworks, especially in jurisdictions with unique accounting or reporting standards. Achieving consistency in data from entities operating under diverse regulatory requirements can be complex and may require adjustments or conversions.
  • Impact: Inconsistent reporting practices across jurisdictions could result in data discrepancies at the group level, affecting the overall accuracy and reliability of the consolidated report.
  • Recommendation: Provide standardized reporting guidelines for Z01.01 that address common jurisdictional discrepancies, allowing for adjustments to align local data with group-level reporting requirements. Offering specific instructions on how to handle common jurisdictional differences, such as currency conversion, local GAAP vs. IFRS, and reporting frequency, would improve consistency.
• Data Quality and Validation Issues for Small or Newly Acquired Entities
  • Challenge: Smaller entities or newly acquired subsidiaries may lack the necessary infrastructure for standardized data reporting, leading to potential data quality issues. These entities might not yet be fully integrated into the group’s data systems, which could result in inconsistent or incomplete data in the consolidated report.
  • Impact: Reporting errors or incomplete data from smaller or newly acquired entities could undermine the accuracy of group-level reporting, particularly if these entities operate independently or are not yet fully integrated into the group’s data structure.
  • Recommendation: Allow institutions to provide summary data for entities below a certain materiality threshold or grant an exemption for newly acquired entities for an initial reporting period (e.g., 12 months). This phased approach would enable institutions to gradually integrate new entities into group reporting without compromising data quality.
• Systems Integration and Compatibility Challenges
  • Challenge: Expanding the scope to all entities requires data systems capable of collecting and consolidating information from a diverse range of IT platforms and data formats, which can be challenging for groups with decentralized IT infrastructures or legacy systems.
  • Impact: Systems integration issues can result in inefficiencies, higher costs for system upgrades or data transformation, and potential delays in consolidating data from entities using incompatible systems or formats.
  • Recommendation: Provide a phased implementation timeline for full group-level reporting, allowing institutions to make necessary IT investments and system integrations. Alternatively, allow entities to report in their native format with an institution-level reconciliation to align with group reporting standards, simplifying integration and reducing immediate system upgrade needs.
• Complexity in Aggregating Financial and Non-Financial Risk Exposures
  • Challenge: Including all entities in Z01.01 requires aggregating both financial and non-financial risks across the group. Non-financial risks, such as operational or compliance risks, can vary significantly by entity and may require qualitative assessment, complicating the aggregation process.
  • Impact: The complexity of aggregating diverse risk exposures could lead to inconsistencies in how risks are reported across entities, potentially masking significant risks in specific parts of the group.
  • Recommendation: Allow separate reporting of financial and non-financial risks within Z01.01, with qualitative assessments for non-financial risks where appropriate. A standardized template for non-financial risk reporting could help ensure consistency across entities and enhance the overall clarity of group-level risk reporting.
• Resource Constraints and Increased Compliance Costs
  • Challenge: Expanding Z01.01 to cover all group entities imposes additional workload and compliance costs, requiring dedicated resources for data collection, validation, and consolidation. Smaller groups or those with fewer resources may find it challenging to allocate sufficient staff and technical support for this expanded scope.
  • Impact: The increased compliance burden could disproportionately affect smaller groups within large multinational organizations, potentially leading to resource strain and increased reliance on external consultants or third-party providers for compliance.
  • Recommendation: Consider proportional reporting requirements based on the size and complexity of the group. For example, institutions could be allowed to submit high-level summaries for smaller entities while providing full, detailed reporting for larger or systemically important entities. This approach would reduce resource strain while still ensuring that critical data is captured at the group level.
• Frequency of Updates and Real-Time Reporting Challenges
  • Challenge: For large groups, maintaining up-to-date information on all entities on a continuous basis can be challenging, particularly if reporting is required in real time or at high frequency. This could lead to challenges in keeping data accurate and reflective of the current risk profile across the entire group.
  • Impact: High-frequency reporting may result in data inconsistencies or delays, especially if entities must submit updates on short notice or if data consolidation processes are not fully automated.
  • Recommendation: Define clear frequency requirements for Z01.01 updates, such as quarterly or annual submissions, with provisions for material change updates only if significant events occur (e.g., mergers or significant acquisitions). Allowing for periodic updates rather than continuous reporting would help institutions allocate resources effectively while maintaining accurate, timely data.

Risk Accounting Contribution to Enhanced Z01.01 Reporting

The Risk Accounting framework, with its Risk Unit (RU) metric, can support institutions in managing and reporting the expanded scope of Z01.01, helping to address several of the outlined challenges:

• Consistent Risk Quantification Across Entities: By assigning RUs to each entity within the group, institutions can standardize risk reporting across diverse entity types, enhancing consistency and comparability. RUs provide a uniform basis for quantifying risk exposures at the entity level, simplifying the aggregation of financial and non-financial risks.
• Structured Materiality Thresholds for Reporting: Institutions can use RUs to establish materiality thresholds for reporting entities within Z01.01, focusing on entities with the highest risk or financial impact. This approach ensures that data collection efforts concentrate on entities with significant risk exposure, reducing the burden of including low-risk entities.
• Enhanced Real-Time Data Integration: By integrating RUs into existing data systems, institutions can track and update risk exposures for each entity in near real-time, improving data quality for high-frequency reporting. The use of RUs also enables automated updates when material changes occur, minimizing manual data consolidation efforts.

ii. Do you see an issue in the ability of the group to identify the resolution group to which each entity reported in the organizational structure belongs?

Challenges:

Identifying the resolution group for each entity within a large organizational structure poses several challenges, particularly for complex, multinational financial institutions with diverse operational structures.

Resolution groups are designed to facilitate orderly resolution in the event of a financial failure, but they require precise alignment of entities based on risk, operational dependencies, and jurisdictional considerations.

Below is a detailed breakdown of potential issues that may arise in identifying the resolution group for each entity, followed by recommendations to address these challenges.

Key Issues in Identifying Resolution Groups for Each Entity

• Complexity of Organizational Structures in Large Financial Groups
  • Challenge: Large financial groups often have intricate, multi-layered organizational structures that include various legal entities, such as subsidiaries, branches, joint ventures, and special purpose vehicles (SPVs), each serving different functions. The diversity of these entities complicates the process of mapping each one to a specific resolution group, especially when entities may contribute to multiple functional areas or customer segments.
  • Impact: The complexity of these structures can make it difficult to assign entities to resolution groups accurately, increasing the risk of misclassification. Misalignment could disrupt resolution planning, as entities critical to certain operations might be assigned to the wrong group, affecting operational continuity in resolution scenarios.
  • Recommendation: Implement a standardized classification system across the group that categorizes entities based on their operational roles, financial interconnectedness, and importance to critical functions. This classification should align with predefined criteria for resolution groups, allowing for a consistent approach to assigning entities based on their operational and financial significance.
• Jurisdictional Regulatory Differences and Overlapping Requirements
  • Challenge: In multinational groups, regulatory requirements for resolution groups vary by jurisdiction, with some countries mandating specific criteria for entities included in resolution groups (e.g., based on criticality to financial stability, regulatory importance, or interconnectedness). These differences in jurisdictional requirements can lead to ambiguity in determining resolution group alignment for cross-border entities.
  • Impact: Inconsistent regulatory standards across jurisdictions can lead to overlapping or conflicting classifications, where an entity may qualify for multiple resolution groups under different jurisdictional rules. This creates challenges in aligning these entities to a single resolution group within the group’s organizational structure.
  • Recommendation: Develop a jurisdiction-specific resolution framework that complies with local regulations while ensuring compatibility with the group’s overarching resolution strategy. Centralizing this framework within a resolution planning unit or committee would enable consistent decision-making and alignment with regulatory expectations across all jurisdictions.
• Dependencies and Interconnections Among Entities
  • Challenge: Many entities within a group are operationally or financially interdependent, relying on shared services (e.g., IT systems, liquidity, or risk management) provided by other entities. These interdependencies make it difficult to classify entities independently, as certain entities may be integral to the functioning of multiple parts of the group.
  • Impact: Failure to consider inter-entity dependencies can lead to gaps in the resolution group structure, where critical services provided by one entity may not be available to others within the same resolution group during a resolution scenario, potentially leading to operational disruption.
  • Recommendation: Conduct a dependency analysis for all entities, mapping out key operational and financial interconnections to understand which entities support or rely on others. This analysis should be used to inform resolution group assignment, ensuring that entities with high dependency are grouped together or have contingency arrangements to ensure continuity.
• Identification and Classification of Material Legal Entities (MLEs)
  • Challenge: In resolution planning, Material Legal Entities (MLEs) are typically identified based on their role in critical functions or their financial and operational significance. However, determining which entities meet the MLE criteria can be challenging, particularly for entities that operate across multiple functions or geographies.
  • Impact: Misidentifying MLEs could lead to improper resolution group classifications, where non-material entities are included in resolution groups unnecessarily or critical entities are overlooked. This misclassification risks inefficient resolution planning and can dilute the focus on genuinely critical entities.
  • Recommendation: Implement clear criteria for MLE identification based on financial exposure, role in critical functions, and operational dependencies. Regularly review and update the list of MLEs to reflect any changes in the entity’s significance or function, ensuring that resolution groups only include entities that meet the defined materiality criteria.
• Integration Challenges for Newly Acquired or Merged Entities
  • Challenge: When new entities are acquired or merged into the group, integrating these entities into existing resolution groups can be challenging, especially if they bring unique operational risks, dependencies, or regulatory requirements that differ from the group’s established structure.
  • Impact: Newly acquired or merged entities may not be fully integrated into the group’s risk management and operational frameworks, creating ambiguity in aligning them with existing resolution groups. Failure to classify these entities appropriately can lead to regulatory compliance issues and increased risks during resolution.
  • Recommendation: Develop a transitional resolution planning process for newly acquired or merged entities, assessing their operational fit and regulatory alignment with existing resolution groups. A phased integration approach can be used to gradually bring these entities into the group’s resolution framework, ensuring they are fully assessed and classified accurately.
• Data Availability and Quality for Entity Classification
  • Challenge: Accurately identifying resolution groups requires high-quality, up-to-date data on each entity’s operational function, financial exposure, and dependencies. For large groups, maintaining such data across numerous entities can be challenging, and inconsistencies in data quality or completeness can affect the accuracy of entity classification.
  • Impact: Poor data quality can lead to misclassification of entities or incomplete identification of resolution group members, affecting the robustness of the resolution plan. Inaccurate data may also result in regulatory scrutiny or the need for repeated data reconciliation.
  • Recommendation: Establish a centralized data management system that consolidates data for all entities in real time, ensuring accuracy and completeness. Regular data validation and reconciliation processes should be implemented to keep entity information current, supporting accurate classification within resolution groups.
• Frequency and Timing of Updates to Resolution Group Assignments
  • Challenge: Financial groups often undergo structural changes, with entities expanding, merging, or divesting frequently. These changes can affect the assignment of entities to resolution groups, requiring timely updates to reflect the current organizational structure.
  • Impact: Infrequent updates or delays in reflecting structural changes in resolution group assignments can result in outdated resolution planning, potentially missing newly significant entities or retaining entities that are no longer critical.
  • Recommendation: Implement a policy for periodic reviews of resolution group assignments (e.g., quarterly or semi-annually), with additional reviews triggered by significant organizational changes. This approach ensures that the resolution group structure reflects the group’s current operational and risk profile.

Potential Risk Accounting Contribution

The Risk Accounting framework, with its use of Risk Units (RUs), can support institutions in accurately identifying and classifying entities within resolution groups by providing a standardized metric for measuring non-financial risks across entities. Here’s how RUs can address some of the challenges outlined above:

• Objective Criteria for Materiality and Criticality: By assigning RUs based on each entity’s financial exposure, operational function, and dependencies, institutions can establish objective criteria for identifying MLEs. Entities with high RU scores indicate material importance and can be prioritized for inclusion in resolution groups, reducing subjectivity in classification.
• Dependency Analysis for Accurate Resolution Group Assignment: RUs can quantify inter-entity dependencies by measuring the risk exposure tied to shared services or operational dependencies. This approach ensures that entities with significant dependencies on one another are grouped together in resolution plans, reducing the risk of operational disruption during resolution.
• Consistency Across Jurisdictions: Using RUs as a standardized risk metric helps normalize risk assessments across entities, regardless of jurisdictional differences in regulation. This allows institutions to apply a consistent framework for resolution group classification globally, ensuring regulatory alignment while maintaining operational coherence.

i. Are the data-point definitions provided for reporting of the Carrying Amount sufficiently clear?

Challenges:

The data-point definitions provided for reporting the Carrying Amount are a central aspect of accurately capturing the value of liabilities on an institution’s balance sheet. 

While these definitions cover some essential aspects, additional clarity on specific areas would improve consistency, reduce interpretative discrepancies, and ensure that institutions report Carrying Amounts in alignment with regulatory expectations. Below is a detailed breakdown of key areas where further clarification would be beneficial, along with recommended adjustments to the definitions.

Key Areas Requiring Additional Clarity in Carrying Amount Definitions

• Clarification of Accounting Standards and Valuation Basis
  • Current Ambiguity: The instructions lack explicit guidance on which accounting standards (e.g., IFRS, US GAAP) should be applied when calculating Carrying Amounts, particularly when institutions operate in multiple jurisdictions with varying accounting frameworks.
  • Recommended Clarification: Specify that Carrying Amount should be determined according to IFRS standards unless otherwise indicated, with allowances for institutions following other frameworks (e.g., US GAAP) to adjust based on equivalent principles. This will ensure a uniform approach and enable comparability across institutions operating under different accounting systems.
• Treatment of Accrued Interest and Fees in Carrying Amount Calculation
  • Current Ambiguity: The definition does not specify whether accrued interest, fees, or other ancillary costs should be included in the Carrying Amount for liabilities, particularly in cases involving complex financial instruments.
  • Recommended Clarification: Explicitly state that accrued interest and fees should be included in the Carrying Amount where applicable, following the effective interest method. For example, a field could be introduced to capture “Carrying Amount including Accrued Interest” and “Carrying Amount excluding Accrued Interest,” allowing institutions to report both figures if relevant. This would help institutions consistently account for accrued but unpaid amounts tied to liabilities.
• Guidelines for Reporting Amortized Cost vs. Fair Value
  • Current Ambiguity: Certain financial instruments may be reported at either amortized cost or fair value, depending on their classification (e.g., loans vs. derivatives), which could lead to inconsistencies in reporting Carrying Amounts.
  • Recommended Clarification: Provide clear guidelines on whether to use amortized cost or fair value for different types of instruments, such as specifying that non-derivative liabilities should typically be reported at amortized cost while derivatives are reported at fair value. Adding examples for common instruments like loans, bonds, and derivatives would enhance consistency in how Carrying Amounts are calculated across asset types.
• Handling of Foreign Currency-Denominated Liabilities
  • Current Ambiguity: The instructions do not specify how to report Carrying Amounts for liabilities denominated in foreign currencies, particularly regarding the exchange rates to be used (e.g., spot rate at period end, average rate).
  • Recommended Clarification: Define a standard currency conversion approach for foreign currency-denominated liabilities, recommending that institutions use the period-end spot exchange rate for Carrying Amounts. Alternatively, provide an option to report both the original currency amount and the converted amount, enabling regulators to see both the actual and adjusted values. This clarification would ensure uniform reporting for multi-currency liabilities.
• Specific Instructions for Carrying Amount of Derivative Liabilities
  • Current Ambiguity: The Carrying Amount for derivative liabilities is typically recorded at fair value, but the instructions do not specify how to handle derivatives with significant fluctuations in market value or derivatives that are part of a hedge accounting relationship.
  • Recommended Clarification: Specify that derivative liabilities should be reported at fair value as per accounting standards and indicate whether derivatives held for hedging purposes should be separately identified. Additionally, guidance on whether to report positive and negative fair values for netting arrangements would improve clarity, especially for institutions with large derivatives portfolios.
• Guidelines for Including or Excluding Embedded Derivatives in Carrying Amount
  • Current Ambiguity: For financial instruments with embedded derivatives (e.g., convertible bonds), it is unclear if the Carrying Amount should include the value of the embedded derivative or be reported separately.
  • Recommended Clarification: Define the treatment of embedded derivatives by specifying whether institutions should include or exclude the embedded derivative component from the Carrying Amount. Alternatively, allow institutions to separately report the carrying amount of the host contract and the embedded derivative. This approach would enhance transparency, particularly for liabilities with significant embedded risk components.
• Treatment of Liabilities with Non-Standard Settlement Features or Contingencies
  • Current Ambiguity: The instructions do not provide guidance on how to report the Carrying Amount of liabilities with contingent settlement terms or other non-standard features, such as liabilities that may convert to equity under specific conditions.
  • Recommended Clarification: Specify that the Carrying Amount should reflect the liability’s value at the reporting date based on its most likely settlement scenario. For example, liabilities with contingent conversion clauses could be reported with an annotation indicating the conditions under which the liability could be converted. This would ensure consistency in handling non-standard liabilities and improve comparability across institutions.
• Documentation of Adjustments to Carrying Amount
  • Current Ambiguity: There is limited guidance on documenting adjustments made to Carrying Amounts, such as those for impairments, fair value changes, or amortizations. Without a standardized approach, institutions may document these adjustments inconsistently.
  • Recommended Clarification: Introduce specific fields to document any adjustments applied to the Carrying Amount, such as “Impairment Adjustments,” “Amortization Adjustments,” and “Fair Value Changes.” Each adjustment type could be captured with a standardized code or description, ensuring that all institutions report adjustments in a clear and comparable manner.
• Guidelines for Liabilities Held at Net Carrying Value under Netting Agreements
  • Current Ambiguity: For liabilities that are subject to netting arrangements (e.g., certain types of collateralized derivatives or interbank exposures), it is unclear if the Carrying Amount should be reported on a gross or net basis.
  • Recommended Clarification: Specify whether Carrying Amounts for nettable liabilities should be reported on a net or gross basis, aligning with applicable accounting standards. Allow institutions to indicate whether netting has been applied and provide a separate data field for the netted value, offering transparency in cases where liabilities are partially offset by assets.

Potential Risk Accounting Contribution

The Risk Accounting framework, with its standardized Risk Units (RUs), can support institutions in capturing and reporting Carrying Amounts consistently, especially for complex liabilities. Here’s how RUs can enhance clarity and comparability in Carrying Amount reporting:

• Consistent Application of Fair Value and Amortized Cost Adjustments: RUs can be used to quantify non-financial risks associated with fair value changes or amortized cost adjustments, enabling institutions to align their Carrying Amounts with a risk-adjusted view that remains consistent across accounting standards.
• Quantifiable Approach for Embedded and Contingent Features: RUs offer a way to quantify the additional risks from embedded derivatives or contingent features, allowing institutions to assign a risk value to these components separately from the primary liability. This supports clearer reporting for complex liabilities and enables consistent inclusion or exclusion of these features based on regulatory guidance.
• Standardized Adjustments and Documentation of Changes: By applying RUs to track adjustments (e.g., impairment or fair value changes), institutions can systematically document changes to Carrying Amounts, enhancing transparency. Each adjustment’s risk-weighted impact can be measured with RUs, enabling regulators to assess the real-time financial implications of Carrying Amount changes.

ii. Do the revised data points for the reporting of Own Funds by Investment Firms better correspond to the reporting obligations for these types of Institutions? If not, please elaborate what changes you deem appropriate.

Challenges:

The revised data points for reporting Own Funds by investment firms are an essential step towards achieving consistency in the assessment of capital adequacy and resilience.

However, while the updates reflect a closer alignment with regulatory obligations, there are specific areas where the data points could be further refined to better capture the unique capital structure and operational risks faced by investment firms. Below is a detailed analysis of aspects where additional clarification or adjustment could enhance the relevance and applicability of these data points.

Key Areas for Further Refinement in Own Funds Reporting for Investment Firms

• Granularity in Tiered Capital Components
  • Current Limitation: The revised data points for Own Funds capture general categories such as Common Equity Tier 1 (CET1), Additional Tier 1 (AT1), and Tier 2 capital. However, investment firms often have unique capital instruments that may not fit neatly within these standard categories, especially for firms that rely on contingent convertible instruments or other non-standard equity-like instruments.
  • Suggested Change: Introduce sub-categories within each tier (CET1, AT1, Tier 2) that allow investment firms to report non-standard capital instruments separately. For instance, a “Hybrid Capital” sub-category within AT1 would enable firms to report instruments with both equity and debt characteristics, providing a more accurate view of capital composition. Additionally, specific fields for contingent convertible bonds (CoCos) or subordinated debt with specific triggers could clarify the firm’s capital resilience in stress scenarios.
• Inclusion of Deduction Adjustments Specific to Investment Firms
  • Current Limitation: While the revised data points include standard deductions, such as goodwill and intangible assets, they may not fully capture specific deductions relevant to investment firms, such as exposures to high-risk, volatile assets or investments in illiquid financial instruments. These exposures often represent a significant risk for investment firms but may be underrepresented in the deduction criteria.
  • Suggested Change: Add specific deduction categories that address investment-firm-specific risks, such as “Deductions for Illiquid Holdings” or “Deductions for High-Risk Financial Assets.” These additions would allow for an accurate calculation of net Own Funds by accounting for exposure types that directly affect investment firms' capital adequacy.
• Detailed Reporting of Risk-Weighted Assets (RWAs) Related to Trading Book Exposures
  • Current Limitation: The revised data points include provisions for reporting RWAs but may lack the necessary detail to differentiate between trading book exposures, which are highly relevant to investment firms, and non-trading book exposures. Investment firms often face significant market risks in their trading activities, which impact their capital needs differently than standard credit exposures.
  • Suggested Change: Introduce a breakdown within RWAs specifically for trading book exposures, including categories for different asset classes (e.g., equities, fixed income, derivatives) within the trading book. This granularity would allow regulators to assess the capital adequacy related to market risk more effectively, ensuring that firms maintain sufficient capital against trading-related risks.
• Clarification on the Treatment of Off-Balance Sheet Exposures and Contingent Liabilities
  • Current Limitation: Many investment firms operate with significant off-balance sheet exposures, such as derivative contracts or financial guarantees, which carry risks not immediately visible in the firm’s balance sheet. The revised data points do not fully address how such off-balance sheet exposures should be treated in the Own Funds reporting context, potentially leading to inconsistent reporting across firms.
  • Suggested Change: Specify data points for reporting off-balance sheet exposures within Own Funds calculations. Fields for “Off-Balance Sheet Derivative Exposures” or “Contingent Liabilities” would ensure that these exposures are consistently reported and risk-weighted. Additionally, introducing a standardized approach to valuing and risk-weighting these off-balance sheet exposures would improve consistency.
• Inclusion of Adjustments for Market Risk and Operational Risk Exposures
  • Current Limitation: Investment firms are particularly exposed to market and operational risks, which are core components of their risk profile. However, the current data points focus more heavily on credit risk, potentially overlooking capital requirements for these other significant risk types.
  • Suggested Change: Introduce data points specifically for adjustments related to market and operational risks within Own Funds. This could include a field for “Market Risk Adjustment” based on VaR (Value-at-Risk) or other market risk metrics, and an “Operational Risk Adjustment” field that allows firms to account for potential operational losses. This would align Own Funds reporting with the actual risk structure of investment firms, ensuring capital adequacy is more reflective of market volatility and operational risks.
• Standardized Treatment for Leverage Ratio Reporting
  • Current Limitation: Leverage ratio requirements are relevant to investment firms due to their reliance on short-term funding and high leverage in some trading activities. The revised data points do not, however, provide explicit instructions on how to report the leverage ratio, especially in cases where firms use derivatives or repurchase agreements extensively.
  • Suggested Change: Include a standardized data point for the leverage ratio that specifies how derivative and repo exposures should be incorporated. Defining whether gross or net exposure should be used in calculating leverage ratio components would clarify reporting for highly leveraged investment firms and allow regulators to assess leverage consistently across different types of firms.
• Granularity in Tier 1 Deductions for Specific Risk Mitigants and Guarantees
  • Current Limitation: Investment firms often rely on specific types of guarantees and collateral arrangements that may not be easily classified under general deductions for Tier 1 capital. For example, guarantees on trading book exposures or collateral arrangements for margin lending are common, yet may not be well-represented under the current deduction fields.
  • Suggested Change: Introduce fields for specific deductions related to guarantees, collateral arrangements, or credit enhancements that investment firms frequently use. This could include a “Guarantee Deduction” and “Collateral Deduction” category within the Tier 1 section, allowing for a more detailed and accurate reflection of how these mitigants impact the net Own Funds calculation.
• Inclusion of Stress Testing and Scenario Analysis Requirements in Own Funds Reporting
  • Current Limitation: The current data points do not require institutions to report on stress testing or scenario analysis, which are critical tools for investment firms to gauge the resilience of Own Funds under adverse conditions.
  • Suggested Change: Add data points that require investment firms to report key results from internal stress tests related to Own Funds, such as maximum potential losses under stress scenarios. Fields for “Projected Own Funds in Stress Scenario” or “Stress Loss Coverage Ratio” would provide insight into the firm’s capacity to withstand significant market or operational disruptions, aligning capital adequacy with real-world risk exposure.

Potential Risk Accounting Contribution

The Risk Accounting framework, through its use of Risk Units (RUs), can further enhance Own Funds reporting by providing standardized, quantifiable insights into the non-financial risks that are particularly relevant to investment firms. Here’s how RUs can contribute to the suggested improvements:

• Enhanced Breakdown of Capital Components: RUs can be used to assign risk-adjusted scores to different types of capital instruments within CET1, AT1, and Tier 2, helping to distinguish non-standard instruments and accurately reflect their associated risks. By capturing the risk level tied to each capital type, institutions can provide more granular capital composition data.
• Risk-Adjusted Treatment of Market and Operational Exposures: By assigning RUs to market and operational risk exposures, institutions can integrate these risks into the Own Funds calculation consistently. This quantitative approach aligns Own Funds with actual risk exposure, ensuring that capital adequacy reflects the investment firm’s risk profile.
• Consistent Reporting of Off-Balance Sheet and Contingent Exposures: RUs allow for a standardized, scalable approach to quantifying off-balance sheet exposures, such as derivative contracts and guarantees. This enables investment firms to report off-balance sheet risks as part of their Own Funds calculation in a comparable and transparent manner.

iii. Do you anticipate any difficulties in providing the additional data required for the reporting of intragroup financial connections (for liabilities excluded from bail-in)?

Challenges:

The reporting of intragroup financial connections, especially for liabilities excluded from bail-in, presents several challenges due to the complex nature of intragroup relationships, data aggregation limitations, and regulatory nuances. Below is a detailed analysis of specific difficulties institutions may encounter in providing this data, followed by suggestions for mitigating these challenges and ensuring accurate, consistent reporting.

Key Challenges in Reporting Intragroup Financial Connections for Bail-in Excluded Liabilities

• Complexity of Tracking Intragroup Liabilities Across Multiple Legal Entities
  • Challenge: In large, diversified organizations, tracking intragroup liabilities across various legal entities, especially those spread across jurisdictions, can be highly complex. This complexity is compounded when liabilities are excluded from bail-in due to local regulatory or contractual provisions.
  • Impact: Without a centralized system to manage and monitor intragroup liabilities across entities, the process can require significant manual reconciliation, which increases the risk of errors and inconsistencies.
  • Suggested Mitigation: Institutions could consider adopting a centralized data management platform that tracks all intragroup liabilities across the organization. By standardizing data entry and tracking at the group level, institutions can streamline the reporting of these liabilities and reduce the need for manual reconciliation.
• Identification and Segmentation of Bail-in Excluded Liabilities
  • Challenge: Determining which intragroup liabilities are excluded from bail-in can be challenging, as exclusion criteria may vary based on jurisdictional rules, contractual clauses, and specific regulatory interpretations. In some cases, liabilities may partially meet bail-in criteria, adding further complexity to the classification process.
  • Impact: Misclassification of liabilities as bail-in or bail-in-excluded can lead to inaccurate reporting and regulatory discrepancies, potentially affecting the institution’s resolution planning.
  • Suggested Mitigation: Clear internal guidelines and standardized criteria for bail-in eligibility assessments, developed in consultation with legal and regulatory teams, can help ensure accurate classification of liabilities. Additionally, conducting a periodic review of liabilities in light of evolving regulatory interpretations can keep classifications up-to-date.
• Data Availability and Systems Integration Limitations
  • Challenge: Many institutions face data availability issues when reporting granular intragroup liabilities, especially if their systems are not integrated across legal entities or if data resides in multiple formats. Legacy systems, in particular, may lack the functionality to capture details required for this reporting (e.g., detailed exposure categorization and inter-entity linkage data).
  • Impact: System fragmentation can hinder timely data retrieval, leading to increased compliance costs and potentially incomplete or inconsistent reporting.
  • Suggested Mitigation: Institutions could consider investing in system upgrades or data integration initiatives to enhance cross-entity visibility and data sharing. Implementing data consolidation tools that automatically pull relevant information from various systems can reduce manual effort and improve reporting accuracy.
• Currency Conversion and Valuation Consistency for Multi-Currency Intragroup Liabilities
  • Challenge: Intragroup liabilities are often denominated in multiple currencies, which introduces additional complexities for reporting. Institutions need consistent currency conversion practices to ensure accuracy, especially as currency values fluctuate and exchange rates vary by reporting date.
  • Impact: Inconsistent currency conversions could lead to discrepancies in reported values, especially for liabilities excluded from bail-in that require precise valuation.
  • Suggested Mitigation: Institutions should adopt a standardized approach for currency conversion, specifying whether spot, average, or period-end exchange rates should be used. By applying a uniform currency conversion policy, institutions can enhance the consistency of their intragroup liability reporting.
• Frequent Updates Due to Changing Intragroup Transactions and Financial Connections
  • Challenge: Intragroup financial connections are often dynamic, with liabilities and exposure levels changing frequently due to new transactions, changes in market conditions, or organizational restructuring. This fluidity complicates reporting, particularly for liabilities excluded from bail-in that require continuous monitoring.
  • Impact: High transaction volumes and frequent liability adjustments make it difficult to keep reports updated and accurate, especially when dealing with end-of-period cutoffs.
  • Suggested Mitigation: Implementing a real-time tracking system that captures intragroup transactions as they occur can alleviate this challenge. With near-real-time data updates, institutions can generate up-to-date reports without needing extensive last-minute adjustments.
• Cross-Jurisdictional Regulatory Variances in Bail-In Requirements
  • Challenge: Different jurisdictions may have unique bail-in rules, with variations in which liabilities qualify for exclusion from bail-in. This regulatory diversity requires institutions operating in multiple regions to navigate complex legal and regulatory landscapes.
  • Impact: Inconsistent regulatory interpretations can lead to discrepancies in classification, with some liabilities deemed bail-in-excluded in one jurisdiction but not in another.
  • Suggested Mitigation: Institutions should work closely with regional regulatory and compliance teams to align their classifications with local regulations. Creating a central repository of jurisdiction-specific bail-in rules and maintaining a record of regulatory clarifications can aid in consistently applying classification standards.
• Documentation and Audit Trail for Liability Exclusions
  • Challenge: Regulators may require documentation explaining why certain liabilities are classified as excluded from bail-in. Without detailed records or an audit trail, institutions may find it challenging to substantiate these classifications during regulatory reviews or audits.
  • Impact: Insufficient documentation can lead to compliance risks, especially if regulators question the criteria used to exclude certain liabilities from bail-in.
  • Suggested Mitigation: Establishing an audit trail with clear documentation for each liability’s bail-in eligibility status, along with the rationale for exclusion, can strengthen compliance and facilitate smoother regulatory reviews. Documentation should include details on the regulatory or contractual basis for exclusion.
• Quantifying Interdependencies and Contingencies within Intragroup Connections
  • Challenge: Intragroup liabilities are often part of broader interdependent financial arrangements. For example, one entity’s liability may act as collateral for another entity’s exposure. Identifying and quantifying these interdependencies adds complexity, particularly when reporting liabilities excluded from bail-in.
  • Impact: Without accounting for interdependencies, institutions may misrepresent the overall financial risk or exposure level in their reporting.
  • Suggested Mitigation: Use a dependency matrix to map inter-entity relationships, capturing the dependencies and contingencies tied to each liability. This approach allows institutions to view liabilities in context, providing a holistic view of intragroup financial connections.

Potential Risk Accounting Contribution

The Risk Accounting framework, through the application of Risk Units (RUs), can help institutions overcome some of these challenges by offering a standardized approach for quantifying and reporting intragroup financial connections, especially those excluded from bail-in. Here’s how RUs can support the suggested mitigations:

• Consistent Tracking and Classification of Liabilities: By assigning RUs to each intragroup liability based on bail-in eligibility, currency exposure, and counterparty, institutions can maintain a standardized record of each liability’s risk profile. RUs provide a consistent basis for tracking and categorizing liabilities across entities, simplifying classification and ensuring comparability.
• Real-Time Data Updates for Dynamic Transactions: Risk Accounting supports continuous monitoring of intragroup exposures by assigning RUs dynamically, reflecting changes in transaction volume or currency fluctuations as they occur. This approach enables institutions to keep their intragroup liability reports up-to-date and accurate, even as financial connections evolve.
• Holistic View of Dependencies and Exclusions: RUs capture interdependencies within intragroup financial connections by measuring the aggregate risk tied to each connection. Institutions can use RUs to create a dependency matrix, identifying which liabilities are central to inter-entity connections and where contingencies exist, ensuring that regulators see a complete and interconnected view of liabilities.
• Standardized Documentation and Audit Trails: By using RUs to record and justify bail-in exclusions, institutions can create a standardized audit trail. Each RU score provides a quantifiable rationale for a liability’s exclusion, aligned with regulatory criteria and documented clearly for audit purposes.

iv. Do you see merit in providing additional clarification about any data-point definition existing in the previous version of the CIR on Resolution Reporting? If so, for which specific data points?

Suggested Data Points for Additional Clarification

Several data points in the previous version of the Common Implementing Regulation (CIR) on Resolution Reporting would benefit from additional clarification. These clarifications are especially relevant given the increased granularity and specificity in the current templates, which could introduce ambiguity if definitions are not precise. The following data points would benefit from further clarification to ensure consistent interpretation and reporting accuracy across institutions:

Carrying Amount vs. Outstanding Amount for Liabilities

• Need for Clarification: The distinction between carrying and outstanding amounts, as required for specific liability data, may not always be clear in practice. For some instruments, such as certain types of secured liabilities or liabilities with embedded derivatives, the carrying amount may not align directly with the amount outstanding at a given time.
• Suggested Clarification: Clear guidelines on when to report carrying versus outstanding amounts, and how to handle liabilities with variable principal or interest components, would ensure uniform application. Additionally, specific examples (e.g., how to report repurchase agreements or collateralized loans) could help institutions navigate complex cases.

Maturity Breakdown for Liabilities and Bail-In Eligibility

• Need for Clarification: Reporting liabilities by maturity and eligibility for bail-in adds complexity, as institutions must categorize each liability into specific maturity buckets and determine eligibility based on jurisdictional or contractual terms. For liabilities with flexible maturity options (e.g., early redemption or put/call features), categorization may be ambiguous.
• Suggested Clarification: Additional guidance on categorizing liabilities with variable or uncertain maturity dates, along with illustrative examples for complex instruments, would reduce inconsistencies. This could include criteria for determining eligibility based on different legal regimes or contractual clauses related to bail-in applicability.

Intragroup Liabilities and Treatment of Consolidated Entities

• Need for Clarification: The instructions on reporting intragroup liabilities (e.g., distinguishing between intragroup and external liabilities within consolidated groups) can be challenging for complex group structures with numerous intercompany transactions.
• Suggested Clarification: More detailed definitions and criteria for determining when liabilities qualify as “intragroup” under the EBA’s reporting standards, particularly for entities with mixed consolidation bases (accounting vs. prudential), would improve reporting accuracy. Clear guidelines on specific scenarios, such as partially owned subsidiaries or entities located in third countries, would also aid compliance.

Critical Functions Capacity and Dependencies on Financial Market Infrastructures (FMIs)

• Need for Clarification: The previous CIR requires reporting on the capacity of critical functions and dependencies on FMIs, but without detailed definitions, institutions may interpret capacity assessments and dependency evaluations differently, especially for qualitative data points like onboarding capacity and substitutability of FMIs.
• Suggested Clarification: Definitions of “onboarding capacity” and specific criteria for “dependency” on FMIs would be helpful. Providing examples of how to assess dependencies (e.g., scenarios for dependency versus substitutability) would guide institutions in aligning their assessments with regulatory expectations.

Relevant Services and Operational Asset Mapping

• Need for Clarification: The templates covering relevant services and mapping to operational assets require institutions to identify and categorize services supporting critical functions, yet distinctions between types of operational assets (e.g., core versus ancillary assets) may not always be clear.
• Suggested Clarification: More precise definitions of operational assets that support critical and essential services, along with guidelines on categorizing assets that are indirectly involved in critical operations, would help ensure consistent mapping across institutions.

Potential Risk Accounting Contribution

Risk Accounting can help mitigate ambiguity by applying the standardized RU metric across these data points. By quantifying non-financial risks in a consistent format, RUs reduce interpretative variability, particularly where carrying versus outstanding amounts, maturity segmentation, and critical function dependencies are involved. 

However, additional clarifications from the EBA, as suggested above, would further enhance the effectiveness of the RU framework by ensuring that data definitions align closely with regulatory requirements and reduce discrepancies across reporting institutions.

i. Do you have questions on how the new instructions on Onboarding Capacity should be interpreted for your organization?

Challenges:

The new instructions on onboarding capacity, which aim to assess the institution’s ability to handle additional transactional volume or services in times of stress, could benefit from further clarification. Below are detailed questions that could help interpret and apply these instructions more consistently:

Specific Questions on Onboarding Capacity Instructions

• Definition and Scope of Onboarding Capacity
  • What is the specific definition of “onboarding capacity” in the context of critical functions?
    Clarity on whether onboarding capacity refers strictly to the institution’s capability to handle an increase in transactional volume or to continue existing operations under stress would help refine assessments. For instance, does it include both scaling up new clients and handling increased volume from existing clients?
  • Does onboarding capacity include both anticipated and emergency scenarios, or only projected increases in regular operations?
    Understanding whether onboarding capacity assessments should account for both normal operational growth and unexpected surges (e.g., market disruption scenarios) would guide institutions in preparing more accurate capacity reports.
• Quantitative vs. Qualitative Measurement
  • Should onboarding capacity be measured in terms of quantitative metrics (e.g., transaction volume, number of clients handled per day) or qualitatively (e.g., overall resilience and adaptability)?
    A specific guideline on whether capacity should be expressed in concrete figures or broader, qualitative descriptions would help standardize reporting. For quantitative assessments, recommended units (e.g., transactions/day or client onboarding capacity) would ensure comparability across institutions.
  • Is there an expected range or benchmark for onboarding capacity assessments that institutions should aim for?
    Benchmarks or threshold ranges would provide a useful point of reference, allowing institutions to assess if their capacity falls within an acceptable range, especially for critical functions like payments or custody.
• Methodology and Assumptions for Capacity Assessment
  • What assumptions should institutions make when assessing onboarding capacity?
    Should institutions base their onboarding capacity on average historical volumes, peak historical usage, or hypothetical stress scenarios? Clear guidance on these assumptions would support consistent and realistic capacity evaluations.
  • Is there a specific methodology that institutions are recommended to use when calculating onboarding capacity?

If a standardized calculation or model is expected, providing detailed steps or preferred methodologies would ensure that institutions apply the instructions consistently. For example, should institutions use scenario analysis, stress testing, or other modeling techniques to project onboarding capacity?

• Reporting Onboarding Capacity for Shared or Multi-Function Resources
  • How should onboarding capacity be reported for shared resources that support multiple critical functions?

For institutions that use common resources (e.g., shared IT systems or operational staff) across different critical functions, guidance on whether capacity should be reported per function or as an aggregated capacity would clarify reporting requirements.

• Should institutions adjust reported onboarding capacity based on interdependencies or shared resources?
  Where onboarding capacity for one critical function depends on another (e.g., a payments system dependent on IT support), understanding whether to account for these dependencies in capacity assessments would enable more accurate reporting.
• Frequency of Onboarding Capacity Assessments
  • How frequently should onboarding capacity be assessed and updated?
    Clear instructions on assessment frequency—annually, quarterly, or only when significant changes occur—would ensure institutions maintain relevant and up-to-date capacity reports without excessive reevaluation.
  • Are institutions required to reassess onboarding capacity following operational or market changes that impact capacity?
    For instance, if an institution experiences a significant increase in client volume or upgrades its systems, guidance on whether and when to update onboarding capacity assessments would help maintain report accuracy.
• Expectations for Documenting Capacity Limits and Constraints
  • Should institutions report specific capacity limits, such as maximum volume per transaction type, or simply an overall capacity range?
    Providing clarity on whether to document detailed capacity limits (e.g., maximum daily transactions for payments) or general capacity ranges would help institutions report information at the expected level of detail.
  • Is there a requirement to list or explain specific operational constraints that could impact onboarding capacity?
    Understanding whether institutions should document constraints like staffing limitations, system bandwidth, or dependency on external providers would enhance the quality of capacity assessments, particularly if certain functions are constrained by limited resources.
• Testing and Validation Requirements for Onboarding Capacity
  • Are institutions expected to validate onboarding capacity through periodic testing, such as stress testing or scenario analysis?
    Clarity on whether testing is required, and if so, which types of tests (e.g., operational stress tests or contingency simulations) would allow institutions to validate their assessments accurately and consistently.
  • If testing is required, what frequency and conditions are expected for these tests?

Detailed guidance on test frequency (e.g., annually, biannually) and any recommended conditions for validation (e.g., specific volume or transaction stress scenarios) would standardize testing practices, ensuring comparable capacity assessments.

Potential Risk Accounting Contribution

The Risk Accounting framework can support more structured and quantitative onboarding capacity assessments by using Risk Units (RUs) to capture non-financial risks that impact an institution’s ability to onboard additional clients or transaction volume. Here’s how RUs can address each question area:

• Quantitative Measurement of Capacity: RUs provide a standardized measure for onboarding capacity, allowing institutions to assign RUs based on their available resources and operational constraints. This approach enables a quantitative expression of onboarding capacity, aligning with possible benchmarks or thresholds.
• Scenario-Based Capacity Testing: Using RUs, institutions can model onboarding capacity under different scenarios by assigning different RU levels to specific conditions (e.g., normal operations, peak periods). This aligns with testing and validation requirements, enabling more reliable, scenario-based capacity assessments.
• Documenting Constraints and Dependencies: RUs can quantify dependencies and constraints (e.g., reliance on shared resources or external providers), offering a clear view of any factors that could impact onboarding capacity. This structured approach supports consistent documentation of capacity limits, helping institutions to report constraints more precisely.

ii. Do you find the availability of a comments section useful to explain your assessment of the critical functions? Would you suggest another means of doing this, and if so, what?

Challenges:

The comments section offers a flexible, narrative approach for institutions to elaborate on their assessments of critical functions, providing context and qualitative insights that standardized data points alone may not fully capture.

However, while the comments section has value, it can also introduce variability in reporting quality and length, making it challenging to extract consistent insights across institutions.

Below is a detailed analysis of the usefulness of the comments section, followed by suggestions for enhancing it and proposing alternative methods for delivering clear, comparable information.

Usefulness of the Comments Section

• Contextualizing Quantitative Data
  • Benefits: The comments section allows institutions to explain nuances in their critical function assessments that may not be fully captured by quantitative metrics alone. This includes detailing specific dependencies, operational complexities, or unique risk factors associated with each critical function, which can provide valuable insights for regulators seeking a comprehensive understanding of resilience.
  • Limitations: Without structured guidance, comments may vary significantly in detail and focus. Some institutions may provide comprehensive context, while others might limit their explanations, leading to inconsistencies. Additionally, free-text comments can be difficult to analyze systematically, limiting their usefulness for comparative analysis.
• Explaining Assumptions and Methodologies
  • Benefits: Institutions often make assumptions or employ specific methodologies to assess critical functions, especially when estimating onboarding capacity or evaluating dependencies. The comments section allows institutions to describe these assumptions, making it easier for regulators to understand the basis of the reported metrics.
  • Limitations: Varied explanations can lead to challenges in interpreting assumptions consistently. Furthermore, without specific prompts, institutions may omit important details, reducing the comments’ value as a tool for verifying and understanding the reported data.
• Justifying Risk Ratings and Substitutability Assessments
  • Benefits: When assessing critical functions, institutions often assign risk ratings and evaluate substitutability. The comments section provides space to justify these ratings, helping regulators understand the criteria used and the rationale behind certain risk assessments or substitutability decisions.
  • Limitations: Because substitutability and risk assessments are somewhat subjective, inconsistent commentary can lead to discrepancies. For instance, one institution may detail specific risks while another simply states a high-level conclusion, limiting comparability across submissions.

Suggested Enhancements for the Comments Section

To address these limitations while retaining the flexibility and contextual richness of the comments section, the following enhancements are recommended:

• Structured Prompts within the Comments Section
  • Description: Incorporate prompts or subheadings within the comments section to standardize responses and ensure all institutions address key aspects. Prompts could cover areas such as:

1. Assumptions used in the assessment (e.g., scenarios considered for onboarding capacity).
2. Description of dependencies on third-party providers or systems.
3. Justification of critical function’s risk rating or substitutability score.

• Benefits: Structured prompts ensure that institutions provide comprehensive, comparable explanations, making it easier for regulators to understand and assess the underlying rationale for reported data.
• Guidance on Expected Length and Detail Level
  • Description: Provide clear guidelines on the expected level of detail and length for comments to ensure they are concise yet informative. This could include word or character limits and guidance on focusing commentary on areas of highest risk or complexity.
  • Benefits: Establishing expectations around detail and brevity would help institutions produce focused responses, reducing unnecessary information while ensuring that important contextual details are consistently covered.
• Checklist of Key Elements to Address in the Comments
  • Description: Include a checklist of critical elements that institutions should address when commenting on each critical function. This checklist could cover:

1. Identification of key operational risks.
2. Description of primary and secondary dependencies.
3. Contingency plans for potential disruptions.

• Benefits: A checklist would standardize the information provided in comments, ensuring that each assessment includes all relevant factors. This would improve comparability and provide a more systematic basis for regulatory review.

Alternative Means for Providing Explanations

In addition to refining the comments section, the following structured alternatives could enhance the clarity and comparability of critical function assessments:

• Drop-Down Fields with Pre-Defined Options for Common Explanations
  • Description: For common aspects of critical function assessments, such as dependencies or risk levels, include drop-down menus with pre-defined options (e.g., “High Dependency,” “Medium Dependency,” “Low Dependency”). Institutions could select the option that best describes their assessment and then provide a brief, focused explanation.
  • Benefits: Drop-down menus simplify reporting and reduce variability by providing structured response options, while still allowing institutions to elaborate in a standardized way. Regulators benefit from streamlined data entry, as well as improved consistency and comparability across institutions.
• Standardized Tables for Key Elements (Dependencies, Substitutability, Capacity)
  • Description: Replace or supplement the comments section with standardized tables that require institutions to provide specific data points on each critical function. Each table could include fields for:

1. List of main dependencies (e.g., critical IT systems, FMI providers).
2. Substitutability score or timeframe (e.g., can be substituted within 24 hours, 1 week).
3. Onboarding capacity in quantitative terms (e.g., daily transaction volume that can be handled).

• Benefits: Structured tables ensure that all critical data is captured in a uniform format, facilitating easier comparison and analysis by regulators. Institutions could add brief comments to each field for additional context, creating a balance between structure and flexibility.
• Scorecard Format for Justifications of Risk Ratings
  • Description: Develop a scorecard that requires institutions to justify risk ratings for each critical function. The scorecard could include sections for:

1. Operational risk level (e.g., based on volume, dependency on third parties).
2. Substitutability risk (e.g., ease of finding alternatives).
3. Impact on resolution planning (e.g., potential effect on liquidity or market stability).

• Benefits: A scorecard would provide a standardized approach to explaining risk ratings, ensuring consistency while giving regulators a clear view of each factor influencing the rating. The structured format would reduce variability in explanations and improve transparency in how ratings are determined.

Potential Risk Accounting Contribution

The Risk Accounting framework, with its Risk Unit (RU) metric, can further support structured commentary on critical functions by providing a consistent measure of non-financial risks. Integrating RUs with the proposed alternatives above would streamline reporting on critical functions by:

• Providing Quantifiable Measures for Dependencies and Risks: By assigning RUs to dependencies, substitutability, and capacity, institutions can offer a standardized, numeric basis for their assessments, reducing the need for lengthy explanations in the comments section. This structured approach allows regulators to assess critical functions’ risk profiles based on objective metrics rather than relying solely on qualitative descriptions.
• Supporting a Scorecard for Consistent Risk Ratings: RUs can be used within a scorecard to rate risk levels objectively, enhancing comparability and transparency in critical function assessments. The use of RUs to quantify risk exposure across operational areas ensures a uniform approach, helping to avoid subjective or inconsistent commentary.
• Improving Structured Tables with Quantitative Data: RUs can populate fields in structured tables to capture dependency levels, substitutability, and capacity thresholds. This quantitative approach allows institutions to meet regulatory expectations while minimizing manual input and commentary.

i. Do you see any issue in identifying “relevant services” as defined in the revised ITS?

Challenges:

The definition of "relevant services" may be unclear, leading to inconsistent identification of services critical to operational continuity.

Potential Risk Accounting Contribution:

As noted in Annex II’s relevant services templates (SERV 1 through SERV 5), a clear identification of services critical to operational continuity is essential. The Risk Accounting framework’s systematic approach to quantifying dependencies could streamline this process. Additional definitions from the EBA on “relevant services” would aid institutions in applying RUs more consistently, ensuring alignment with these structured requirements.

ii. Do you think that that the data request on relevant services, as covered in the revised ITS, is sufficiently clear?

Challenges:

The data request on relevant services in the revised ITS introduces an important focus on the critical and essential services that underpin an institution’s operational continuity. While the emphasis on relevant services is a positive development for capturing dependencies within the organization, there are areas where the clarity of definitions and reporting requirements could be enhanced. Specifically, the following points highlight areas where greater clarity would improve the consistency and quality of reporting:

Definition of “Relevant Services” and Distinctions between Critical and Essential Services

Challenge: The term "relevant services" broadly encompasses both critical services (which support critical functions necessary to the economy) and essential services (which are necessary for implementing resolution strategies effectively).

However, the distinction between critical and essential services can be nuanced, particularly where services have indirect or support functions. For example, a technology service that is indirectly linked to a critical function may not clearly qualify as either critical or essential, depending on its perceived impact.

Recommendation for Clarification: Clear definitions and practical examples illustrating the distinction between critical and essential services, especially for services that indirectly support key operations, would reduce ambiguity.

Such clarity would enable institutions to apply consistent criteria when identifying services that fall within each category.

Operational Asset Mapping and Its Scope

Challenge: The ITS requires mapping of relevant services to operational assets (e.g., facilities, IT systems, intellectual property) that support their continuity. This mapping, however, raises questions about the granularity required and the scope of assets to be included, especially for shared assets that support multiple services or entities.

Recommendation for Clarification: Detailed guidance on the expected level of granularity and how to treat shared operational assets in the mapping process would be beneficial. For instance, a standard criterion could be provided to determine whether an asset should be classified as essential to a relevant service if it is used across multiple critical functions. Additionally, examples of how to map complex IT systems or facilities that serve multiple critical functions would help institutions apply the guidance more consistently.

Roles and Staff Dependencies Linked to Relevant Services

Challenge: The revised ITS introduces the concept of “relevant roles,” referring to key personnel whose availability is essential to the continuity of relevant services. However, it is not always clear how to determine which roles qualify as “relevant” versus those that are supportive but not essential, particularly in large institutions where roles may have overlapping responsibilities.

Recommendation for Clarification: More specific criteria for identifying relevant roles and guidance on how to evaluate role criticality would support consistent reporting. Examples could include role-specific attributes, such as unique expertise or non-substitutable functions, that qualify a position as “relevant.” Additionally, defining how organizations should assess dependency on these roles across departments or functions would further clarify the requirement.

Quantifying Service Capacity and Substitutability

Challenge: The ITS requests an assessment of each relevant service’s capacity and its substitutability by alternative providers, which may require significant qualitative judgment. The term “capacity” in this context could be interpreted as either the service’s ability to meet normal operational demands or its resilience in times of stress, which introduces potential variability in interpretation.

Recommendation for Clarification: Providing clear definitions of “capacity” and “substitutability” for relevant services, including thresholds for assessing acceptable levels, would enable more objective reporting. The EBA could offer illustrative scenarios to guide institutions on how to evaluate a service’s capacity (e.g., volume, resilience under stress scenarios) and its ability to be substituted (e.g., viable timelines for switching providers).

Reporting Frequency and Thresholds for Changes in Relevant Services

Challenge: The revised ITS does not specify the reporting frequency for changes in relevant services or whether a materiality threshold applies for reporting modifications to service capacity, substitutability, or operational dependencies. Without clear frequency guidelines, institutions may struggle to determine when updates or reassessments are required, potentially leading to over-reporting or under-reporting.

Recommendation for Clarification: Establishing a clear reporting frequency (e.g., annually, or upon significant change) and defining materiality thresholds for relevant services reporting would provide institutions with clear triggers for updating their assessments. This would reduce unnecessary reporting effort for minor changes while ensuring that significant shifts in service dependencies or capacities are captured consistently.

Potential Risk Accounting Contribution

The Risk Accounting framework can support the identification and assessment of relevant services by providing a standardized metric (RUs) to quantify non-financial risks associated with critical and essential services. By assigning RUs to operational assets and roles, institutions can systematically track and report dependencies, capacity, and substitutability within a structured framework.

However, the additional clarifications suggested above would improve the application of RUs, especially for assessing indirect dependencies, evaluating service resilience, and determining update frequency for significant changes.

These enhancements would help institutions apply the RU framework with greater accuracy, ensuring that the reporting meets the EBA’s objectives for transparency and consistency across entities.

iii. Do you see any overlap between this data request and related data requests on relevant/critical services raised by your Resolution Authority as part of the resolvability assessment?

Challenges:

The data request on relevant and critical services in the revised ITS does overlap with similar data requirements typically raised by Resolution Authorities (RAs) during resolvability assessments. This overlap could lead to duplication in reporting efforts, increased compliance costs, and potential inconsistencies in how these critical services are defined and assessed across different regulatory submissions. Below are specific areas where overlap is observed, along with recommendations on how to address these redundancies:

Areas of Overlap

Identification of Critical and Relevant Services

• Overlap: Both the ITS and resolvability assessments require institutions to identify and report on critical and relevant services that are essential to operational continuity and resolution planning. These requirements often overlap in scope, as both focus on services integral to critical functions (e.g., payment processing, IT systems) and core business lines that support the institution’s stability.
• Challenge: Institutions may need to perform redundant data collection and categorization efforts to comply with both the ITS and RA assessments, as each may have slightly different expectations or definitions for critical and relevant services.
• Recommendation: A harmonized definition of critical and relevant services, used consistently across both the ITS and RA requirements, would reduce redundancy. This could include aligning criteria for assessing criticality (such as the impact on financial stability or operational continuity) and requiring a single, consolidated list of services that satisfies both reporting requirements.

Dependency and Substitutability Assessments for Relevant Services

• Overlap: Both the ITS and RA resolvability assessments request institutions to evaluate the dependencies of critical services on specific providers (such as FMIs or key third-party vendors) and assess substitutability in case of disruption. These assessments include identifying which services rely heavily on external providers and how easily these services can be transferred to alternate providers under stressed conditions.
• Challenge: Performing dependency and substitutability assessments across different regulatory submissions can result in duplicative analysis, particularly if each assessment requires a unique format or specific criteria. This redundancy is resource-intensive and may create inconsistencies if updates to one assessment do not automatically carry over to the other.
• Recommendation: Establishing a unified framework for dependency and substitutability analysis, which could be updated centrally and referenced across both the ITS and RA submissions, would simplify compliance. If the same dependency thresholds and substitutability criteria are applied, institutions could generate a single report that meets the needs of both regulatory frameworks, reducing the need for separate evaluations.

Mapping of Services to Operational Assets and Roles

• Overlap: Both the ITS and resolvability assessments require detailed mapping of relevant services to operational assets (such as IT systems, physical infrastructure, and intellectual property) and key roles essential to service continuity. This involves specifying which assets and roles are directly linked to critical and relevant services and would be required to maintain these services during resolution.
• Challenge: Maintaining consistent mappings across different data requests can be difficult, particularly if each regulatory requirement has slightly different expectations for detail or format. For example, the ITS may specify a detailed mapping of roles, while the RA might focus more on the functional rather than role-specific dependencies.
• Recommendation: Aligning the ITS and RA requirements for service-to-asset and service-to-role mapping would streamline data collection and ensure that mapping information is consistent across submissions. Institutions could develop a standardized template for mapping services to assets and roles, reducing the need for redundant mapping exercises and simplifying updates.

Assessment of Onboarding Capacity and Resilience of Critical Services

• Overlap: Both the ITS and RA resolvability assessments request evaluations of onboarding capacity (the ability to continue or expand services under stress) and service resilience. This often includes metrics to assess each service’s operational limits and fallback capabilities in the event of disruption, which are crucial for resolution planning.
• Challenge: Performing separate capacity and resilience assessments for the ITS and RA can lead to duplicated efforts, particularly if each requires a different scenario or testing framework. Repeating these assessments across different submissions may also lead to inconsistent results if changes in capacity are not uniformly reflected across regulatory reports.
• Recommendation: A unified approach to capacity and resilience assessments, using a standardized framework that satisfies both ITS and RA requirements, would reduce duplicative efforts. This could include using common scenarios, capacity metrics, and resilience indicators. Institutions would then need only one capacity and resilience assessment, which could be referenced in both submissions, ensuring consistency.

Potential Risk Accounting Contribution

The Risk Accounting method can play a significant role in mitigating overlap by creating a standardized, centralized risk reporting structure that institutions can use across multiple regulatory submissions. By quantifying non-financial risks through Risk Units (RUs), institutions can:

Consolidate Data Collection: RUs provide a single, additive measure for non-financial risks, allowing institutions to aggregate critical service data, dependency assessments, and capacity measures into one framework. This consolidated data can then be used for both ITS and RA submissions, ensuring that critical data points (e.g., service dependencies, asset mappings) are consistent across regulatory requirements.

Streamline Capacity and Resilience Assessments: Risk Accounting allows institutions to continuously monitor the RU levels associated with each critical service, operational asset, or role, providing an ongoing assessment of capacity and resilience. This dynamic tracking reduces the need for repeated manual evaluations by maintaining up-to-date capacity metrics that meet both ITS and RA expectations.

Facilitate Unified Reporting Templates: Through the RU framework, institutions can create unified reporting templates that meet the data requirements of both ITS and resolvability assessments. For example, by mapping RUs to each service and updating them centrally, institutions can generate a single dependency map or asset-role association that fulfills both reporting obligations.

i. Is the definition of “substitutability” provided in the new reporting on Alternative CCP providers (Z09.04 c0030) sufficiently clear? If not, what clarifications do you think would be necessary?

Challenges:

The concept of “substitutability” for Central Counterparty (CCP) providers is critical to assessing an institution’s resilience and its ability to maintain continuity in the event that a primary CCP provider is unavailable. However, the current definition of substitutability in template Z09.04 (c0030) could benefit from additional specificity to ensure consistent application across institutions. The following clarifications would help address potential ambiguities and support more accurate, comparable reporting.

Key Areas for Clarification

Criteria for Assessing Viability of Alternative CCPs

• Current Ambiguity: The definition of substitutability lacks clear criteria for assessing when an alternative CCP is considered “viable.” Institutions may interpret viability differently, with some focusing on operational capabilities (e.g., technical compatibility), while others may consider market-related factors (e.g., liquidity and pricing).
• Suggested Clarification: The EBA could establish explicit criteria for what constitutes a viable CCP substitute, which could include operational readiness, liquidity sufficiency, and alignment with the institution’s existing infrastructure. For example, substitutability criteria might require that the alternative CCP be able to clear a minimum volume within a defined timeframe or offer access to similar asset classes as the primary CCP.

Definition of Acceptable Timeframe for Transition

• Current Ambiguity: The timeframe in which an institution must be able to switch to an alternative CCP is not specified, leading to varied interpretations of substitutability. Without a defined transition period, institutions may overestimate or underestimate their capacity to transfer activities.
• Suggested Clarification: The EBA could define an acceptable transition period (e.g., within one day, one week) that determines whether a CCP is genuinely substitutable. This timeframe could be standardized across institutions or vary based on the nature of the asset classes or the criticality of the CCP function. For example, shorter timeframes could apply to high-frequency asset classes, while longer ones could apply to low-volume, specialized trades.

Consideration of Market and Regulatory Constraints

• Current Ambiguity: There is currently limited guidance on how to assess market and regulatory constraints that may impact substitutability, such as regional restrictions or licensing requirements that prevent a swift transition to an alternative CCP.
• Suggested Clarification: The definition of substitutability could include an assessment of regulatory and market barriers. Institutions could be required to confirm that any listed alternative CCP meets all regulatory requirements (such as jurisdictional licensing) and that market conditions (such as membership or collateral requirements) are favorable for a timely transition.

Financial and Operational Costs of Switching CCPs

• Current Ambiguity: The current definition does not specify whether financial or operational costs should factor into determining substitutability. Significant costs associated with switching CCPs may reduce the practical viability of alternatives, even if technically possible.
• Suggested Clarification: The EBA could clarify if cost considerations should impact the assessment of substitutability. For instance, if switching costs are prohibitively high, the alternative CCP might not be considered substitutable for practical purposes. Clear thresholds or guidelines around acceptable cost levels for transitions would enable institutions to assess substitutability with a consistent approach.

Testing Requirements for Substitutability

• Current Ambiguity: It is unclear whether institutions are expected to test substitutability under various conditions to validate that alternative CCPs can handle the operational and transactional demands if a primary CCP fails.
• Suggested Clarification: The EBA could introduce optional or mandatory substitutability testing requirements, such as scenario-based assessments or periodic validation exercises. Testing could include sample transactions to evaluate the operational feasibility of transitioning to an alternative CCP, ensuring that any theoretical substitute can function as needed in practice.

Potential Risk Accounting Contribution

Risk Accounting provides a structured, quantitative framework that can support a more precise assessment of CCP substitutability by quantifying dependencies through Risk Units (RUs). By applying RUs to measure risk exposure related to each CCP provider, institutions can assess substitutability with added rigor and consistency:

Dependency Scoring: Institutions could use RUs to create a dependency score for each CCP provider, where higher RU scores indicate greater reliance on a specific CCP. This score could serve as a baseline to determine if an alternative CCP is needed to mitigate concentrated risk exposures.

Thresholds for Transition Costs and Timeframes: RUs could also help define cost and timeframe thresholds, as higher RU values would reflect higher risk exposures and potentially greater urgency for substitutability. By setting RU-based thresholds, institutions could standardize their approach to deciding when an alternative CCP is necessary.

ii. Are there additional or modified data points that you propose to include in Z09.03 to adequately capture the activity of the reporting entity with FMI service providers?

Challenges:

Template Z09.03 captures critical data on a reporting entity’s reliance on FMI service providers. However, there are several areas where additional data points could provide a more complete view of an entity’s interactions with FMIs, supporting better risk assessment and resolution planning. The following suggestions outline specific data points that would enhance clarity, granularity, and usability of the data collected, providing a fuller picture of dependency on FMIs:

Suggested Additional or Modified Data Points

1. Volume and Value of Transactions Processed by Each FMI
   • Current Limitation: Z09.03 does not specify transaction volumes or values for each FMI provider, which are key indicators of dependency and potential systemic risk. Without these metrics, it is difficult to assess the extent to which an institution relies on a particular FMI, especially for high-value or high-volume operations.
   • Proposed Data Point: Include fields for the average daily volume and total value of transactions processed by each FMI. These fields would allow institutions to quantify their reliance on each provider based on transaction scale, which is essential for understanding the systemic implications of a disruption.
   • Benefits: By quantifying transaction volume and value, regulators and institutions gain insight into the relative importance of each FMI in supporting core activities, enabling prioritization of risk mitigation efforts for higher-dependency FMIs.
2. Criticality Rating of Each FMI for Core Business Lines
   • Current Limitation: The template does not currently require institutions to specify the criticality of each FMI to specific core business lines, which would indicate the operational impact of a disruption on various parts of the institution.
   • Proposed Data Point: Add a “Criticality Rating” field, where institutions can assign a standardized criticality score to each FMI (e.g., low, medium, high) based on its significance to core business lines. This score would be based on predefined criteria, such as the proportion of a business line’s transactions that rely on the FMI, or the operational risk posed by losing access to it.
   • Benefits: This addition would help regulators understand which FMIs are essential to the institution’s core functions, making it easier to assess and prioritize contingency plans for FMIs deemed critical.
3. Dependency on FMI-Specific Operational Services (e.g., Clearing, Settlement, Custody)
   • Current Limitation: While Z09.03 requires general information about the institution’s relationship with each FMI, it does not detail specific operational services provided by the FMI, such as clearing, settlement, or custody. These services have distinct risk implications and may vary in their substitutability.
   • Proposed Data Point: Include fields for each of the primary operational services provided by the FMI (e.g., “Clearing,” “Settlement,” “Custody,” etc.), with an indicator for the institution’s level of dependency on each service.
   • Benefits: By distinguishing between these services, the data can better capture nuances in the institution’s reliance on FMIs, supporting tailored risk management and contingency planning for specific functions.
4. Substitutability Assessment with Alternative FMIs
   • Current Limitation: While substitutability is generally addressed in related templates, Z09.03 does not explicitly capture whether the institution has viable alternatives for each FMI relationship. This omission makes it difficult to assess contingency options in case of a disruption.
   • Proposed Data Point: Add a field for “Alternative FMI Availability,” with options to indicate whether an equivalent FMI service provider is readily available (e.g., “Yes,” “No,” or “Partial” for limited availability). Additionally, a brief “Substitutability Explanation” field could capture any specific conditions or limitations associated with switching providers.
   • Benefits: By capturing substitutability within Z09.03, the template provides a direct assessment of contingency options, improving preparedness for FMI-related disruptions.
5. Exposure to Operational or Cyber Risks through FMIs
   • Current Limitation: The template does not capture specific operational or cyber risks associated with dependence on FMIs. Given the digital and interconnected nature of FMIs, institutions face significant exposure to these risks, which could have systemic implications.
   • Proposed Data Point: Introduce a field for “Operational/Cyber Risk Exposure,” where institutions rate the level of risk associated with each FMI (e.g., low, medium, high) and briefly describe any identified vulnerabilities (e.g., single points of failure, prior incidents).
   • Benefits: Including this data point would enable institutions to report their exposure to operational or cyber risks at each FMI, aiding in risk mitigation efforts and regulatory oversight focused on resilience against technology-driven disruptions.
6. Contractual Commitments and Notice Period for Termination with Each FMI
   • Current Limitation: The existing template does not provide visibility into the contractual terms governing the relationship with each FMI, particularly the notice period for termination. These terms affect the institution’s ability to transition to an alternative FMI if needed.
   • Proposed Data Point: Add fields for “Contractual Commitment” (e.g., term length, renewal conditions) and “Notice Period for Termination” (in days). These details would capture any constraints that could affect the institution’s flexibility in substituting FMIs.
   • Benefits: This data point would give regulators a clearer understanding of the institution’s ability to respond to disruptions or regulatory requirements for FMI transitions, helping assess whether continuity planning is constrained by contractual obligations.

Potential Risk Accounting Contribution

The Risk Accounting framework can support the integration of these additional data points by applying Risk Units (RUs) to quantify dependencies and exposures related to FMIs, adding a consistent, standardized measure to these new data fields. Here’s how RUs would enhance each suggested data point:

• Quantifying Transaction Volume and Value: RUs can aggregate exposure levels based on transaction volume and value, providing a dynamic measure of an institution’s dependency on each FMI. Higher RUs for FMIs with larger transaction volumes indicate a greater systemic risk, supporting more focused contingency planning.
• Assessing Criticality and Dependency on Specific Services: By assigning RUs to different service types (e.g., clearing, settlement), institutions can assess which FMI services are most critical, ensuring that resources for continuity planning are directed where they are most needed.
• Evaluating Operational and Cyber Risk Exposure: RUs can also be used to score FMI-related operational and cyber risks, providing a risk-adjusted measure that informs decisions on substitutability and resilience efforts for each FMI relationship.
• Incorporating Contractual Commitments: RUs can reflect the impact of contractual constraints on substitutability by adjusting risk exposure levels based on termination notice periods and other commitment factors. This enables institutions to quantify the impact of these constraints within their overall risk exposure profile.

iii. Are the instructions across Z09.01-Z09.04 sufficiently clear and detailed, and if not, what clarifications do you think are necessary and where?

Challenges:

The instructions for templates Z09.01-Z09.04, which address institutions' relationships with Financial Market Infrastructure (FMI) providers, cover critical areas of dependency, substitutability, and operational capacity.

However, certain aspects of these instructions would benefit from further clarification to ensure consistent interpretation and comprehensive reporting.

Below are specific areas within each template where additional guidance would enhance clarity and usability, along with recommendations for clarifications that could facilitate more effective reporting.

Template Z09.01: Overview of FMI Dependencies

Areas Needing Clarification

• Definition of “Dependency”
  • Current Limitation: The instructions lack a precise definition of “dependency,” particularly in terms of what constitutes a material dependency on an FMI provider. Institutions may interpret dependency differently, leading to inconsistencies in reporting across entities with varied operational and risk profiles.
  • Recommended Clarification: Provide a clear definition of dependency, including criteria or thresholds (e.g., dependency based on volume of transactions, criticality to business continuity). For example, dependency could be defined as any relationship where the FMI processes a specified percentage of the institution’s total transactions or provides unique services that cannot be easily substituted.
• Granularity of Data on Critical Services
  • Current Limitation: It is unclear if institutions should report all services provided by each FMI or only those deemed critical. This lack of specificity could lead to either underreporting or overly detailed submissions that may not provide actionable insights.
  • Recommended Clarification: Specify that only critical services should be reported and define “critical” in terms of the service’s impact on core functions or financial stability. Further, include examples of typical critical services (e.g., settlement, clearing for high-value transactions) to guide institutions in identifying which services to report.

Template Z09.02: Detailed Exposure to Individual FMI Providers

Areas Needing Clarification

• Exposure Measurement Criteria
  • Current Limitation: The instructions do not specify whether exposure should be quantified based on transaction volume, value, or both. Institutions may struggle to determine which metric provides a more accurate reflection of dependency on an FMI provider.
  • Recommended Clarification: Provide explicit guidance on how exposure should be measured, suggesting a standardized metric (e.g., daily average transaction volume and/or total transaction value over a specific period). Where applicable, specify that institutions may need to include both volume and value to fully capture exposure in cases where high-frequency, low-value transactions differ significantly from low-frequency, high-value transactions.
• Timeframe for Calculating Exposure
  • Current Limitation: The instructions lack guidance on the timeframe for calculating exposure (e.g., monthly, quarterly, annually), which can lead to inconsistencies, especially for institutions with seasonal transaction volumes.
  • Recommended Clarification: Define a standardized reporting period for exposure data, such as the average daily volume and value over the previous quarter. Providing a consistent timeframe would improve comparability across institutions, as all entities would report exposure based on a uniform timeframe, reflecting ongoing or recent dependency accurately.
• Treatment of Intraday Exposure
  • Current Limitation: For institutions that rely on FMIs for intraday services (e.g., intraday liquidity facilities), there is no guidance on how to account for exposure during the day, which may differ significantly from end-of-day totals.
  • Recommended Clarification: Include a requirement or optional field for reporting peak intraday exposure to capture the institution’s reliance on FMIs during the operational day. This would help assess the real-time risk posed by reliance on FMIs, which may be crucial for institutions with large intraday exposures.

Template Z09.03: Key Dependencies on Specific FMI Services

Areas Needing Clarification

• Identification of Specific Services and Service Categorization
  • Current Limitation: Instructions are limited regarding what qualifies as a “specific service” and how these should be categorized. Ambiguity around whether general categories like “clearing” and “settlement” suffice, or if more granular categorization is needed, can lead to inconsistent reporting.
  • Recommended Clarification: Provide a list of standardized service categories (e.g., clearing, settlement, custody, collateral management) with a definition of each, and require institutions to assign their FMI services to these categories. Alternatively, allow institutions to define services but provide examples to ensure that the level of detail meets regulatory expectations.
• Dependency Level Quantification for Each Service
  • Current Limitation: It is not clear if institutions should quantify the dependency level for each service, such as the proportion of total business that relies on the service, or describe dependencies qualitatively.
  • Recommended Clarification: Require institutions to report the dependency level for each service in quantifiable terms (e.g., percentage of total transactions processed, percentage of assets under custody) and suggest a range-based scoring system (e.g., low, medium, high) to ensure a uniform approach.

Template Z09.04: Substitutability of FMI Services

Areas Needing Clarification

• Definition and Criteria for Substitutability
  • Current Limitation: The concept of substitutability lacks specific criteria, making it challenging for institutions to assess whether an FMI provider can be replaced effectively in a short timeframe. This ambiguity may lead to subjective assessments, reducing comparability.
  • Recommended Clarification: Define substitutability in terms of criteria such as technical feasibility (e.g., compatibility of systems), transition cost, and timeframe required to switch to an alternative FMI provider. Provide a standard substitutability rating scale (e.g., “fully substitutable,” “partially substitutable,” “not substitutable”) with specific characteristics for each category to improve reporting consistency.
• Timeframe for Transitioning to an Alternative FMI
  • Current Limitation: There is no indication of the acceptable timeframe within which an institution should be able to switch to an alternative FMI if needed, which could result in inconsistent assessments of substitutability.
  • Recommended Clarification: Specify an expected timeframe for transition (e.g., within 24 hours, 1 week), depending on the service type or criticality level. For example, for services deemed critical to financial stability, a shorter, more immediate transition timeframe may be expected, while less critical services could allow longer transition periods.
• Operational and Financial Feasibility of Switching Providers
  • Current Limitation: Instructions do not require institutions to account for the operational or financial feasibility of switching to an alternative provider, even though high costs or significant operational challenges may effectively render a service non-substitutable.
  • Recommended Clarification: Instruct institutions to consider operational and financial feasibility when assessing substitutability. Additional fields could allow institutions to describe any prohibitive costs, regulatory restrictions, or technical challenges that impact their ability to substitute FMIs. This would lead to a more realistic view of substitutability, taking into account constraints beyond technical compatibility alone.

Potential Risk Accounting Contribution

The Risk Accounting framework, with its standardized Risk Unit (RU) metric, can support these clarifications and enhance the reporting process across templates Z09.01-Z09.04 by offering a quantifiable, consistent measure of dependency and substitutability. Here’s how Risk Accounting can integrate with the clarified instructions:

• Dependency Quantification: RUs can quantify dependency on each FMI based on transaction volumes, service criticality, and the institution’s exposure level. By applying a consistent RU-based scoring system, institutions can ensure that dependency measures align with the clarified definitions and criteria, supporting more accurate and comparable reporting.
• Standardized Substitutability Rating: Using RUs to create a substitutability rating can standardize the assessment process, enabling institutions to report substitutability based on quantifiable thresholds (e.g., high-cost and operationally challenging FMIs receive higher RU scores). This provides a structured approach to assess transition feasibility, aligning with the proposed timeframes and cost considerations.
• Clearer Operational and Financial Feasibility Metrics: By assigning RUs to potential transition barriers, such as high costs or regulatory constraints, Risk Accounting can capture a holistic view of substitutability. This RU-based measure supports detailed assessments of operational and financial feasibility, ensuring that institutions provide realistic and actionable insights into their ability to substitute FMI services.

i. Are the data-point definitions provided for reporting of the Granular Liability Data sufficiently clear? If this is not the case, for which data points would you require additional clarifications?

Challenges:

The data-point definitions provided for the reporting of Granular Liability Data are essential for accurately capturing the institution’s liability structure, particularly concerning critical aspects such as carrying amounts, outstanding amounts, maturity, and eligibility for bail-in.

However, several definitions and instructions could benefit from further specificity to ensure clarity and consistency across institutions. Below are key areas where further clarification would enhance data quality and comparability, along with specific suggestions to address potential ambiguities.

Key Areas Requiring Additional Clarification

• Distinction between Carrying Amount and Outstanding Amount
  • Current Ambiguity: The instructions do not clearly differentiate “carrying amount” from “outstanding amount” in cases where the two may vary due to accounting adjustments, accrued interest, or currency fluctuations. This ambiguity can lead to inconsistent reporting, especially for liabilities with complex structures or for instruments whose book and face values differ.
  • Recommended Clarification: Provide explicit definitions distinguishing carrying amount as the balance sheet value (net of any adjustments) and outstanding amount as the total principal due at maturity. Including specific guidance on handling instruments with accrued interest or foreign currency adjustments would reduce interpretation variability. An illustrative example for commonly complex liabilities, such as bonds issued at a discount or loans with accrued interest, could further clarify the distinction.
• Guidance on Maturity Categorization for Variable-Maturity Liabilities
  • Current Ambiguity: For liabilities with variable maturities, such as callable or puttable bonds, it is unclear whether institutions should report the first possible maturity date, the final maturity, or an expected maturity date based on historical redemption behavior. This lack of guidance creates potential inconsistencies, as different institutions may report maturity differently depending on their interpretation.
  • Recommended Clarification: Define a standardized approach to maturity categorization for variable-maturity instruments, specifying that either the earliest callable/puttable date or the final contractual maturity should be used consistently. Alternatively, introduce an “expected maturity” category where institutions can provide a maturity estimate based on average historical behavior, with clear instructions on when this approach is applicable.
• Eligibility for Bail-in and Inclusion of Liabilities in Bail-in Scope
  • Current Ambiguity: While the instructions require institutions to indicate which liabilities are eligible for bail-in, they do not specify criteria for determining eligibility, especially in cases where eligibility might be unclear due to cross-border regulatory considerations or complex contractual clauses.
  • Recommended Clarification: Offer specific criteria for determining bail-in eligibility, including examples of standard contractual clauses or regulatory conditions that affect eligibility. Further guidance on treatment of liabilities with conditional bail-in clauses or liabilities governed by foreign jurisdictions would improve consistency, ensuring that all institutions apply uniform eligibility standards.
• Segmentation of Liabilities by Counterparty Type
  • Current Ambiguity: The requirement to segment liabilities by counterparty type (e.g., financial institutions, corporate clients, retail clients) lacks clear definitions for each category, particularly for entities that may not fit neatly into one classification (such as small businesses that may be classified as either retail or corporate).
  • Recommended Clarification: Provide clear definitions for each counterparty category, possibly including criteria based on size, legal structure, or regulatory classification. Guidance on ambiguous cases, such as small businesses or mixed-use entities, would support consistent categorization, helping to ensure that institutions report liabilities uniformly across counterparty types.
• Accounting for Derivative Liabilities and Off-Balance Sheet Exposures
  • Current Ambiguity: The instructions are unclear regarding the reporting of derivative liabilities and off-balance sheet exposures, which can be complex due to their contingent nature and potential for rapid fluctuations. This lack of guidance could lead to significant inconsistencies, as some institutions might report these liabilities based on notional amounts, while others use mark-to-market values.
  • Recommended Clarification: Specify that derivative liabilities should be reported based on fair value or mark-to-market value, consistent with accounting standards, and clarify how off-balance sheet exposures should be handled (e.g., whether to include notional amounts or only recognized liabilities). Adding examples for commonly held derivatives, such as interest rate swaps or foreign exchange contracts, would further support clarity.
• Reporting of Liabilities with Multi-Currency Exposure
  • Current Ambiguity: For liabilities denominated in multiple currencies or those subject to currency translation adjustments, the instructions do not specify whether institutions should convert these liabilities into a single reporting currency or report each currency exposure separately. This ambiguity could result in differing reporting practices across institutions with multi-currency liabilities.
  • Recommended Clarification: Require institutions to report all liabilities in a single reporting currency (e.g., EUR), with guidance on the exchange rate (e.g., spot rate on reporting date or average rate over a period) to be used for conversion. Alternatively, allow multi-currency liabilities to be reported in their original currency alongside an aggregate converted value, ensuring that currency exposures are accurately reflected in the liability report.
• Liabilities with Embedded Options and Complex Contractual Terms
  • Current Ambiguity: The instructions do not address how to report liabilities with embedded options (e.g., conversion options, prepayment options), which can significantly affect the liability's value and maturity profile. Without guidance, institutions may take different approaches to valuing and categorizing these liabilities.
  • Recommended Clarification: Define a standardized approach for liabilities with embedded options, specifying whether the reporting should be based on the liability’s current book value, its notional amount, or an adjusted value that accounts for the embedded option. Additionally, guidance on categorizing these liabilities by maturity (e.g., first call date vs. final maturity) would improve consistency across institutions.

Potential Risk Accounting Contribution

The Risk Accounting framework, through the use of Risk Units (RUs), can enhance the reporting of granular liability data by providing a standardized, quantifiable metric for capturing non-financial risks associated with various types of liabilities. Here’s how RUs can contribute to addressing some of these specific clarifications:

• Standardized Measurement of Carrying and Outstanding Amounts: By applying RUs to quantify the risk exposure of each liability type, institutions can develop a consistent approach to measuring carrying and outstanding amounts, even for liabilities with fluctuating values. RUs provide a dynamic view that reflects current risk levels, minimizing the need for separate valuations.
• Uniform Reporting for Variable-Maturity Liabilities: The RU metric can be applied to assess risk associated with variable-maturity liabilities, providing a standardized approach to capturing early call or put risks. This method enables institutions to assess liability exposures based on expected maturity without the need for extensive adjustments.
• Consistent Bail-in Eligibility Assessment: By linking RUs to specific eligibility criteria, Risk Accounting offers a framework for determining bail-in eligibility consistently. Institutions can use RUs to capture risk exposures related to bail-in provisions, ensuring that only liabilities meeting predefined criteria are included.
• Clear Categorization for Counterparty Types and Embedded Options: RUs enable a structured approach to categorize liabilities by counterparty type and track liabilities with embedded options by quantifying the risk exposure related to each. This categorization aligns with regulatory expectations, providing a consistent view of liability data that supports regulatory comparisons.