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Exposure at default

Exposure at Default (EAD) is a critical parameter in credit risk assessment under the Basel Framework, representing the estimated gross exposure of a financial institution to a counterparty at the moment of default, gross of specific provisions but before any loss given default adjustments.^[1] It forms one of the core components—alongside probability of default (PD) and loss given default (LGD)—used to compute expected loss and risk-weighted assets for regulatory capital requirements in banking.^[1] Introduced in Basel II and refined in subsequent updates including Basel III, EAD applies to both on-balance sheet items (such as loans) and off-balance sheet items (such as commitments and derivatives), ensuring banks hold sufficient capital against potential credit losses.^[2] In the standardised approach to credit risk, EAD is calculated straightforwardly as the carrying value for on-balance sheet exposures, while off-balance sheet exposures are converted to credit equivalents using prescribed credit conversion factors (CCFs).^[2] For instance, direct credit substitutes like guarantees attract a 100% CCF, commitments over one year a 50% CCF, and unconditionally cancellable commitments a 10% CCF, with the resulting EAD multiplied by external risk weights to derive risk-weighted assets.^[2] This approach relies on supervisory formulas rather than bank-specific models, promoting consistency across institutions but potentially overlooking nuanced risk profiles.^[2] For counterparty credit risk in derivatives and securities financing transactions, EAD follows specialised methods outlined in the Basel Framework's counterparty credit risk chapters.^[2] Under the internal ratings-based (IRB) approach, banks may use their own estimates for EAD, subject to supervisory approval and floors, allowing for more tailored risk measurement particularly for undrawn commitments and complex instruments.^[1] In the foundation IRB variant, off-balance sheet EAD uses standardised CCFs similar to the standardised approach, whereas the advanced IRB permits internal models for CCFs, provided they meet minimum data and validation standards, with a regulatory floor of at least the on-balance sheet amount plus 50% of the off-balance sheet exposure.^[1] For drawn exposures, EAD cannot fall below the amount that would impact regulatory capital if written off, plus associated provisions, ensuring conservatism in low-exposure scenarios.^[1] These methods integrate with credit risk mitigation techniques, such as netting and collateral, per Basel guidelines.^[1] EAD's estimation is vital for stress testing and portfolio management, as it captures potential drawdowns on facilities during economic downturns when defaults are more likely, influencing overall banking stability under global regulatory standards.^[2] While Basel III enhancements, including output floors, aim to harmonise EAD across approaches and reduce variability in capital calculations, challenges persist in accurately modeling future exposures amid volatile markets.^[1]

Regulatory Framework

Basel Accords Integration

Exposure at Default (EAD) serves as one of the three core risk parameters—alongside Probability of Default (PD) and Loss Given Default (LGD)—within the Internal Ratings-Based (IRB) approach introduced in the Basel II framework, enabling banks to estimate expected loss (EL) as EL = PD × LGD × EAD and determine regulatory capital requirements for credit risk.^[3] Under Pillar 1 of Basel II, finalized in June 2004, EAD quantifies the expected gross exposure at the time of a borrower's default, encompassing both on-balance sheet items (such as drawn loans) and off-balance sheet items (such as undrawn commitments converted via credit conversion factors).^[3] This parameter ensures that risk-weighted assets reflect potential future drawdowns and undrawn amounts, promoting a more risk-sensitive capital framework while requiring supervisory validation for banks' internal EAD models.^[3] The Basel III accords, developed between 2010 and 2017 in response to the global financial crisis, enhanced the credit risk framework by mandating higher quality capital, with a minimum Common Equity Tier 1 (CET1) ratio of 4.5% of risk-weighted assets, alongside additional buffers to improve loss absorption.^[4] Liquidity requirements, including the Liquidity Coverage Ratio (LCR) effective from 2015 and the Net Stable Funding Ratio (NSFR) from 2018, indirectly influence EAD by necessitating robust stress testing of exposures under adverse conditions, such as incorporating stressed inputs for counterparty credit risk and downturn loss given default estimates.^[4] These measures compel banks to refine EAD calculations to account for liquidity stresses and potential increases in exposure during market turmoil, thereby aligning capital holdings more closely with systemic vulnerabilities.^[4] Building on Basel III, the final reforms—often termed Basel IV and published in December 2017—introduce a 72.5% output floor on risk-weighted assets derived from IRB approaches, ensuring that internal model outputs, including EAD estimates, do not yield RWAs below 72.5% of those calculated under the standardized approach.^[5] This floor constrains the capital relief benefits of advanced EAD models by imposing a conservative baseline, reducing variability in capital requirements across institutions and enhancing global comparability.^[5] Implementation is phased over five years starting January 1, 2022, reaching the full 72.5% level by January 1, 2027, with jurisdictional variations extending adoption in some regions up to 2030.^[5]

Evolution and Recent Updates

Following the finalization of Basel III, the transition to Basel 3.1 represents a significant evolution in the regulatory treatment of Exposure at Default (EAD), with implementations varying by jurisdiction. In the European Union, Basel 3.1 standards are integrated through Regulation (EU) 2024/1623 (CRR III), applying from January 1, 2025, and introducing revisions to the standardized approach for credit risk that refine EAD calculations for both pre-default and post-default exposures, including updated credit conversion factors for off-balance-sheet items to better capture potential drawdowns.^[6] In the United Kingdom, the Prudential Regulation Authority delayed implementation to January 1, 2027, following a January 2025 announcement, while maintaining a phase-in period leading to full application by 2030.^[7] Globally, member jurisdictions of the Basel Committee are expected to complete phase-in by around 2030, with the revisions aiming to enhance risk sensitivity in EAD estimation under both standardized and internal ratings-based (IRB) approaches without altering core methodologies.^[8] Basel IV, often viewed as an extension of Basel III, introduced parameter input floors for IRB models to curb excessive variability and promote conservatism in risk parameter estimates, including those for EAD. These floors apply to probability of default (PD) at 5 basis points for most corporate exposures, loss given default (LGD) at 25% for senior unsecured claims, and conservative credit conversion factors (CCFs) within EAD calculations ranging from 10% to 50% depending on commitment type, ensuring that banks' internal EAD models do not understate potential exposures.^[9] The overall output floor, set at 72.5% of standardized approach risk-weighted assets, further constrains IRB-derived EAD contributions to capital requirements, with a phased introduction from 50% in 2025 to full effect by 2030.^[10] Recent updates from the Basel Committee on Banking Supervision (BIS) and the European Banking Authority (EBA) in 2024-2025 have focused on refining EAD model oversight and counterparty exposures. The EBA's 2024 Credit Risk Benchmarking Exercise, based on 2023 data, revealed stable IRB EAD shares at around 50% of total exposures but highlighted slight increases in EAD-weighted average PD for retail segments like credit cards and SMEs, prompting enhanced supervisory scrutiny and model adjustments to reduce variability across banks. Complementing this, a BIS technical amendment finalized in October 2025 addresses hedging in counterparty credit risk, allowing partial EAD reductions under the standardized approach for counterparty credit risk (SA-CCR) when using credit derivatives or guarantees, but retaining a residual unprotected portion to account for basis and wrong-way risk.^[11] Jurisdictional differences underscore ongoing adaptations, particularly regarding emerging risks. In the United States, the Dodd-Frank Act's framework influences EAD through annual stress testing (DFAST), where supervisory models estimate EAD for credit portfolios, including commercial real estate, assuming full commitment drawdown in adverse scenarios to align with Basel standards, with proposed Basel 3.1 rules (Basel endgame) originally targeting a transition start on July 1, 2025, but with revisions expected by early 2026 as of November 2025.^[12]^[13] In the EU, CRR III mandates comprehensive management of environmental, social, and governance (ESG) risks, including climate-related factors, empowering supervisors to require adjustments in credit risk parameters like EAD via Pillar 2 guidance, with the EBA tasked to evaluate dedicated prudential treatments for ESG exposures by the end of 2025.^[6]

Core Concepts

Definition and Principles

Exposure at Default (EAD) is defined as the expected gross exposure of a facility to a borrower at the time of default, encompassing both drawn amounts outstanding and potential undrawn commitments that may be drawn prior to default.^[3] This measure represents the total amount a bank anticipates being owed at the moment of default, without deductions for provisions, collateral, or recoveries.^[3] EAD forms one of the three core parameters in the regulatory expected loss (EL) calculation for credit risk, expressed as EL = PD × LGD × EAD, where PD is the probability of default and LGD is the loss given default.^[3] The foundational principles of EAD emphasize its role as a forward-looking estimate that accounts for behavioral responses, such as potential drawdowns on undrawn commitments during periods of financial stress leading up to default.^[3] These estimates must be derived from long-run average data, incorporating downturn conditions to ensure conservatism and alignment with economic cycles, and are subject to supervisory validation for accuracy and reliability.^[3] Under Basel III and subsequent updates like Basel 3.1, EAD estimates are subject to input floors to ensure consistency and conservatism across approaches. EAD under the IRB approach applies primarily to exposures in the banking book, where credit risk is assessed under the Internal Ratings-Based (IRB) approach, while positions in the trading book are subject to specific counterparty credit risk rules that also utilize EAD calculations. A key distinction exists between pre-default EAD, used in IRB capital calculations to project exposure at the horizon of default based on expected future drawdowns, and post-default EAD, which measures the actual exposure for impaired assets under regulatory approaches for credit risk, capturing any additional drawings after default occurrence.^[14] Pre-default EAD integrates anticipatory adjustments for borrower behavior, while post-default EAD focuses on realized amounts for provisioning and loss recognition.^[14] EAD was introduced in the Basel II framework, finalized in June 2004, to overcome the limitations of relying solely on current balance sheet exposures, enabling a more risk-sensitive assessment of potential credit losses from off-balance sheet items and commitments.^[3] This innovation marked a shift toward incorporating dynamic exposure modeling in regulatory capital requirements.^[3]

Relation to Other Risk Parameters

Exposure at default (EAD) forms one of the core components in the expected loss (EL) calculation under the Basel framework, expressed as EL = PD \times LGD \times EAD, where PD represents the probability of default, LGD the loss given default, and EAD the magnitude of exposure at the point of default.^[15] This equation underscores EAD's role in quantifying the potential financial impact of a default, distinct from PD's focus on the likelihood of default occurrence and LGD's emphasis on the severity of loss after default.^[1] EAD complements PD and LGD by addressing facility-specific dynamics, such as the drawn and undrawn portions of credit lines, which PD—tied to borrower creditworthiness—and LGD—dependent on collateral recovery rates—do not directly capture.^[1] For instance, while PD assesses the borrower's overall default risk over a one-year horizon, and LGD estimates the economic loss as a percentage of the exposure, EAD provides the absolute exposure amount, including potential drawdowns on commitments at default time.^[16] This separation ensures a granular breakdown of credit risk, with EAD focusing on exposure variability rather than borrower propensity or recovery shortfalls.^[1] In internal ratings-based (IRB) models, EAD estimates can incorporate correlations with PD and LGD for portfolio-level risk aggregation, reflecting how higher default probabilities or losses might influence exposure drawdowns, though EAD remains a distinct input for individual facility calculations.^[17] These interdependencies are modeled to capture joint behaviors, such as increased utilization during economic stress, but regulatory guidelines maintain EAD's independence in core computations to avoid overcomplication at the exposure level.^[18] The regulatory emphasis on EAD stems from its contribution to capital adequacy by accounting for potential future exposures, a refinement post-2008 financial crisis to mitigate procyclicality where low-volatility periods understated risks.^[4] Basel III reforms, informed by crisis lessons, introduced stressed EAD calibrations in certain contexts to prevent capital buffers from eroding during booms and amplifying downturns.^[4]

Calculation Methods

Standardized Approach

The Standardized Approach for Exposure at Default (EAD) provides a rule-based methodology under the Basel framework, utilizing fixed supervisory parameters to estimate potential credit exposure without relying on internal bank models. This method applies predetermined risk weights to on-balance sheet assets and Credit Conversion Factors (CCFs) to off-balance sheet items, promoting consistency and simplicity for institutions not approved for more advanced approaches. It forms a foundational element of credit risk capital calculations, ensuring that undrawn commitments are converted into equivalent on-balance sheet exposures based on standardized assumptions about drawdown behavior.^[2] For on-balance sheet exposures, EAD equals the outstanding amount at the time of assessment, with no further adjustment for undrawn portions, as these are addressed separately under off-balance sheet rules. Specific provisions and partial write-offs are deducted to arrive at the net exposure value used in risk-weighted asset computations. In contrast, off-balance sheet exposures are converted using the formula EAD = nominal amount × CCF, where CCFs are predefined supervisory values reflecting the perceived risk of utilization. Representative CCFs include 20% for short-term self-liquidating trade letters of credit arising from the movement of goods, 50% for transaction-related contingent items such as performance bonds, bid bonds, and warranties, and 100% for full-risk items like direct credit substitutes (e.g., general guarantees) and financial standby letters of credit serving as collateral. As of 2025, commitments receive a 40% CCF regardless of maturity under Basel 3.1 updates effective from January 2023.^[5]^[2] Post-default, the EAD reflects the gross exposure including applicable accrued interest and fees per general Basel principles, net of specific allowances, to capture all contractual obligations at default. This treatment aligns with impairment principles under IFRS 9 emphasizing gross carrying amounts for expected credit losses in stage 3 assets.^[1] The Standardized Approach is mandatory for smaller banks and those lacking approval for the Internal Ratings-Based (IRB) approaches, serving as the default method for credit risk assessment. Updates under Basel 3.1, effective from January 2023, refined the framework with revised risk weights for specialized lending exposures, such as project finance and income-producing real estate, to better address sector-specific risks while maintaining the non-model-based structure. As of November 2025, these updates are implemented in major jurisdictions including the EU and UK.^[19]

Foundation IRB Approach

The Foundation IRB approach, introduced under the Basel II framework, represents a hybrid method for calculating regulatory capital requirements for credit risk, where banks are permitted to develop and use their own internal models to estimate the probability of default (PD) for eligible exposures, while relying on supervisory-provided parameters for loss given default (LGD) and exposure at default (EAD). This approach bridges the fully standardized method and the more advanced IRB variant by allowing partial customization through bank-specific PD estimates, thereby encouraging improved risk management practices without the full complexity of estimating all risk parameters internally.^[3]^[1] In the Foundation IRB framework, EAD calculation focuses on the potential exposure at the time of default, distinguishing between on-balance sheet and off-balance sheet items. For on-balance sheet exposures, EAD is simply the current outstanding amount, measured gross of specific provisions, with no further adjustments permitted beyond regulatory capital reductions. For off-balance sheet items, such as commitments and contingent liabilities, EAD is determined using the formula:

\text{EAD} = \text{drawn amount} + (\text{undrawn amount} \times \text{CCF})

where the credit conversion factor (CCF) is a supervisory value drawn from the standardised approach to ensure consistency and limit variability. CCF values include 40% for commitments (as updated under Basel 3.1 effective 2023), 50% for transaction-related contingent items, and 100% for direct credit substitutes, reflecting the assumed likelihood of drawdown in default scenarios. These CCFs apply without bank-specific modeling, emphasizing a conservative, rule-based treatment for undrawn portions.^[3]^[2]^[16] Eligibility for the Foundation IRB approach is restricted to corporate, sovereign, and bank exposures, where banks must demonstrate robust PD modeling capabilities meeting minimum data, process, and validation requirements. Retail exposures, by contrast, utilize a simplified IRB variant that does not distinguish between foundation and advanced methods, instead requiring pooled estimates for PD, LGD, and EAD tailored to retail portfolios. This limitation ensures that less complex retail lending does not overburden smaller institutions while maintaining supervisory oversight on higher-risk wholesale exposures.^[1]^[20] With the implementation of Basel 3.1 reforms, effective from 2023 in key jurisdictions and fully by 2026 in the UK, revisions include an output floor of 72.5% applied to all IRB risk-weighted assets by 2028 to address variability, as highlighted in regulatory benchmarking exercises. These measures aim to reduce RWA dispersion while preserving the approach's hybrid nature; no specific EAD input floors apply to Foundation IRB, as EAD uses fixed supervisory parameters.^[14]

Advanced IRB Approach

In the Advanced IRB approach, banks are authorized to employ internal statistical models to estimate exposure at default (EAD), leveraging historical data to achieve more tailored and precise assessments compared to the Foundation IRB method. These models rely on comprehensive datasets spanning at least seven years for corporate, sovereign, and bank exposures—or five years for retail exposures—ideally encompassing a full economic cycle to capture variations in default conditions. A key aspect involves incorporating behavioral drawdown patterns, where models account for the propensity of obligors to increase drawings on undrawn commitments as default risk materializes, thereby reflecting the anticipated gross exposure at the point of default.^[20] The core estimation formula for EAD under this approach is the sum of the current drawn amount plus the expected undrawn drawdown, with the latter derived from internally modeled credit conversion factors (CCFs) based on empirical evidence of usage patterns. In practice, this may be expressed as EAD = current outstanding + (undrawn commitment × modeled CCF), adjusted for a margin of conservatism to address estimation uncertainty and potential downturn effects, such as EAD_pre-default × (1 + downturn adjustment) in certain model specifications to align with conservative loss projections. For derivatives and securities financing transactions, banks may utilize advanced simulation techniques like Monte Carlo methods to project potential future exposures, subject to supervisory validation and alignment with methods like the Standardized Approach for Counterparty Credit Risk (SA-CCR). These models must demonstrate robust predictive accuracy and avoid material biases, with EAD estimates applied across eligible asset classes including corporates, banks, sovereigns, and retail, provided banks obtain prior supervisory approval. However, Basel 3.1 restricts A-IRB modeling for certain classes like large corporates and financial institutions, reverting them to Foundation IRB with fixed parameters.^[1] Model validation adheres strictly to Basel guidelines, requiring annual backtesting to compare realized EAD against model predictions, incorporation of stress scenarios (such as mild recessions with zero growth over two quarters), and ongoing documentation of data sources and methodologies. This ensures reliability and conservatism, particularly for portfolios with limited default experience. The approach integrates with probability of default (PD) and loss given default (LGD) estimates in the overall IRB capital framework but focuses on exposure dynamics.^[20] Basel 3.1 reforms, implemented from 2023 with full effects by 2028 in key jurisdictions including the EU (2025) and UK (2026), introduce a structural EAD floor in the Advanced IRB approach: EAD cannot fall below the on-balance sheet amount plus 50% of the off-balance sheet exposure using applicable standardized CCFs. This, along with an output floor rising to 72.5%, curbs variability in internal estimates and enhances comparability. Additional provisions target low-default portfolios, mandating detailed calibration evidence; these stem from revisions to the Basel Framework's CRE32 standards. As of November 2025, these changes are progressively applied to reduce reliance on internal models for high-risk exposures.^[21]^[22]

Influencing Factors

Asset Class Variations

Exposure at default (EAD) calculations under the internal ratings-based (IRB) approaches vary significantly across asset classes to reflect the unique characteristics of each exposure type, such as drawdown behavior and inherent risks.^[1] For corporate exposures, EAD typically incorporates higher credit conversion factors (CCFs) ranging from 50% to 100% for undrawn portions of revolving facilities, like lines of credit and revolving underwriting facilities, due to the potential for rapid utilization near default.^[2] This range accounts for the flexibility of such commitments; in advanced IRB models, CCFs are estimated internally using historical data, subject to supervisory approval and validation.^[1] Additionally, EAD includes the notional amounts of guarantees and derivatives, often calculated using methods like the current exposure method (CEM) to capture replacement cost and potential future exposure.^[23] Under Basel III final reforms implemented progressively through 2025, an output floor of 72.5% harmonizes IRB EAD calculations with the standardized approach, reducing variability across asset classes.^[2] Retail exposures employ simplified EAD estimation to accommodate the diverse nature of consumer lending. For residential mortgages, EAD is generally the drawn amount plus 50% of the undrawn commitment in the standardized approach, though advanced IRB allows internal models subject to supervisory floors; fully drawn loans carry no undrawn component, resulting in EAD equal to the outstanding balance.^[2] In contrast, qualifying revolving retail exposures, such as credit cards and overdrafts, use a 75% CCF for undrawn amounts under the foundation IRB, with advanced models incorporating behavioral analyses of historical usage patterns at default to estimate actual drawdowns more accurately. Counterparty credit risk exposures, arising from derivatives and securities financing transactions, determine EAD through specialized methods that emphasize volatility and future exposure potential. The current exposure method (CEM) or the standardized approach for counterparty credit risk (SA-CCR) is applied, where EAD equals 1.4 times the sum of replacement cost and potential future exposure (PFE), with PFE calculated across asset classes like interest rates (0.5% supervisory factor) and equities (higher factors up to 32%).^[23] This incorporates add-on factors for notional amounts to project possible increases in exposure over the contract life. Specialized lending exposures, such as project finance, utilize the supervisory slotting approach when internal PD models are unavailable, assigning exposures to risk categories with predefined risk weights applied to the full commitment amount as EAD.^[24] For project finance, slots range from "strong" (70% risk weight) to "weak" (250%), effectively treating EAD as 100% of the commitment in operational phases, though construction-phase commitments may apply 100% CCFs to reflect full potential drawdown, with adjustments up to 150% in high-risk slots to account for volatility.^[24]

Mitigation and Adjustments

In collateralized transactions, regulatory frameworks allow for the reduction of exposure at default (EAD) through the comprehensive approach, which adjusts the unadjusted EAD to account for eligible collateral after applying supervisory haircuts to reflect potential declines in collateral value.^[25] The adjusted exposure (E*) is calculated as: E^* = E \times (1 + H_e) - C \times (1 - H_c - H_{fx}) where E is the current exposure value, H_e is the haircut appropriate for the exposure, C is the current value of collateral received, H_c is the haircut for the collateral, and H_{fx} is the haircut for currency mismatch (8% for a 10-business-day holding period). This ensures conservatism in the estimate.^[25] This method applies to financial collateral such as cash, securities, and gold, provided the collateral meets eligibility criteria including legal enforceability and frequent valuation.^[1] Netting agreements further mitigate EAD, particularly for derivatives and securities financing transactions, by permitting banks to calculate EAD based on the net exposure rather than gross positions under eligible master netting agreements as defined in Basel III.^[25] These agreements must demonstrate legal validity across jurisdictions and include close-out netting provisions to offset positive and negative values within the netting set, reducing the overall EAD for counterparty credit risk.^[20] This approach is applicable across asset classes like over-the-counter derivatives, where gross-to-net conversion factors further adjust the potential future exposure component.^[25] Guarantees and credit derivatives provide another layer of EAD mitigation via the substitution approach, which transfers the EAD of the protected portion to the guarantor or protection provider if their probability of default (PD) is equivalent to or lower than that of the original counterparty.^[25] Eligible instruments include unconditional guarantees from sovereigns, banks, or credit default swaps that cover the full exposure, with the unprotected portion retaining the original EAD treatment.^[1] In the IRB approaches, this substitution aligns the risk parameters with the protection seller's characteristics, subject to no double default recognition to avoid understating risk.^[20] Haircuts and volatility adjustments are integral to these mitigations, with supervisory factors applied to collateral values to capture price volatility and currency risks; for instance, cash collateral in the same currency receives a 0% haircut, while an 8% adjustment applies for currency mismatches, and equities face higher haircuts such as 20% for main index holdings to account for market fluctuations.^[25] These factors are scaled for longer holding periods beyond the standard 10 business days, ensuring the adjusted collateral value remains prudent.^[25] In the advanced IRB approach, banks may incorporate own-model haircuts, but they must be at least as conservative as supervisory values.^[1] Despite these mechanisms, limitations persist in EAD mitigation, as operational hedges—such as non-financial covenants or informal arrangements—are not recognized due to challenges in verifying enforceability and effectiveness.^[20] Additionally, the October 2025 BIS technical amendment clarifies the hedging of counterparty credit risk exposures, including adjustments to EAD for guarantees and credit default swaps, and treatment of proportionate protection without recognizing own credit risk benefits.^[11]

Applications and Implications

Role in Capital Requirements

Exposure at default (EAD) plays a central role in determining risk-weighted assets (RWA), which form the denominator for calculating regulatory capital ratios under the Basel III framework. Specifically, RWA for credit risk exposures are computed as the product of EAD and the applicable risk weight, where risk weights are derived from probability of default (PD) and loss given default (LGD) estimates in internal ratings-based (IRB) approaches.^[2] This calculation underpins the minimum Tier 1 capital requirement of 6% of RWA, with common equity Tier 1 (CET1) at 4.5%, ensuring banks hold sufficient capital against potential losses. Beyond regulatory capital, banks incorporate EAD into internal economic capital models, often using value-at-risk (VaR) methodologies to assess portfolio-level risk and allocate capital exceeding minimum requirements. These models integrate EAD with PD and LGD to estimate unexpected losses at a high confidence level, typically 99.9%, enabling more tailored risk management across business lines.^[26] In the context of the countercyclical buffer (CCyB), which ranges from 0% to 2.5% of total RWA, the buffer applies to RWA (incorporating EAD) and is activated during periods of excess credit growth to build capital resilience, allowing drawdown in downturns to mitigate procyclicality. Separately, downturn adjustments to EAD parameters, such as credit conversion factors for undrawn commitments, reflect heightened drawdowns during economic stress, increasing RWA.^[27]^[1] For instance, empirical evidence shows that borrowers approaching default increase usage of credit lines, elevating EAD and thus the buffer's impact on capital adequacy.^[28] From 2025 onward, under the Basel IV reforms finalizing Basel III—with implementation beginning at 50% in 2025 and phasing annually to 72.5% by 2030 in jurisdictions such as the EU—output floors constrain the benefits of IRB-based EAD calculations by requiring RWAs to be at least 72.5% of those under the standardized approach at full phase-in. This limitation on advanced IRB EAD modeling could increase capital requirements by 20-30% for banks heavily reliant on internal approaches, as analyzed in industry assessments.^[29]

Challenges and Best Practices

One significant challenge in estimating exposure at default (EAD) arises from the limited availability of historical default data for undrawn commitments, which introduces substantial model uncertainty due to the rarity of observed drawdowns at default. This scarcity complicates the calibration of credit conversion factors (CCFs) and direct EAD models, often leading institutions to rely on proxy data or conservative assumptions that may not fully capture tail risks.^[30] The 2024 European Banking Authority (EBA) benchmarking exercise (published in April 2025) further underscores this issue, noting variability in EAD-weighted risk parameters for retail exposures, including credit cards and other retail, with ongoing monitoring of model dispersion despite reductions in PD variability.^[31] Validation of EAD models faces heightened regulatory scrutiny, especially regarding the incorporation of downturn scenarios to ensure robustness during economic stress.^[32] Under Basel III frameworks, supervisors emphasize regular model audits, with expectations for annual reviews to assess performance against stress conditions, as outlined in ongoing guidance from the Bank for International Settlements (BIS) and national authorities.^[16] Failure to adequately validate downturn effects can result in conservative floors or output adjustments, amplifying compliance costs for institutions using internal ratings-based (IRB) approaches. To address these challenges, best practices include the cautious application of machine learning techniques for predicting drawdowns on commitments, provided they adhere to supervisory requirements for interpretability and back-testing.^[33] Such methods enhance predictive accuracy for EAD distributions by incorporating non-linear patterns in borrower behavior, but must be bounded by regulatory guidelines to avoid over-reliance on black-box models.^[34] Additionally, integrating EAD estimates with IFRS 9 expected credit loss (ECL) frameworks promotes consistency in impairment provisioning, enabling forward-looking adjustments that align regulatory capital with accounting provisions.^[35] This integration, as exemplified in practical guidance, involves using EAD as a core input for lifetime ECL calculations across exposure stages.^[36] Emerging risks from climate and geopolitical factors are increasingly influencing EAD estimation, necessitating enhanced scenario analysis to capture potential increases in drawdowns or collateral devaluations. The 2025 Office of the Superintendent of Financial Institutions (OSFI) guidelines in Canada mandate climate scenario exercises for federally regulated institutions, requiring assessments of how physical and transition risks could amplify EAD through sector-specific exposures.^[37] Similarly, geopolitical tensions elevate default risks on international portfolios, prompting banks to adjust EAD models for heightened credit conversion on cross-border commitments.^[38] A notable case illustrating these challenges occurred in the post-2008 financial crisis, where underestimation of EAD from off-balance-sheet commitments contributed to amplified losses during the liquidity squeeze that precipitated failures like Lehman Brothers.^[39] The rapid drawdown on undrawn lines amid market panic exposed flaws in pre-crisis models, leading to regulatory reforms that strengthened downturn calibration for such exposures.^[32]

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[19]
Chapter 3 – Credit risk – standardised approach - Bank of England
Nov 30, 2022 · The Basel 3.1 standards provide two approaches for calculating risk-weighted assets (RWAs) for credit risk – the standardised approach (SA) and the internal ...
[20]
CRE36 - IRB approach: minimum requirements to use IRB approach
Dec 8, 2022 · Banks' EAD estimates must be developed using a 12-month fixed-horizon approach; ie for each observation in the reference data set, default ...
[21]
[PDF] High-level summary of Basel III reforms
The revised IRB framework removes the use of the A-IRB approach – which allows banks to estimate the. PD, LGD, exposure at default (EAD) and maturity of an ...
[22]
[PDF] POLICY ADVICE ON THE BASEL III REFORMS: CREDIT RISK
16 Paragraph 125 of the revised Basel III framework states that 'Institutions must use their own estimates of EAD for undrawn revolving commitments'. Page ...
[23]
CRE52 - Standardised approach to counterparty credit risk
Jun 5, 2020 · The Standardised Approach for Counterparty Credit Risk (SA-CCR) applies to over-the-counter (OTC) derivatives, exchange-traded derivatives and long settlement ...
[24]
CRE33 - IRB approach - Bank for International Settlements
Dec 8, 2022 · This chapter sets out the calculation of risk-weighted assets and expected losses for specialised lending exposures subject to the supervisory slotting ...Missing: EAD | Show results with:EAD
[25]
CRE22 - Standardised approach: credit risk mitigation
Nov 26, 2020 · This chapter sets out the standardised approaches for the recognition of credit risk mitigation, such as collateral and guarantees.Missing: EAD | Show results with:EAD
[26]
Technical Amendment - Hedging of counterparty credit risk exposures
Oct 28, 2025 · The technical amendment was published for consultation in November 2024 and has been finalised with some adjustments to reflect stakeholder ...Missing: December | Show results with:December
[27]
[PDF] Range of practices and issues in economic capital frameworks ...
Banks usually employ one of two general modelling approaches to quantify counterparty credit risk exposures, a Value at Risk. (VaR)-type model or a Monte Carlo ...
[28]
https://www.moodys.com/web/en/us/insights/credit-risk/usage-and-exposures-at-default-of-corporate-credit-lines.html
[29]
Usage and exposures at default of corporate credit lines - Moody's
Sep 1, 2019 · Exposure at default (EAD), the usage estimation conditional upon default, enters into the regulatory capital calculation under Basel II, ...
[30]
[PDF] Basel IV Update: SA and IRB - KPMG agentic corporate services
An aggregate floor on RWA calculated under IRB will range from 50% to 72.5% of that calculated under SA, with a phase-in period of five years with the following ...
[31]
[PDF] Understanding the Exposure at Default Risk of Commercial Real ...
We study and model the determinants of exposure at default (EAD) for large U.S. construction and land development loans from 2010 to 2017.
[32]
[PDF] Guidance on credit risk and accounting for expected credit losses
... model usage and on an ongoing basis. Model validation should be conducted when the ECL models are initially developed and when significant changes are made ...Missing: EAD | Show results with:EAD
[33]
[PDF] Statistical and machine learning for credit and market risk ...
Jan 26, 2022 · ... Prediction 55. 2.1 Introduction ... The Exposure at. Default (EAD) defines the outstanding amount at the time of failure.
[34]
Machine Learning in Risk Management - Finalyse
Dec 10, 2017 · SML comprises several methods that are particularly useful for estimating crucial inputs in credit risk modelling (PD, LGD, EAD). In this short ...Supervised Machine Learning... · Decision Trees · Artificial Neural Networks...
[35]
Exposure at Default: IFRS 9 Ramifications - Aptivaa
Exposure at Default (EAD) is an estimate of a financial institution's (FI) exposure to its counterparty at the time of default.Missing: post- | Show results with:post-
[36]
[PDF] Applying IFRS - Impairment of financial instruments under IFRS 9 - EY
Apr 9, 2018 · This loan does not allow any prepayment, therefore the EAD is constant. The effective interest rate of the loan is assumed to be the ...
[37]
Climate Risk Management - Office of the Superintendent of Financial ...
Mar 7, 2025 · The Guideline establishes OSFI's expectations related to the FRFI's management of climate-related risks. It aims to support FRFIs in developing greater ...Climate Risk Management · Iii. Climate Scenario... · Vi. Osfi's Financial Data...
[38]
[PDF] Geopolitical Risk and Global Banking - Federal Reserve Board
Our bank-level analysis shows that as exposure to foreign geopolitical risk rises, the aggregate probability of default in U.S. banks' loan portfolios increases ...
[39]
[PDF] Risk Management Lessons from the Global Banking Crisis of 2008
Oct 21, 2009 · Market stresses during the crisis yielded additional information on the behavior of various on- and off- balance-sheet items during an event.