Response to consultation on Draft Guidelines on the methodology to estimate and apply credit conversion factors under the Capital Requirements Regulation

1. Question 1: How material are the cases for your institution where you would have to assign an SA-CCF to exposures arising from undrawn revolving commitments and thus restrict the use of own estimates of LGDs within the scope of application for IRB-CCF in the CRR3? For which cases would you not have enough data to estimate CCFs but have enough data to es-timate own estimates of LGDs?

We understand the EBA’s intention to assess practical and methodological implications under this requirement.

Challenges under the Current Framework

The current approach presumes that borrower drawdown behaviour, particularly under stress, can be reliably captured through statistical estimation based on historical exposure data. However, this approach assumes borrower behaviour is entirely exogenous, overlooking the possibility that internal factors within the bank may interact with external conditions and contribute to changes in utilization patterns. In reality, factors such as abrupt policy shifts, internal operational incidents, reputational deterioration, or inconsistencies in credit delivery often serve as triggers for borrower drawdowns. These drivers, which may include internal operational, conduct, or reputational factors, as well as external influences such as macroeconomic volatility, regulatory changes, or shifts in governmental policy, often lie outside the scope of traditional credit risk modelling.

Where institutions lack sufficient historical drawdown data, especially for legacy or low-volume portfolios, this approach offers no reliable alternative beyond the fallback to SA-CCF, despite the potential availability of robust LGD models. This disconnect limits risk sensitivity and may penalize institutions that have the capability to identify internal factors contributing to utilization behaviour but cannot reflect them in regulatory models.

How Risk Accounting Can Help

In this way, risk accounting would enable institutions to move from reactive modelling based solely on historical borrower behaviour to anticipatory insight based on their own risk posture. In circumstances where CCF data is insufficient but LGD estimation remains viable, RUs could act as structured proxies for drawdown probability, offering supervisory confidence in the reliability of modelled exposures.

In a hypothetical application, an institution with a risk accounting framework in place might detect elevated RUs in specific product lines or business units due to deteriorating internal controls or operational pressure. These risk signals could indicate a higher likelihood of borrower drawdowns in those areas, prompting closer calibration of CCF assumptions or pre-emptive mitigation.

We propose that institutions with mature risk accounting frameworks be permitted to incorporate RU-based indicators as part of their CCF estimation, particularly where traditional data is lacking. This approach would not only enhance model accuracy but align capital requirements more closely with actual risk conditions, reducing over-reliance on conservative SA-CCFs that may not reflect true exposure potential.

The supporting attachment provides technical details and hypothetical illustrations demonstrating how residual non-financial risk quantification through risk accounting could augment CCF estimation in data-constrained environments.

2. Question 2: Do you have any comments related to guidance on the identification of a relat-ed set of contracts which are connected such that they constitute a facility?

We understand the EBA’s intention to assess practical and methodological implications under this requirement.

Challenges under the Current Framework

The current guidelines lean heavily on legal or formalistic criteria for defining a facility, which may not always reflect how contracts function in practice. Many banking relationships are governed by operational, behavioural, or credit risk management linkages that bind contracts together functionally, even if they appear independent on paper.

As a result, exposures may be under- or overestimated if these relationships are not captured, particularly in cases involving dynamic limits, collateralized overdrafts, or linked standby facilities. The current approach may also fail to recognize that operational failures or inconsistencies across related contracts can trigger drawdowns in others, especially during times of stress.

How Risk Accounting Can Help

A risk accounting framework enables institutions to identify and track operational interdependencies across contracts by mapping them to shared internal processes, controls, and risk ownership structures. For example, different contracts serviced under a single credit management system or reliant on the same limit monitoring function could be associated with a common set of residual risks.

Using Risk Units (RUs) as a measurement tool, banks could assess concentrations of non-financial risk that affect multiple contracts simultaneously. This makes it possible to proactively identify clusters of exposures that should be treated as a facility from a risk perspective, even if legal documentation does not compel aggregation.

In a hypothetical case, a bank applying risk accounting might observe a rise in RUs linked to a shared servicing function across multiple client agreements. This insight would suggest the need to reassess how those agreements are grouped for CCF purposes, as a common operational vulnerability could affect the drawdown likelihood of all linked contracts.

We therefore propose that institutions be encouraged to incorporate risk-based assessments (including indicators derived from risk accounting) into their facility definition methodologies. This would strengthen the alignment between credit exposure modelling and the true sources of systemic or correlated drawdown risk.

3. Question 3: Do these GL cover all relevant aspects related to the definition of revolving commitments that you consider relevant for the scope of the IRB-CCF? Have you identified any product that should be in the scope of the IRB-CCF that is currently excluded in the GL? In terms of off-balance sheet exposures, how material are the exposures that fall within the defined scope of the IRB-CCF for your institution?

We understand the EBA’s intention to assess practical and methodological implications under this requirement.

Challenges under the Current Framework

The current guidance appears to emphasize formal characteristics (e.g. ability to re-borrow after repayment, or the absence of fixed repayment schedules) when defining revolving commitments. However, this may overlook the functional realities of certain products that behave as revolving in practice, even if they do not meet the strict definitional criteria.

For example, facilities such as:

• Overdrafts with intermittent zero balances,
• Securities-backed credit lines that reset based on market values,
• Supplier or trade finance arrangements linked to fluctuating invoice volumes,

may all involve drawdown behaviours that are economically similar to traditional revolvers. Excluding them may create blind spots in exposure modelling and risk-based capital allocation.

Furthermore, the current approach may not fully capture how institutions manage these exposures operationally. For instance, credit lines embedded within broader commercial relationships may not be tracked or reported as discrete revolvers, despite functioning as such.

How Risk Accounting Can Help

A risk accounting framework enables institutions to detect and quantify exposure volatility tied to internal and external process factors, regardless of formal product classification. By focusing on how credit availability interacts with operational control environments, it helps identify products or arrangements that should be treated as revolving from a risk standpoint, even if they fall outside a narrow regulatory definition.

Through the use of Risk Units (RUs), institutions can trace drawdown risk to specific drivers such as credit operations breakdowns, inconsistent collateral monitoring, or business model pressures. These signals provide a more accurate and dynamic lens for determining whether a facility exhibits revolving characteristics and should therefore be brought within the IRB-CCF scope.

Hypothetically, a bank using risk accounting may find elevated RUs tied to a category of supplier finance agreements that experience frequent fluctuations in usage tied to procurement cycles. This would justify treating them as economically revolving, even if the contractual language does not explicitly define them as such.

We recommend that the guidance allow for a functional overlay, where institutions can, with justification, treat certain product types as revolving for IRB-CCF purposes based on operational behaviour and risk profile. Risk accounting provides a disciplined framework for supporting such determinations in a traceable and supervisory-auditable way.

4. Question 4: Are there products that have an advised limit of zero but a nonzero unadvised limit that should be included in the scope of the IRB-CCF GL? How material are these cases for your institution?

We understand the EBA’s intention to assess practical and methodological implications under this requirement.

Challenges under the Current Framework

Under the prevailing regulatory treatment, the absence of a formal, advised limit may result in these products being excluded from IRB-CCF modelling altogether. This creates a disconnect between credit risk reality and regulatory exposure modelling, especially if borrowers understand and act upon implicit drawdown capacity.

Moreover, the current treatment overlooks the fact that even products lacking a documented advised limit still consume operational capacity, expose the institution to drawdown behaviour, and reflect a form of contingent credit risk.

Crucially, such exposures are often left out not because they are immaterial, but because institutions lack visibility into the operational rules, behavioural triggers, or internal controls governing their actual usage.

How Risk Accounting Can Help

Risk accounting introduces a structured mechanism for identifying and quantifying the residual non-financial risks that can result in implicit drawdown permissions being activated. It allows institutions to analyse whether the lack of a formal advised limit is offset by operational behaviours, systems configurations, or client servicing practices that effectively allow access to credit.

By assigning Risk Units (RUs) to internal processes associated with these facilities, such as limit monitoring, exception handling, or manual overrides, a bank could identify conditions under which unadvised drawdowns are likely to occur. This supports the case for including such products in IRB-CCF scope where warranted.

For example, a hypothetical institution using risk accounting may find elevated RUs in its current account operations function due to inconsistencies in overdraft handling and exception approvals. These risk signals, even without a formal credit line, would indicate the presence of implicit exposure potential that should be reflected in CCF modelling.

We therefore recommend that the IRB-CCF guidance explicitly recognize products with zero advised but nonzero unadvised limits as potential candidates for inclusion, subject to documented analysis. A risk accounting framework can provide the basis for such assessments in a structured and auditable way.

5. Question 5: Do you think that dynamic limits (e.g. limits the extent of which is dependent on the market value of financial collateral pledged by the obligor in relation to the revolving loan) warrant a specific treatment in the IRB-CCF GL? How material are these cases for your institution?

We understand the EBA’s intention to assess practical and methodological implications under this requirement.

Challenges under the Current Framework

The standard IRB-CCF methodology presumes either static credit limits or predictable exposure profiles. This creates tension when applied to facilities with dynamic limits, where exposures may change frequently, even daily, due to collateral price movements or internal valuation practices.

This introduces several issues:

• Volatility in exposure: The actual drawdown capacity may increase sharply during benign market conditions, then contract during stress, often in a non-linear way.
• Triggering behaviour: Declines in collateral value can trigger preemptive drawdowns by borrowers who anticipate further reductions in access.
• Operational dependencies: These products rely heavily on accurate, timely collateral monitoring and limit recalibration processes, which are vulnerable to failure during periods of stress.

The existing regulatory treatment may fail to reflect these features, leading to underestimation of risk in benign times and reactive responses during downturns.

How Risk Accounting Can Help

Risk accounting enables institutions to assess not only the economic exposure but also the operational fragility that accompanies dynamic limit structures. Through the assignment of Risk Units (RUs), banks can track control dependencies such as real-time collateral valuation, margin call processing, and client communication protocols, all of which influence drawdown patterns.

In a hypothetical example, a bank might identify elevated RUs associated with its collateral operations unit, reflecting overstretched limit recalibration processes or delays in issuing margin calls. This would signal an increased probability of drawdown events linked to control failures, even if the collateral appears sufficient on paper.

Furthermore, risk accounting can support the proactive classification of certain dynamically limited products as conditionally revolving, based on both their legal design and operational risk profile. This could justify a more differentiated treatment in IRB-CCF estimation and help prevent systemic blind spots in capital planning.

We therefore recommend that the IRB-CCF GL include specific provisions for dynamically limited products and encourage institutions to use risk-based evidence, including risk accounting metrics, to determine whether these products warrant distinct exposure modelling.

6. Question 6: Have you identified any unwarranted consequences of including fully drawn revolving commitments in the scope of the IRB-CCF. How material are these cases for your institution?

We understand the EBA’s intention to assess practical and methodological implications under this requirement.

Challenges under the Current Framework

Including fully drawn revolving commitments within the CCF framework may introduce several unintended consequences, particularly when applied mechanistically:

False signals of exposure volatility: A fully drawn facility that has no legal or practical capacity for re-draw may be inappropriately subjected to CCF modelling, resulting in artificial exposure inflation.

Overlapping frameworks: These facilities may already be covered under other risk measurement regimes (e.g. credit or maturity risk), leading to potential double-counting of risk.

Inflexibility in boundary treatment: The boundary between drawn and undrawn may shift frequently in practice, especially with working capital lines that fluctuate near full utilization. Without a nuanced understanding of operational triggers, rigid classification may misrepresent actual exposure dynamics.

How Risk Accounting Can Help

Risk accounting offers an alternative path to resolve this ambiguity by allowing institutions to classify revolving facilities based on operational continuity and process-driven exposure potential, rather than nominal usage status alone.

Through Risk Units (RUs), banks can identify whether fully drawn facilities continue to pose contingent risk due to factors like:

• Automatic renewal policies,
• Client expectations of limit reinstatement,
• Inconsistent limit enforcement practices,
• Historical patterns of drawdown–repayment cycles.

For example, a bank may determine, through elevated RUs in its credit administration process, that certain fully drawn revolving facilities tend to be reinstated immediately upon repayment (e.g. intraday working capital lines). In this case, their treatment as static exposures would understate the true risk behaviour.

Conversely, if RUs remain stable and low, it would support the exclusion of those facilities from IRB-CCF scope, even if they are technically revolving, because they no longer functionally behave as such.

We recommend that institutions be allowed to differentiate fully drawn revolving facilities based on operational behaviour and risk signals. Risk accounting provides a transparent, quantifiable, and auditable methodology to support this distinction, minimizing both overstatement and understatement of exposure risk.

We include here the answer to Question 7 (as it appears to be missing from the submission form):

Question 7: Do you have any concerns about the introduction of the notion of the different samples that constitute the RDS for CCF estimation? Do you have a modelling practice implemented that deviates from this approach?

We understand the EBA’s intention to assess practical and methodological implications under this requirement.

Challenges under the Current Framework

While the intention behind differentiated RDS samples is to improve statistical soundness, this layered structure may present several limitations in practice:

• Data fragmentation: Splitting the RDS into multiple categories risks diluting sample sizes, especially for portfolios with low default rates or limited historical records.
• Overhead in governance: Maintaining consistent rules across all subsets of the RDS can introduce complexity in data governance, lineage, and documentation — potentially diverting focus from model substance to compliance form.
• Reduced modelling flexibility: Institutions that rely on more holistic or integrated modelling approaches may find the rigid delineation of sample types restrictive, particularly where behaviours (e.g. post-default drawdowns) span multiple data windows.

In some cases, institutions may already be using merged or smoothed datasets where separation is not meaningful or introduces instability.

How Risk Accounting Can Help

Through Risk Units (RUs), institutions can detect when certain portfolios or exposure types are behaving atypically — such as unusually high draw activity during specific periods — regardless of whether those samples fall into the "observation" or "calibration" subsets under the EBA schema.

For example, a bank might observe heightened RUs linked to control breakdowns in a specific origination channel that coincide with anomalies in drawdown patterns. This insight would inform CCF calibration regardless of the sample's formal classification, providing greater continuity and real-time relevance.

If institutions are allowed to incorporate operational risk-based overlays — such as those provided by risk accounting — into their modelling governance, they may find less need for rigid sample segmentation. The objective of robust and representative estimation can be achieved through real-world behavioural validation, rather than just statistical partitioning.

We therefore suggest that the EBA consider flexibility in the application of RDS segmentation, where institutions can demonstrate alternative, risk-based governance mechanisms that provide equivalent or superior assurance on model integrity.

8. Question 8: Are there cases for your institution where the calibration samples should be shorter than the sample used to calculate the long run average (LRA) CCF

We understand the EBA’s intention to assess practical and methodological implications under this requirement.

Challenges under the Current Framework

While a long-run average supports capital stability and comparability, using it uniformly can overlook meaningful recent changes in drawdown behaviour, particularly in the presence of:

• Material shifts in product design or contractual structures,
• Enhanced risk mitigation processes (e.g. limit tightening, automatic blocks),
• Significant external factors (e.g. macroeconomic shocks or regulatory changes) that break historical continuity.

The current framework does not always provide institutions with enough flexibility to account for these changes unless the calibration and LRA samples are decoupled — which the EBA is now exploring.

However, if institutions shorten the calibration sample without proper justification, this could introduce model instability or selectively omit downturn data, undermining conservatism.

How Risk Accounting Can Help

Risk accounting provides a disciplined and auditable means to justify the need for a shorter calibration window, grounded not just in statistical diagnostics but in measurable shifts in operational risk exposure.

Similarly, where RUs spike due to emerging control issues or external stressors, risk accounting can flag the need to revert to a longer window or overlay conservatism, even if recent data seems benign.

This approach avoids opportunistic cherry-picking of calibration samples and grounds modelling decisions in real changes to risk-bearing capacity.

We therefore support the flexibility to use shorter calibration windows where institutions can demonstrate, via structured frameworks such as risk accounting, that the change reflects genuine operational or portfolio transformations, rather than noise or model optimization.

9. Question 9: Do you have any concerns with the requirements introduced to analyse and mitigate a lack of representativeness for CCF? Do the requirements on the different data samples when observing a lack of representativeness impede your ability to model CCF portfolios?

We understand the EBA’s intention to assess practical and methodological implications under this requirement.

Challenges under the Current Framework

The proposed requirements, while logically sound, may become problematic when:

• Portfolios evolve faster than data can accumulate (e.g. due to digitization or market shifts),
• Segments with historically low draw activity lack materiality thresholds but are still included in full-scope requirements,
• Institutional changes in underwriting, risk appetite, or control environments are not reflected in legacy data.

Moreover, strict adherence to representativeness diagnostics may create modelling gridlock, where institutions are neither permitted to use internal estimates due to limited RDS coverage, nor able to meaningfully calibrate fixed CCFs for diverse or low-volume subsegments.

How Risk Accounting Can Help

Using Risk Units (RUs), institutions can identify which portfolios are exposed to similar operational risk structures — even when their historical draw behaviours differ. This can support justifiable grouping or extrapolation in CCF modelling.

For example, if two retail product lines differ in vintage or volume but share identical onboarding, monitoring, and limit management processes (as reflected in comparable RU patterns), their risk of future drawdowns may be functionally equivalent — supporting pooled calibration or a shared conservative overlay.

Conversely, a portfolio segment may appear statistically representative but be undergoing rising control fragility (reflected in rising RUs), undermining its usefulness as a calibration anchor.

We recommend that the EBA allow institutions to incorporate operational risk-based diagnostics — including risk accounting — into their methodology for assessing representativeness, especially for evolving portfolios or emerging products where statistical adequacy may lag behind risk insights.

10. Question 10: Do you have any concerns with linking the fixed CCF to the lack of historical data available to the institution in relation to the coverage by the RDS of material subseg-ments of the application portfolio? How is your institution currently treating these cases?

We understand the EBA’s intention to assess practical and methodological implications under this requirement.

Challenges under the Current Framework

While fixed CCFs offer a safeguard against underestimation in low-data environments, this approach can introduce the following concerns:

• Disincentivizing innovation: Emerging products or delivery channels may face punitive capital treatment during their ramp-up period, not due to intrinsic riskiness, but simply because of data immaturity.
• Inconsistent application: Institutions with similar product lines but different data histories may face uneven capital requirements, despite comparable real-world exposures.

How Risk Accounting Can Help

Risk accounting presents a compelling complement to data-based thresholds by introducing an operational risk visibility layer that can either support or challenge the need for fixed CCF treatment.

For instance, where historical data is lacking, institutions can assess the risk-bearing capacity of key processes — such as onboarding, limit-setting, or monitoring — using Risk Units (RUs). A portfolio with no historical default data but consistently low RU levels across relevant functions may not warrant full fixed CCF conservatism. On the other hand, if RUs show rising fragility, then the use of fixed CCF would be justified, even if basic statistical indicators appear benign.

This operational lens enables more tailored application of fixed CCF rules, avoiding both undue penalization and blind optimism.

In practice, risk accounting could be used as an interim governance mechanism for newly launched products or legacy segments lacking data. It ensures that capital treatment remains aligned with underlying risk signals, bridging the gap until statistical models can be developed or validated.

We propose that the EBA consider allowing institutions to substitute or supplement statistical thresholds with structured operational risk assessments, like those offered through risk accounting, to determine the necessity and scope of fixed CCF application.

11. Question 11: Are there any concerns with requiring consistency in the analysis of changes in the product mix with the institution’s definition of facility? Are institutions able to identify and link contracts (partially) replacing other contracts where the closing or repayment of one contract is related to the origination of a new contract? Are institutions able to link new contracts that are originated after the reference date to related contracts existing at refer-ence date? In particular, is it possible in the case contracts that are revolving commitments are replaced by contracts that are non-revolving commitments (e.g. by a term loan)?

We understand the EBA’s intention to assess practical and methodological implications under this requirement.

Challenges under the Current Framework

In theory, contract-level linkage across facility replacements should support more precise modelling. In practice, however, this assumption encounters several issues:

• Data infrastructure gaps: Legacy systems often treat contracts in isolation, lacking metadata or relational tracking between originating and replacing facilities.
• Behavioral ambiguity: The intent behind replacing a revolving facility with a term loan may not always be clear — is it strategic refinancing, early crystallization of risk, or operational consolidation?
• Overlapping obligations: In some cases, old and new facilities coexist temporarily, further blurring the relationship and confusing model logic.

These complications can result in under- or overestimation of CCFs depending on whether credit lines are treated as fully drawn, replaced, or partially available.

How Risk Accounting Can Help

Risk accounting enhances visibility into such transitions by capturing the operational rationale and control environment surrounding product replacement events.

• Through the lens of Risk Units (RUs), institutions can detect when a replacement arises from:
• An internal policy shift (e.g. replacement of overdraft products due to tightened controls),
• Client-specific concerns (e.g. borrower deterioration prompting restructuring),
• Broader systemic developments (e.g. a shift away from revolving credit in a market segment).
• Additionally, RU trends can help validate whether the replacement results in reduced, unchanged, or heightened exposure risk — supporting more accurate CCF adjustment.

In practical terms, institutions could use RU insights to flag when a "new" term loan is, in effect, a crystallization of undrawn revolving exposure. This would guide more appropriate calibration of realized CCF, even in the absence of perfect contract tracing.

12. Question 12: Do institutions consider it proportionate to the risks of underestimation of CCF to perform the identification analysis and allocation procedure? If it is deemed not propor-tional, what would be an alternative approach that is still compliant with Article 182(1b) CRR?

We understand the EBA’s intention to assess practical and methodological implications under this requirement.

Challenges under the Current Framework

While the allocation procedure promotes consistency and conservatism, it can create excessive operational burden when:

• Portfolios involve high-volume, low-risk retail products, where the cost of granular identification outweighs the risk of underestimation.
• The institution's internal controls and early-warning mechanisms already ensure conservative drawdown assumptions, making detailed allocation analysis redundant.
• The segmentation process introduces modelling noise or data manipulation risks in an attempt to meet the procedural requirement, rather than improving actual risk capture.

How Risk Accounting Can Help

Risk accounting provides a compelling risk-sensitive alternative that aligns well with the intent of Article 182(1b). Instead of relying on identification and allocation exercises that may be more about process compliance than real risk differentiation, institutions could apply a Risk Unit (RU)-based approach that:

• Dynamically measures the operational risk profile across facilities,
• Flags early signs of control deterioration or overextension,
• Supports proportional adjustments to CCF estimates based on actual risk-bearing capacity.

For instance, if a subsegment exhibits consistently low RU intensity and stable performance, it could be exempted from extensive allocation exercises, while portfolios with higher RU volatility or control fragility would trigger deeper allocation and validation efforts.

This method allows institutions to triage their efforts — focusing analytical resources where the risk of underestimation is highest, while maintaining overall compliance and capital adequacy.

We propose that the EBA consider recognizing structured operational risk frameworks — such as risk accounting — as a valid basis for determining when full allocation procedures are proportionate, and when simplified, risk-based alternatives may be more effective.

13. Question 13: Do you have any concerns on the proposed approach for the treatment of so-called ‘fast defaults’? In case you already apply a 12-month fixed-horizon approach, do you apply a different treatment for ‘fast defaults’ in practice, (and if so, which one)? Is the ‘fast default’ phenomenon material according to your experience? If yes, for which exposures, exposure classes or types of facilities?

We understand the EBA’s intention to assess practical and methodological implications under this requirement.

Challenges under the Current Framework

The standard approach risks misrepresenting the drawdown profile of exposures that default shortly after origination, for several reasons:

• Low utilization window: These defaults often occur before the borrower has the opportunity to draw significantly, potentially understating the “true” CCF.
• Heterogeneity of causes: Fast defaults may result from fraud, systemic onboarding failures, or unexpected borrower distress — each with different risk implications.
• Over-adjustment risk: Applying generic treatments across all early defaults may obscure important distinctions or lead to overconservatism.

Moreover, the fast default phenomenon may not be material across all portfolios, raising the question of whether a uniform approach is justified.

How Risk Accounting Can Help

By analysing Risk Units (RUs) prior to and at origination, institutions can determine whether the early default reflects:

• A breakdown in internal control effectiveness (e.g. flawed credit screening or accelerated product issuance),
• An external stressor (e.g. macroeconomic disruption),
• A borrower-initiated adverse event (e.g. fraudulent application or misrepresentation).

Furthermore, by embedding fast default risk into the RU profile of new exposures, institutions could flag high-risk originations before default manifests, enabling earlier intervention and potentially altering drawdown dynamics.

We recommend that the EBA allow for operational risk-informed segmentation of fast defaults using frameworks such as risk accounting. This would allow institutions to avoid blanket conservatism and instead apply risk-sensitive adjustments that reflect the real drivers behind early defaults.

14. Question 14: Do you have any concerns on the multiple default treatment? To what extent are your current models impacted by the application of a multiple default treatment?

We understand the EBA’s intention to assess practical and methodological implications under this requirement.

Challenges under the Current Framework

While the rationale for distinguishing and consolidating multiple defaults is clear, the practical implementation poses several challenges:

• Ambiguity in defining separation vs. continuation: It is not always clear whether subsequent defaults are new events or continuations of a previous unresolved situation, especially when the return to non-default status is brief or technical in nature.
• Data fragmentation: Multiple default paths may be captured across different systems or periods, complicating historical tracking and creating a risk of inconsistent CCF attribution.
• Inflated drawdown attribution: Aggregating multiple defaults without proper segmentation could lead to exaggerated CCF estimates if additional drawings are misaligned with actual risk events.

Moreover, these treatments may disproportionately affect portfolios with high borrower turnover, short maturity products, or jurisdictions where technical defaults are more common.

How Risk Accounting Can Help

Through the use of Risk Units (RUs), institutions can assess whether a new default represents:

• A genuinely new risk emergence following successful risk mitigation,
• A continuation of residual risk that was never adequately resolved, or
• A recurring pattern due to process weaknesses (e.g. poor restructuring quality or misaligned incentives).

By analysing the evolution of RU intensity across the default-recovery-default cycle, institutions can better discriminate between episodic vs. persistent risk exposure, enabling more accurate drawdown attribution.

Risk accounting can also serve as a control layer to identify and mitigate structural vulnerabilities in high-frequency default portfolios, helping prevent overestimation of CCF in portfolios where recurring defaults are often administrative or operational in origin.

We propose that the EBA allow institutions to supplement multiple default analysis with operational diagnostics, such as RU path analysis, to refine attribution of drawdowns and avoid distortion of realized CCF metrics due to data or model rigidity.

15. Question 15: Do you agree with the three principles for the calculation for realised CCF in the context of consumer product mix, and their implications for the cases mentioned as ex-amples? In case of disagreement, what is the materiality of the cases with unwarranted re-sults, in particular in relation with the definition of facility applied in your institution? In case of material unwarranted results, can you describe your alternative practice to this CP?

We understand the EBA’s intention to assess practical and methodological implications under this requirement.

Challenges under the Current Framework

While the three principles aim to ensure consistency, several issues may arise when applying them to consumer portfolios:

• Blurring of facility boundaries: Customers often interact with credit limits across products (e.g. revolving cards, overdrafts, credit lines) in ways that blur legal or operational distinctions between facilities.
• Misleading separation: Treating each facility as independent for CCF purposes may distort reality when customer behaviour is influenced by their total available credit — not by individual limits.
• Distorted attribution: The current approach may lead to unwarranted results, such as artificially high or low realized CCFs, simply because of technical classification, rather than actual changes in exposure behaviour or risk.

In such environments, the rigid application of the principles risks undermining the purpose of CCF estimation — which is to understand real exposure evolution under credit stress.

How Risk Accounting Can Help

Risk accounting provides a viable alternative by reframing consumer exposure modelling around behavioural and operational risk realities, rather than product technicalities.

Through the use of Risk Units (RUs), institutions can:

• Identify control dependencies across products — such as centralized decision-making for credit availability, or automated drawdown constraints,
• Detect behavioural patterns across facilities that signal coordinated or risk-significant utilization behaviours,

For example, if a customer has three interlinked facilities that are managed under a single credit policy and monitored via the same internal risk control infrastructure, it may be more accurate to treat them as a unified risk environment — even if legal documentation separates them.

By aligning the definition of "facility" to actual risk-bearing structures rather than contractual silos, risk accounting can help avoid CCF distortions and improve model integrity.

16. Question 16: Are there any concerns related to the allocation mechanism described in these GL?

We understand the EBA’s intention to assess practical and methodological implications under this requirement.

Challenges under the Current Framework

While the allocation mechanism is intended to improve consistency and minimize underestimation, it may introduce distortions, particularly in institutions with:

• Overlapping credit structures, where exposures are linked via shared limits, guarantees, or operational management,
• Legacy systems, where data granularity does not support precise mapping of drawdowns to specific facilities,
• Heterogeneous consumer portfolios, where behavioural drivers (e.g. convenience, automation, or marketing nudges) determine the source of drawdowns more than contractual arrangements.

In such cases, allocation rules may end up either overly conservative or misaligned with actual usage patterns, especially when applied rigidly across different business models or jurisdictions.

How Risk Accounting Can Help

Risk accounting provides an opportunity to anchor the allocation process in real operational dynamics, rather than purely contractual or technical assignment rules.

Using Risk Units (RUs), institutions can:

• Understand which facilities are more operationally exposed, based on deteriorating internal control environments or weak governance,
• Attribute drawdowns not just based on proximity to default, but on the progression of internal risk drivers across linked exposures,
• Introduce a layer of risk-based prioritization in the allocation logic, ensuring that drawdowns are assigned where the risk-bearing capacity is lowest or the controls are weakest — and not merely where a limit happens to be available.

For example, where a borrower draws funds across two facilities, both within a unified client relationship and monitored by the same operational controls, RU tracking can reveal that one facility is bearing more latent risk due to deteriorating process performance — making it the more appropriate allocation target for capital estimation.

We suggest the EBA allow institutions to enhance the allocation mechanism with internal operational diagnostics like RU assessments, particularly where the contractual allocation approach diverges from the institution’s actual risk-taking and management structures.

17. Question 17: Where credit lines are kept open even if the facility is in default, the alterna-tive option described in this consultation box could lead to high realised CCF values. Is this a relevant element for your institution and if yes, why and how material are these cases with-in the scope of IRB-CCF models?

We understand the EBA’s intention to assess practical and methodological implications under this requirement.

Challenges under the Current Framework

Including post-default drawings in CCF calculations may reflect true economic exposure, but risks overstating risk inappropriately where such drawdowns:

• Occur under exceptional or unintended operational scenarios, such as delayed system flags or contractual oversights,
• Are permitted only under highly controlled conditions, such as for funding restructuring or covering mandatory obligations,
• Represent non-material exposures that nevertheless inflate calculated CCFs due to mechanical aggregation.

Moreover, the approach does not distinguish between institutions that actively monitor and restrict post-default access and those where such activity is a sign of weak risk controls.

How Risk Accounting Can Help

Risk accounting offers a more risk-sensitive framework to interpret and manage post-default drawdowns.

Through RU analysis, institutions can:

• Determine whether post-default drawings reflect a breakdown in internal control — for example, where failed escalation or system override permitted access despite default,
• Identify the purpose and governance surrounding such drawings, such as whether they were part of a formal recovery plan or occurred due to gaps in oversight,
• Monitor whether defaulted facilities were structurally exposed to further drawdowns due to lax facility configuration or product design flaws.

This insight allows institutions to segregate drawdowns that are risk-relevant from those that are system artifacts, ensuring that realized CCF calculations remain proportionate and traceable to actual risk.

18. Question 18: In case of multiple defaults, the CCF might also be driven by drawings while the obligor was in its default probation period or in the dependence period between the merged defaults. Do you expect this to be material for your CCF models?

We understand the EBA’s intention to assess practical and methodological implications under this requirement.

Challenges under the Current Framework

Treating such drawdowns uniformly within realized CCF calculations may introduce the following challenges:

• Failure to distinguish between structural control weaknesses and exceptional draw allowances (e.g. for recovery facilitation),
• Difficulty in ensuring consistency when the delineation between defaults and probation periods is subject to judgment or weak governance.

As a result, CCF models may overestimate drawdown behaviour where controls are, in fact, in place — or underestimate where ineffective control environments permit further exposure.

How Risk Accounting Can Help

Risk accounting enables institutions to assess the quality of their operational controls during these transitional periods. By tracking Risk Units (RUs) over time, institutions can:

• Evaluate whether residual internal control failures continue post-default, increasing the likelihood of further drawdowns,
• Differentiate between planned or sanctioned drawings (e.g. for collateral management or scheduled obligations) and uncontrolled access,
• Use RU trajectories to inform whether the second default should be treated as an extension of the first or a new episode, thus aligning draw attribution more closely with real risk emergence.

This operationally grounded view can materially improve the interpretability of CCF models by:

• Flagging unjustified drawdowns as potential indicators of governance lapses,
• Avoiding inflated capital charges when controls effectively prevented further exposure,
• Supporting more proportional regulatory responses to the risk observed.

We recommend that institutions be allowed to incorporate operational diagnostics such as risk accounting when assessing drawdowns during transitional default periods. This would enhance the risk sensitivity and accuracy of CCF estimation.

19. Question 19: Do you see any unwarranted consequences of the proposed approach for in-corporating additional drawings after default? In particular, in order to maintain consistency between the realised CCF calculation and the calculation of the denominator of the realised LGD as described in paragraph 140 of the GL PD and LGD, would this require a redevelop-ment of your LGD models?

We understand the EBA’s intention to assess practical and methodological implications under this requirement.

Challenges under the Current Framework

Integrating additional drawings into both CCF and LGD estimation is conceptually consistent but may present challenges in practice:

• Where post-default drawings are driven by operational errors, they may distort both CCF and LGD estimates if mechanically included,
• Models calibrated historically may not account for such drawdowns, potentially requiring redevelopment of LGD systems,
• Institutions may have difficulty distinguishing between intentional recovery-related financing and unsanctioned exposure increases, particularly if data or controls are lacking.

Furthermore, institutions that already manage post-default exposure separately — for example, by ring-fencing recovery strategies — may find the integration creates artificial volatility in both CCF and LGD outcomes.

How Risk Accounting Can Help

Risk accounting enhances the quality and interpretability of this alignment by embedding operational insight into the treatment of post-default drawings.

Using Risk Units (RUs), institutions can:

• Identify the source and risk attribution of post-default drawings, differentiating governance failures from legitimate risk-managed actions,
• Track the internal control environment across the default timeline, indicating whether further exposure was a function of control degradation or pre-planned resolution activity,
• Segment post-default drawdowns into risk-significant vs. operationally neutral categories, informing whether they should impact LGD model redevelopment.

This allows institutions to make targeted model adjustments rather than undertake full redevelopment. For example, rather than rebuilding LGD models entirely, institutions could adopt an overlay or separate component calibrated for post-default RU activity — particularly where operational evidence indicates that such drawings are rare, controlled, or policy-bound.

We encourage the EBA to clarify that redevelopment is not required in all cases and to allow institutions to document and justify the treatment of post-default drawings using operational frameworks such as risk accounting. This would support both supervisory transparency and modelling proportionality.

20. Question 20: Do you think that the relative threshold is an appropriate approach to restrict the use of the alternative CCF approach for those facilities in the region of instability? Do you think it is appropriate to define a single relative threshold per rating system or are there circumstances where multiple relative thresholds would be warranted? Do you see a need to use an absolute threshold in addition to the relative thresholds?

We understand the EBA’s intention to assess practical and methodological implications under this requirement.

Challenges under the Current Framework

While a relative threshold may offer some flexibility across portfolios and institutions, there are concerns about its application:

• Relative thresholds can be sensitive to model volatility or historical calibration periods, potentially triggering unwarranted restrictions or approvals,
• The absence of absolute boundaries might allow materially risky facilities to qualify for alternative treatments simply because of narrow relative comparisons within an unstable set.
• This may create perverse incentives or distort capital planning, especially if facilities shift in and out of the “region of instability” due to data artefacts rather than true behavioural divergence.

How Risk Accounting Can Help

Risk accounting provides a deeper, control-based view that can be used to supplement or even inform the definition of thresholds — moving from behaviour-based proxies to indicators of risk management effectiveness.

Specifically, Risk Units (RUs) could:

• Identify facilities where operational control degradation explains instability, indicating the need for higher conservatism or disqualification from alternative CCF approaches,
• Detect stable operational environments even in the presence of volatile drawdown behaviour, supporting the case for continued use of advanced modelling,
• Support a hybrid threshold system, where relative thresholds are used as a starting point, but the final determination incorporates operational diagnostics.

We recommend that the EBA consider permitting institutions to supplement the relative threshold mechanism with internal operational metrics — such as RU-based control assessments — to justify exceptions or calibrate multiple thresholds tailored to meaningful risk clusters. Additionally, introducing a bounded absolute threshold could ensure a minimum safety floor, while still allowing flexibility above that floor based on validated internal diagnostics.

21. Question 21: Do you consider the guidance sufficiently clear in relation to the requirement for institutions to set up a policy to define a threshold value?

We understand the EBA’s intention to assess practical and methodological implications under this requirement.

Challenges under the Current Framework

While the EBA's intention to delegate threshold-setting to institutions allows for flexibility, the current guidance may be perceived as vague in several areas:

• It lacks criteria for what constitutes an acceptable threshold policy, potentially leading to divergent practices across institutions,
• There is little guidance on how to justify or validate the selected threshold values, which may complicate supervisory assessment,
• It does not address how internal policies should adapt over time: for example, in response to model evolution or changes in borrower behaviour.
• This could result in inconsistent application of the guidelines, or in policies that are overly conservative due to fear of supervisory challenge.

How Risk Accounting Can Help

Institutions using Risk Units (RUs) could:

• Develop evidence-based policies where threshold values are linked to observed changes in operational control effectiveness,
• Track changes in RU levels over time, informing periodic policy updates to reflect current risk management performance,
• Justify deviations from standard thresholds where internal controls (as measured through RUs) provide mitigating assurance.

This approach offers both consistency and flexibility, ensuring thresholds are meaningful, risk-sensitive, and traceable to internal governance mechanisms.

We encourage the EBA to expand the guidance by including examples of acceptable policy frameworks and to explicitly recognize that institutions may use operational diagnostics, including risk accounting outputs, to calibrate and adjust their threshold-setting practices.

22. Question 22: Do you consider it appropriate to set a prescribed level or range for the de-fined threshold, and if so, what would be an appropriate level for the threshold? In case an absolute threshold is warranted, what would be an appropriate prescribed level for an abso-lute threshold?

We understand the EBA’s intention to assess practical and methodological implications under this requirement.

Challenges under the Current Framework

A fixed threshold may not reflect the diversity of portfolios and operational risk environments,

• If calibrated too conservatively, it may unnecessarily restrict institutions from applying advanced modelling methods,
• Conversely, a liberally set threshold could be exploited in low-control settings, undermining the prudence of CCF estimation.
• Additionally, without clear integration of operational or behavioural diagnostics, threshold levels may act as blunt instruments rather than as risk-informed controls.

How Risk Accounting Can Help

Risk accounting can help strike the right balance between standardization and proportionality:

• Institutions could use Risk Units (RUs) to develop internal benchmark ranges for thresholds based on their operational risk control effectiveness,
• The EBA could define minimum threshold parameters (relative or absolute) that institutions may then adjust within a justified RU-based framework,
• Institutions could demonstrate, for example, that a lower threshold is justified due to consistently strong RU trends and effective risk containment, or that a higher one is needed in a stressed or degraded control environment.

This method supports supervisory confidence while allowing the tailoring of thresholds to institutional realities. It also provides a mechanism to revise thresholds periodically based on observable changes in internal control quality.

We recommend that, if the EBA prescribes threshold levels, these should be positioned as a floor or reference point, with flexibility for institutions to propose and justify deviations using operational data such as that produced via risk accounting.

23. Question 23: Do you think that, for the facilities in the region of instability, and/or for fully drawn revolving commitments, a single approach should be prescribed (e.g. one of the ap-proaches above defined in the Basel III framework), or that more flexibility is necessary for institutions to use different approaches they deem most appropriate for these facilities?

We understand the EBA’s intention to assess practical and methodological implications under this requirement.

Challenges under the Current Framework

Prescribing a single method may enhance simplicity and comparability, but it introduces significant drawbacks:

• It may force institutions to apply suboptimal or mismatched models to product types or risk behaviours that the method does not properly capture,
• It risks over- or underestimating credit exposure due to structural inflexibility, particularly in institutions with diverse portfolios,
• It discourages innovation or refinement of models tailored to an institution’s actual credit management practices and risk controls.
• In some cases, a one-size-fits-all solution could lead to unnecessary conservatism or even model risk.

How Risk Accounting Can Help

Risk accounting supports a more nuanced and institution-specific application of modelling methods by offering a standardized operational risk lens across product types and portfolios.

With RUs, institutions can:

• Assess the operational integrity of controls supporting the facility, helping justify why a more advanced or tailored method is appropriate despite instability,
• Identify specific drivers of instability — such as process lapses, weak monitoring, or borrower responses to external shocks — and determine whether these warrant modelling restrictions,
• Support proportional regulatory decisions based on actual internal capability and control effectiveness rather than theoretical model fit alone.

Rather than prescribing a single approach, the EBA could define a principles-based framework where institutions demonstrate the appropriateness of their method selection based on observable operational conditions, documented through tools like risk accounting.

This enables risk-sensitive modelling while safeguarding against misuse or unwarranted model complexity. It also supports greater transparency in supervisory dialogue.

We recommend that the EBA maintain flexibility in approach selection while strengthening requirements around internal justification and control diagnostics, which risk accounting is well placed to provide.

24. Question 24: If such flexibility is indeed warranted, what is the technical argumentation why prescribing a single alternative approach for these facilities is not suitable? Which cases or which types of revolving commitments could not be modelled under the approaches pre-scribed? Are there types of revolving commitments that could not be modelled by any of the approaches described in the Basel III framework?

We understand the EBA’s intention to assess practical and methodological implications under this requirement.

Limitations of the Current Framework

Mandating a single approach for revolving commitments and facilities in the "region of instability" could be technically and practically inadequate due to the following:

• Diverse credit product designs (e.g., trade finance, margin-based lending, multi-purpose lines) may feature draw patterns not captured by standard assumptions,
• Behavioral complexity driven by embedded options, counterparty incentives, or dynamic limit mechanisms may violate model assumptions,
• Data insufficiency or representativeness issues may prevent robust calibration under a prescribed method, leading to unreliable capital figures.
• Additionally, some revolving structures include contractual or informal risk mitigants (such as usage covenants or automatic limit reductions) that standard Basel III approaches do not accommodate.

Role and Benefit of Risk Accounting

Risk accounting offers a path to justify and structure alternative approaches by grounding modelling decisions in a transparent assessment of operational risk dynamics.

With Risk Units (RUs), institutions can:

• Highlight operational nuances that justify deviation from the prescribed method (e.g., effective control environments that stabilize behaviour),
• Identify classes of facilities whose drawdown risk arises from governance or process variability, not from borrower behaviour alone,
• Develop institution-specific insights to segment portfolios and apply tailored approaches supported by measurable risk control effectiveness.

For example, a trade finance facility with collateralized draw triggers may be modelled more appropriately using an institution-developed method that accounts for operational safeguards — which could be validated using RU stability data.

We encourage the EBA to maintain modelling flexibility in such cases and to recognize structured, risk-sensitive frameworks like risk accounting as valid bases for tailoring or extending beyond the Basel III approach set. This fosters proportionality, innovation, and alignment with real-world practices.

25. Question 25: Which of the three approaches described in the Basel III framework is pre-ferred in case a single approach would be prescribed?

We understand the EBA’s intention to assess practical and methodological implications under this requirement.

Considerations and Constraints

Each of the three Basel III alternatives presents trade-offs:

• The fixed CCF approach is simple and transparent, but may lack risk sensitivity in portfolios with more dynamic draw behaviour.
• The hybrid approach provides partial modelling flexibility but may still suffer from rigidity where exposure volatility is operationally managed.
• The reference data approach assumes access to comparable industry datasets, which may not be consistently available or relevant for all institutions or product types.

Choosing a single approach may inadvertently favour institutions with specific business models, data access, or product offerings.

Risk Accounting-Based Preference

In light of these limitations, a preferred approach should support operational differentiation and be amenable to transparent oversight. In this respect, the hybrid approach appears most aligned with the principles of risk accounting, as it allows:

• Basic parameterization where internal data is limited,
• Tailoring through supplemental internal analysis where operational insight is available,
• Progressive enhancement over time as data quality improves or controls mature.

Risk accounting enhances the hybrid method by:

• Providing structured diagnostics to inform where simplification is acceptable,
• Identifying operational breakdowns or strengths that affect drawdown reliability,
• Supporting the alignment between observed facility behaviour and model assumptions.

We therefore recommend the hybrid approach as the most compatible option should a single method be prescribed. However, we reiterate that maintaining institutional flexibility, supported by demonstrable risk accounting frameworks, remains the preferred overarching strategy.

26. Question 26: For the purpose of the long run average calculation, are there any situations where such intermediate exposure weighted averaging at obligor level would lead to a dif-ferent outcome (that is unbiased) with regard to the CCF estimation? How material is this for your portfolio?

We understand the EBA’s intention to assess practical and methodological implications under this requirement.

Challenges under the Current Framework

While exposure-weighted averaging is intended to ensure proportional representation of obligor behaviour, its universal application may not always be appropriate:

• In cases where exposure limits fluctuate frequently, obligor-level averaging may overweight transient exposures,
• It may dilute the influence of drawdown-sensitive contracts, especially if aggregated with more passive or administratively driven lines,
• Facilities with distinct usage patterns under a single obligor (e.g., a mix of working capital lines and contingent commitments) may be inappropriately combined.

This could obscure meaningful behavioural risk signals and produce biased estimates, particularly in portfolios with diverse facility types under the same counterparty.

How Risk Accounting Addresses This

A risk accounting approach allows institutions to interrogate obligor-level averaging in light of operational differences across commitments:

• Risk Units (RUs) can help disaggregate the internal risk profiles of individual facilities, allowing for selective weighting or segmentation in LRA calculations,
• RU trends may indicate that certain facility types — even under a shared obligor — merit separate treatment due to differential control performance or drawdown drivers,
• Institutions could justify model adjustments where RU-based evidence demonstrates that obligor-level exposure weighting introduces distortion.
• In portfolios with high structural diversity, such refinement could meaningfully reduce estimation bias and improve capital accuracy.

We recommend that the EBA permit institutions to identify circumstances — supported by operational diagnostics such as RUs — where facility-level weighting or disaggregation is more appropriate. This would enhance both risk sensitivity and model transparency without undermining the principle of long-run averaging.

27. Question 27: Do you have any comments on the condition set to use the simple approach to estimate additional drawings after default. Do you consider that the simple approach is also relevant for retail portfolios?

We understand the EBA’s intention to assess practical and methodological implications under this requirement.

Challenges under the Current Framework

While the simple approach provides clarity and a baseline of conservatism, applying it universally, including to retail portfolios, may be overly blunt:

• Retail exposures can exhibit highly heterogeneous drawdown patterns, with significant variance across product types, channels, and customer segments,
• A standard simple approach may overestimate or underestimate exposure risk if operational controls or product features (e.g., usage alerts, automated credit controls) are ignored,
• The lack of behavioural nuance may distort capital requirements or obscure underlying risk control effectiveness.

How Risk Accounting Addresses This

Risk accounting provides an alternative basis for assessing whether the simple approach is appropriate for a given portfolio or subsegment. By analysing operational risk signals through Risk Units (RUs), institutions can:

• Identify retail portfolios where operational risk control effectiveness justifies deviation from the simple approach,
• Highlight segments where control failures or usage anomalies suggest the simple approach is conservative and warranted,
• Develop a tiered application of the simple method based on demonstrable internal conditions rather than portfolio label alone.

For example, a card portfolio with strong transaction controls and stable RU profiles may justify a refined model, whereas an unsecured overdraft portfolio with degraded controls may warrant the fallback treatment.

We recommend that the EBA allow for operational diagnostics — including RU analysis — to inform the appropriateness of the simple approach at a more granular level, particularly within diverse retail portfolios. This would ensure both conservatism and risk sensitivity.

28. Question 28: It was considered that requiring institutions to exclude unresolved cases from the long run average CCF, if their realised CCF is lower than the LRA of the corresponding fa-cility grade, could be seen as too conservative. Do you have any comments on this treat-ment introduced in the simple approach? Do you have specific examples when this treat-ment would not be appropriate?

We understand the EBA’s intention to assess practical and methodological implications under this requirement.

Challenges under the Current Framework

While the proposed treatment aims to guard against underestimation of risk, it raises several issues:

• Excluding unresolved cases solely because their CCF is lower than the LRA may introduce systematic upward bias, especially in portfolios with long-dated facilities or low-risk behaviours,
• It may penalize effective operational controls that limit drawdowns post-default, thereby distorting the link between control performance and capital requirements,

The rule does not differentiate between cases where low realisations reflect true behavioural stability versus those where they are a product of early-stage observation or random variation.

How Risk Accounting Addresses This

Risk accounting provides a more differentiated view of unresolved cases, enabling better treatment decisions:

• Institutions can use Risk Units (RUs) to assess the underlying control quality of each unresolved case, helping distinguish those with low residual drawdown risk,
• RUs could also track operational deterioration or stability throughout the post-default period, offering a real-time signal of when inclusion or exclusion from LRA becomes justified,

This allows for selective and risk-sensitive inclusion of unresolved cases, avoiding blanket exclusions that may misrepresent true portfolio risk.

For instance, a facility in technical default but under tight drawdown controls with improving RU metrics could be justifiably included in the LRA despite a low realised CCF. Conversely, an unresolved case showing increasing operational risk could be excluded even if its current realised CCF is benign.

We recommend that the EBA consider supplementing its proposed rule with a diagnostic overlay — allowing institutions to retain low-CCF unresolved cases in the LRA where operational evidence supports it. Risk accounting provides a ready framework for such evaluation.

29. Question 29: Do you have any comments on the modelling approach to estimate additional drawings after default for unresolved cases?

We understand the EBA’s intention to assess practical and methodological implications under this requirement.

Challenges under the Current Framework

Estimating additional drawings for unresolved cases is inherently uncertain. While current approaches may apply statistical approximations or conservative overlays, these often:

• Rely heavily on historical averages or fixed assumptions that may not reflect current facility conditions,
• Overlook real-time operational factors influencing draw behaviour, such as control deterioration, counterparty incentives, or changes in external environment,
• Struggle to differentiate between cases with genuinely minimal risk of further drawdowns and those with hidden or emerging vulnerabilities.

How Risk Accounting Addresses This

Risk accounting offers an evidence-based enhancement to the modelling of unresolved cases:

• Risk Units (RUs) allow institutions to monitor the operational effectiveness and behavioural signals of each unresolved facility,
• Facilities with stable or improving RU trends could be modelled using lower or tapered additional drawing assumptions,
• Conversely, facilities with deteriorating control performance, as flagged by RU volatility, could be modelled with elevated or full-limit drawing assumptions.

This approach avoids blanket conservatism and introduces dynamic, risk-sensitive calibration based on internal operational insights.

Moreover, risk accounting enables institutions to periodically reassess and reclassify unresolved cases based on updated control diagnostics, supporting progressive improvement in model precision over time.

We recommend that the EBA permit institutions to incorporate operational diagnostics — such as those enabled through risk accounting — into their estimation of additional drawings for unresolved cases. This would help ensure that capital requirements reflect real risk while promoting internal control accountability.

30. Question 30: Do you have any concerns with the requirement to use as a maximum drawing period the maximum recovery period set for LGD?

We understand the EBA’s intention to assess practical and methodological implications under this requirement.

Challenges under the Current Framework

While this alignment improves comparability, there are technical and risk-based concerns:

• The LGD recovery horizon reflects the full resolution of a default, often encompassing legal or administrative delays, which may extend beyond periods of active credit draw risk,
• In some products or jurisdictions, drawdowns may cease early due to contractual suspensions, operational blockades, or customer disengagement, making the LGD timeline too conservative for CCF estimation,
• Uniform application may overlook product-specific nuances, particularly in revolving retail portfolios, trade finance, or other contingent products.

This could lead to overstated capital requirements, especially for portfolios with short-term behaviour but long legal recovery processes.

How Risk Accounting Addresses This

Risk accounting supports a more nuanced calibration of the maximum drawing period, aligned with operational realities:

• Risk Units (RUs) can track control activity and behavioural dynamics across the post-default timeline, revealing when drawdown potential meaningfully declines,
• Institutions could use RU deterioration curves to identify natural drawdown cessation points, which may precede the end of the LGD recovery window,
• RU analysis also enables detection of residual drawdown risk spikes, allowing for an adaptive — rather than fixed — cut-off date for modelling purposes.

This would allow institutions to justify shorter (or in some cases longer) drawing periods than the LGD maximum, backed by objective operational evidence.

We recommend that the EBA allow for this risk-sensitive approach, whereby institutions may propose drawing periods shorter than the LGD recovery horizon, provided this is supported by internal diagnostics such as those offered by risk accounting. This ensures alignment with true exposure risk while maintaining consistency and transparency.

31. Question 31: For CCF estimation, do you use estimation methods that incorporate portfolio-level-calibration of the estimates? What are the main reasons to use a calibration at a level that is higher than the grade-level calibration?

We understand the EBA’s intention to assess practical and methodological implications under this requirement.

Challenges under the Current Framework

While broader-level calibration can increase statistical robustness, it introduces several challenges:

• It may mask underlying risk variation across segments, reducing model sensitivity and capital alignment,
• Top-down calibration risks overriding valuable bottom-up insights, particularly where granular behavioural signals exist,
• Aggregated calibration may dilute the influence of control quality or process differences, especially in operationally heterogeneous portfolios.

This can lead to a disconnect between risk management incentives and capital modelling outcomes.

How Risk Accounting Addresses This

Risk accounting offers an internal justification framework for calibrating CCF estimates at higher aggregation levels, when necessary, while still preserving risk sensitivity:

• Risk Units (RUs) can be aggregated across grades or segments to support macro-level calibration decisions based on consistent operational risk patterns,
• Where control effectiveness is stable across subsegments, RU-based evidence can justify consolidation,
• Conversely, significant RU divergence can indicate that portfolio-level calibration is inappropriate and that grade-level (or more granular) modelling is required.

This dual capability provides institutions and supervisors with a principled way to determine the appropriate calibration level — balancing model robustness with operational relevance.

We encourage the EBA to allow such internal diagnostics to inform calibration granularity. Risk accounting can bridge the gap between granular credit modelling and portfolio-wide capital stability, supporting well-founded, transparent estimation frameworks.

32. Question 32: Do you have any comments on the guidance for the CCF estimation of default-ed exposures?

We understand the EBA’s intention to assess practical and methodological implications under this requirement.

Challenges under the Current Framework

While the guidance introduces helpful structure, it presents certain constraints:

• The guidance may underappreciate the diversity of post-default behaviours, particularly for retail vs. corporate exposures,
• It often assumes defaulted exposures exhibit consistent drawdown patterns, whereas in reality these patterns can be driven by operational factors, such as system overrides, legal constraints, or manual interventions,
• There is limited integration of how internal controls or post-default recovery practices affect remaining drawdown risks.

This may lead to either over- or underestimation of exposure, depending on how draw potential is operationally curtailed or unintentionally allowed.

How Risk Accounting Addresses This

Risk accounting offers a powerful overlay to better inform CCF estimation for defaulted exposures:

• Risk Units (RUs) can be used to monitor operational conditions after default, offering early warnings of residual exposure risk,
• Institutions could use RU deterioration or volatility as proxies for identifying which exposures remain at high drawdown risk,
• RU metrics allow for segmentation of defaulted exposures based on actual operational risk, enabling more tailored and risk-sensitive estimation.

For example, a portfolio segment under automated drawdown freezes with stable RU performance may warrant a significantly lower CCF than a similar segment with persistent RU volatility and manual workarounds.

We recommend that the EBA consider incorporating operational risk diagnostics — such as RUs — into the guidance for defaulted exposure modelling. This would align estimation more closely with actual institutional risk conditions and support supervisory transparency.,

33. Question 33: Do you have any comments on the determination of the low share of ob-served additional drawings after default in the historical observation period relative to the observed undrawn amount at default date? Do you consider it appropriate to set a pre-scribed threshold to determine what constitutes this low share? If so, what would be an ap-propriate value for such a materiality threshold?

We understand the EBA’s intention to assess practical and methodological implications under this requirement.

Challenges under the Current Framework

Setting a prescribed threshold can support comparability but may risk:

• Oversimplifying diverse risk environments by applying a one-size-fits-all cutoff,
• Penalizing portfolios with effective operational controls that naturally lead to low post-default drawdowns,
• A static threshold might not reflect real drawdown risk in operationally distinct settings — such as secured vs. unsecured lending, automated vs. manual draw controls, or retail vs. wholesale exposures.

How Risk Accounting Addresses This

Risk accounting supports a more dynamic, risk-sensitive approach to identifying low drawdown shares:

• Institutions can use Risk Units (RUs) to monitor post-default operational behaviour, enabling them to distinguish between truly low-risk cases and those where low drawdowns may be temporary or misleading,
• RU trends and control effectiveness scores provide granular diagnostics to justify the inclusion or exclusion of cases near a threshold,
• Institutions could propose internal thresholds tailored to their control environments, backed by evidence from RU analysis.
• Rather than prescribing a universal threshold, the EBA could define a guidance range (e.g., 5–15%) and permit institutions to justify positions within or outside that range using operational diagnostics like those based on risk accounting.

This approach encourages stronger risk management, improves model relevance, and maintains regulatory consistency.

34. Question 34: Are there examples where the haircut approach should be considered the most appropriate approach for estimating the downturn CCF?

We understand the EBA’s intention to assess practical and methodological implications under this requirement.

Challenges under the Current Framework

The haircut approach is often considered simplistic, and its use may:

• Be viewed as a conservative fallback rather than a method reflecting true behavioural dynamics,
• Miss out on the nuances of counterparty or product-specific downturn sensitivities,
• Fail to incorporate real-time indicators of deterioration, particularly in portfolios where risk control erosion precedes observed stress-period drawdowns.

How Risk Accounting Addresses This

Risk accounting can enhance the decision framework for applying the haircut approach by helping institutions to:

• Identify portfolios with inconsistent or unreliable downturn data through volatility in Risk Units (RUs) that signal weak control predictability,
• Justify the use of the haircut approach where operational diagnostics demonstrate high vulnerability under stress that is not captured in past observations,
• Monitor RU performance across macroeconomic cycles, enabling institutions to detect systemic fragility that supports a proactive haircut application.

Examples where the haircut approach could be most appropriate:

• New or low-default portfolios, particularly in retail or SME lending, where downturn calibration is statistically infeasible,
• Portfolios with historically unobservable downturn draw behaviour, such as unused overdraft lines that could suddenly activate under stress,
• Where RUs show high operational sensitivity to external shocks — suggesting the observed CCFs under benign conditions would likely not hold during systemic stress.

In such cases, a risk accounting-enabled haircut can preserve prudence while tailoring its severity to internal control diagnostics, rather than applying arbitrary fixed deductions.

We support the use of the haircut method where supported by operational evidence — and recommend that institutions be encouraged to use risk accounting as a tool for substantiating this choice.

35. Question 35: Do you think the add-on of 15 percentage points is adequately calibrated when the downturn impact cannot be observed nor estimated? Could you provide clear examples or reasons why this add-on should be higher or lower than 15 percentage points?

We understand the EBA’s intention to assess practical and methodological implications under this requirement.

Challenges under the Current Framework

While well-intentioned, a fixed 15 percentage point add-on can be problematic:

• It may be insufficient for high-risk portfolios where internal controls are known to deteriorate rapidly under stress,
• Conversely, it may be excessive for portfolios with strong operational resilience, producing inflated capital charges unrelated to actual draw risk,
• It lacks responsiveness to institution-specific conditions or emerging internal indicators that could justify a more tailored approach.

This blanket approach could lead to capital inefficiencies, reduce modelling credibility, and penalize institutions investing in effective risk controls.

How Risk Accounting Addresses This

Risk accounting offers an evidence-based path to calibrating downturn add-ons more precisely:

• Risk Units (RUs) can help institutions assess their operational exposure to stress triggers, supporting decisions to deviate from the fixed add-on when warranted,
• RU volatility trends can act as early indicators of downturn sensitivity, justifying upward adjustments beyond the 15% add-on where internal vulnerabilities are observed,
• Conversely, consistent and stable RU performance across past macroeconomic cycles can provide defensible evidence for a lower add-on, especially in portfolios with embedded draw restrictions.

Examples:

• In a credit card portfolio with prior stress-period RU deterioration and minimal mitigation capacity, a 20–25 percentage point add-on may be more appropriate,
• In a secured SME facility pool with stable RU signals and historical resilience, a lower add-on - e.g., 5–10 percentage points - could be substantiated.

We recommend that the EBA maintain the 15% as a baseline, but allow institutions to propose deviations (upward or downward) backed by operational diagnostics such as those provided by risk accounting. This would support better capital alignment and stronger internal risk governance.

36. Question 36: Have you observed, or do you expect a (statistically significant) correlation be-tween economic indicators and realised CCFs? If so, do you expect higher or lower levels of CCFs observed in the downturn periods compared to the rest of the cycle? Do you have pol-icies in place that restrict or, on the other hand, relax the drawing possibilities in the down-turn periods?

We understand the EBA’s intention to assess practical and methodological implications under this requirement.

Challenges under the Current Framework

The assumption that downturn periods systematically result in higher drawdowns (and therefore higher CCFs) is broadly intuitive but not universally observed:

• Correlations between macroeconomic indicators and drawdowns are often weak or inconsistent, particularly for portfolios with active draw control mechanisms,
• Historical CCF spikes may reflect reactive policy or control lapses, not natural borrower behaviour,
• Institutions may implement restrictions during stress — such as draw freezes or conditional funding clauses — that moderate draw behaviour contrary to model expectations.

As a result, assumptions about higher downturn CCFs may be overstated without a granular understanding of operational practices and behavioural triggers.

How Risk Accounting Addresses This

Risk accounting provides a means to empirically examine and manage this relationship:

• Risk Units (RUs) track the performance of internal controls and operational risk levels, offering insight into how economic stress affects institutional vulnerability to drawdowns,
• Institutions can analyse RU trends against macro indicators (e.g. GDP growth, unemployment, interest rates) to identify causal links and lag structures,
• RU-based diagnostics also capture how internal policy changes — such as draw restriction activation — influence exposure behaviour under stress.

This allows institutions to distinguish between genuine borrower-driven draw increases and institutionally induced risk exposures, providing stronger explanatory power than economic variables alone.

Additionally, risk accounting enables ex-ante stress testing of draw behaviour by modelling how control deterioration, flagged by RU volatility, may evolve under macroeconomic pressure, supporting more grounded downturn CCF adjustments.

We recommend that the EBA encourage institutions to complement macro-level analysis with operational diagnostics, such as those enabled by risk accounting, to ensure more accurate and institution-specific CCF modelling under downturn conditions.

37. Question 37: The possibility to have no downturn effect on CCF estimates is restricted to the case where observations are available during a downturn period. Which alternative meth-odologies could be used to prove the non-existence of a downturn effect on CCF estimates, in the case where no observation is available during a downturn period?

We understand the EBA’s intention to assess practical and methodological implications under this requirement.

Challenges under the Current Framework

Requiring empirical downturn-period observations as the sole basis for proving the absence of a downturn effect has several shortcomings:

• It excludes portfolios with limited or no downturn history, even if other robust indicators suggest minimal stress sensitivity,
• It may encourage over-conservatism in cases where downturn behaviour has been stable or historically mitigated by controls,
• It assumes that the downturn effect, if present, must manifest uniformly across all exposures, ignoring operational heterogeneity.

This could lead to capital inefficiency and unnecessary model complexity for portfolios with structurally low risk.

How Risk Accounting Addresses This

Risk accounting offers a credible alternative methodology for assessing downturn effects in the absence of historical downturn observations:

• Risk Units (RUs) enable institutions to track operational risk resilience over time and under varying economic pressures,
• Where RUs remain stable despite broader macro volatility (e.g., commodity shocks, sector-specific downturns), this can indicate inherent control robustness,
• Institutions can simulate downturn-like conditions using forward-looking stress tests of RU behaviour, identifying whether control effectiveness or behavioural risk materially shifts.

Additionally, retrospective RU analysis can flag periods of incipient economic strain (e.g., liquidity crunches, geopolitical events) and demonstrate whether exposure behaviour remained contained, offering indirect but valid evidence of low downturn sensitivity.

We recommend that the EBA allow institutions to use internal operational diagnostics, including RU-based evidence, to demonstrate the non-existence of a downturn effect in CCFs when empirical downturn-period observations are lacking. This would foster more meaningful, institution-specific risk modelling and promote accountability for internal resilience.

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