Expected Credit Loss refers to the estimated amount of loss a bank can expect to incur on a financial asset over its lifetime. In the landscape of financial risk management, understanding and implementing effective credit loss models is crucial for financial institutions worldwide. The two prominent frameworks, Basel III and IFRS 9, play pivotal roles in guiding organizations through the assessment and recognition of expected credit losses.
What is ECL (Expected Credit Loss)?
Expected Credit Loss includes both expected defaults and the potential for delayed or reduced payments. Prior to the implementation of Basel III and IFRS9, accounting standards relied on an “incurred loss” model, recognizing losses only when evidence of default was present. This backward-looking approach often led to delayed recognition of losses, impacting financial stability and transparency.
Basel III: A Pillar of Capital Adequacy
The Basel Committee on Banking Supervision introduced ECL as part of its Basel III framework, aiming to strengthen the capital adequacy of banks. The Basel Accords are a set of international regulations issued by the Basel Committee on Banking Supervision, which govern the minimum capital requirements for banks. ECL plays a pivotal role in Basel III, particularly under the Credit Value Adjustment (CVA) risk framework. Here, ECL is measured as the 12-month expected loss (12-M ECL) for exposed credit derivatives, reflecting the potential shortfall in the net present value of the derivatives due to counterparty credit risk.
Basel offers two approaches for calculating 12-M ECL: the Standardized Approach and the Internal Ratings-Based Approach (IRB). The Standardized Approach uses a prescribed formula based on credit rating and exposure maturity, while the IRB allows banks to leverage their own internal credit risk models for greater accuracy.
IFRS9: Ushering in a New Era of Transparency
International Financial Reporting Standard 9 (IFRS9) takes a more comprehensive approach to ECL, emphasizing the importance of forward-looking information and economic scenarios. It requires banks to recognize ECL at all times, starting from the initial recognition of a financial asset. This approach leads to higher initial provisions and greater volatility in earnings compared to Basel. Additionally, IFRS9 allows the use of more sophisticated models for ECL calculations, potentially providing a more accurate picture of risk.
IFRS9 further classifies financial assets into three stages based on credit risk:
Stage 1:
Recognition of 12-month expected credit losses for financial assets which have not undergone a significant increase in credit risk since their initial recognition.
Stage 2:
Recognition of lifetime expected credit losses for financial assets that have experienced a significant increase in credit risk since initial recognition.
Stage 3:
Recognition of lifetime expected credit losses for financial assets identified as credit-impaired.
IFRS 9 considers a broader range of information, including historical data, current conditions, and reasonable and supportable forecasts, in estimating ECL. This forward-looking approach is designed to provide a more timely reflection of credit risk in financial statements.
Comparison between Basel vs IFRS9
Firstly, let’s understand the similarities
- Both Basel and IFRS9 require banks to estimate the lifetime ECL of financial assets, reflecting the total loss expected throughout the loan’s life. This is a shift from the “incurred loss” model, where provisions were made only when losses became evident.
- Both frameworks use a forward-looking approach, incorporating past events, current conditions, and future forecasts to assess credit risk. This leads to more timely recognition of potential losses, enhancing financial stability.
Now, let’s delve into the divergence:
Scope:
- Basel: Basel primarily focuses on credit risk exposures held by banks, setting minimum capital requirements based on risk-weighted assets. Credit risk is only one of the components influencing capital needs.
- IFRS9: Whereas IFRS9 primarily regulates financial reporting, dictating how ECLs are measured and recognized in financial statements. This directly impacts reported profits and losses.
Measurement:
- Basel: Basel offers two approaches – “Standardized” and “Internal Ratings-Based” (IRB). The standardized approach relies on external credit ratings and predefined risk weights, while IRB allows banks to use their internal models for more precise risk assessment.
- IFRS9: Provides more flexibility but also demands more sophistication. Banks can select from various measurement models, including “Probability of Default” (PD), “Loss Given Default” (LGD), and “Effective Interest Rate” (EIR).
Timing:
- Basel: Recognizes 12-month ECL for regulatory capital purposes, focusing on near-term default risks. This is distinct from lifetime ECL measurement.
- IFRS9: Recognizes lifetime ECL in the profit or loss statement at each reporting date, reflecting the complete expected loss picture.
Implications:
- Basel: There is less volatility when it comes to provisions, but may not fully capture evolving risks. The standardized approach, while simpler, can be less accurate for complex financial instruments.
- IFRS9: Increased provisioning, leading to potentially higher volatility in reported earnings. However, it provides a more transparent and forward-looking view of credit risk.
Use of Collateral:
- Basel: This allows for the consideration of collateral in assessing credit risk.
- IFRS 9: Requires a more holistic view, considering the impact of collateral on expected credit losses.
Convergence and Challenges:
- Basel and IFRS9 have moved towards aligning their ECL frameworks, aiming for consistency and minimizing reporting burdens.
- Implementation challenges remain, particularly for smaller banks lacking advanced modeling capabilities. Data quality, system upgrades, and model validation are key hurdles.
Implications for Financial Institutions and Investors
The adoption of Basel and IFRS9 has significant implications for financial institutions and investors:
- Increased Capital Requirements: ECL recognition under IFRS9 often leads to higher provisions, impacting capital ratios and potentially restricting lending activity.
- Enhanced Risk Management: Both frameworks necessitate robust risk management practices and sophisticated modeling capabilities to accurately estimate ECL.
- Greater Transparency: Improved disclosure provides investors with a more comprehensive understanding of credit risk and potential losses.
- Impact on Valuation and Investment Decisions: ECL estimates can influence asset valuations and investment decisions, requiring careful consideration by investors.
Challenges and the Road Ahead: Embracing the Future of ECL
Implementing both Basel III and IFRS9 presents several challenges for banks:
- Data Availability and Quality: Accurate ECL calculations require robust data and sophisticated modeling techniques.
- System Upgrades and Integration: Existing systems may require significant upgrades to accommodate the new frameworks.
- Human Resources and Expertise: Banks may need to invest in training and hiring personnel with expertise in ECL modeling and interpretation.
Despite these challenges, embracing the future of ECL offers several benefits:
- Improved Risk Management: Accurate ECL estimates enable better risk management and capital allocation decisions.
- Enhanced Financial Stability: Stronger capital adequacy helps in safeguarding the financial system from potential credit losses.
- Greater Investor Confidence: Improved transparency and disclosure of risk can boost investor confidence in banks.
Conclusion:
Lastly, both Basel and IFRS 9 aim to address credit risk and Expected Credit Loss, but from different perspectives and with different objectives. Basel focuses on regulatory capital adequacy, allowing flexibility in the use of internal models. In contrast, IFRS 9 adopts a forward-looking approach, emphasizing timely recognition of credit losses in financial statements. As we move forward, convergence between Basel III and IFRS9 becomes a desirable goal to reduce complexity and ensure consistency in ECL calculations. Continuous innovation in modeling techniques and data analysis will further help in refining the accuracy and effectiveness of ECL estimations.
