Expected credit losses ecl

What Are Expected Credit Losses (ECL)?

Expected credit losses (ECL) represent a forward-looking estimate of the credit losses that are anticipated over the entire lifetime of a financial instrument. This concept is a core component of modern financial accounting standards, requiring entities to provision for potential losses before they are actually incurred. Unlike previous models that only recognized losses once a loss event had occurred, the ECL model mandates a proactive assessment of credit deterioration, considering historical experience, current conditions, and reasonable and supportable forecasts. The purpose of expected credit losses is to provide a more accurate and timely reflection of an entity's exposure to credit risk on its financial assets.

History and Origin

The concept of expected credit losses gained prominence following the 2008 global financial crisis. Prior to this, accounting standards, such as IAS 39 (International Accounting Standard 39) in International Financial Reporting Standards (IFRS), primarily used an "incurred loss" model. Under this model, banks and other financial institutions could only recognize an impairment charge when there was objective evidence of a loss event, often leading to delayed recognition of credit losses. Critics argued that this lag exacerbated the crisis by obscuring the true financial health of institutions.

In response, accounting standard setters embarked on a journey to develop a more forward-looking impairment model. The International Accounting Standards Board (IASB) introduced IFRS 9 Financial Instruments, which became effective on January 1, 2018, globally for entities reporting under IFRS. IFRS 9 replaced IAS 39 and introduced the expected credit loss model for recognizing impairment on financial assets.¹² Simultaneously, in the United States, the Financial Accounting Standards Board (FASB) developed Accounting Standards Codification (ASC) Topic 326, commonly known as Current Expected Credit Losses (CECL), which became effective for public business entities that are SEC filers for fiscal years beginning after December 15, 2019.¹⁰, ¹¹ The aim of both standards was to ensure more timely recognition of losses.⁹

Key Takeaways

• Expected credit losses (ECL) are a forward-looking estimate of anticipated credit losses over the lifetime of a financial instrument.
• The ECL model was introduced by IFRS 9 and CECL to replace the "incurred loss" model, promoting earlier recognition of potential losses.
• Entities must consider historical data, current conditions, and reasonable forecasts to estimate expected credit losses.
• The calculation typically involves assessing probability of default, loss given default, and exposure at default.
• Implementing ECL models can be complex, requiring robust data management and sophisticated modeling techniques.

Formula and Calculation

The calculation of expected credit losses generally involves three key components:

1. Probability of Default (PD): The likelihood that a borrower will default on their obligations over a specified period.
2. Loss Given Default (LGD): The proportion of the exposure that an entity expects to lose if a default occurs, typically expressed as a percentage of the outstanding amount.
3. Exposure at Default (EAD): The total amount of exposure an entity expects to have on the loan or financial instrument at the time of default.

The basic formula for expected credit losses for a single exposure can be expressed as:

For portfolios of similar assets, entities aggregate these calculations, often segmenting their loan loss provisioning based on shared credit risk characteristics. The estimation should reflect a probability-weighted outcome, considering multiple scenarios, and incorporating reasonable and supportable forward-looking information.⁸

Interpreting Expected Credit Losses (ECL)

Interpreting expected credit losses involves understanding not just the absolute number, but also the assumptions and methodologies underpinning it. A higher ECL indicates a greater anticipation of future defaults and financial losses, which can signal deteriorating asset quality or a more pessimistic economic outlook. Conversely, a lower ECL suggests a more robust portfolio or an optimistic economic forecast.

Under IFRS 9, financial assets are categorized into three stages, influencing how expected credit losses are recognized:

• Stage 1: Financial instruments with no significant increase in credit risk since initial recognition. Entities recognize 12-month expected credit losses.
• Stage 2: Financial instruments that have experienced a significant increase in credit risk since initial recognition but do not have objective evidence of impairment. Entities recognize lifetime expected credit losses.
• Stage 3: Financial assets that have objective evidence of impairment at the reporting date. Entities recognize lifetime expected credit losses and also accrue interest income on the net carrying amount.

This staging mechanism means that even healthy assets require some provision for expected credit losses, reflecting the inherent risk over a year. The shift to lifetime expected credit losses for Stage 2 assets signals a higher level of concern, requiring a more substantial allowance on the balance sheet.

Hypothetical Example

Consider "Alpha Bank" which provides a new business loan of $1,000,000 to "Beta Corp." The loan has a contractual term of five years.

At the time of origination (initial recognition), Alpha Bank assesses Beta Corp.'s creditworthiness and the prevailing economic conditions. Based on historical data for similar loans and reasonable forecasts, they estimate:

• Probability of Default (PD) over the next 12 months: 0.5%
• Loss Given Default (LGD): 40% (meaning they expect to recover 60% of the loan if default occurs)
• Exposure at Default (EAD): $1,000,000

Under the Expected Credit Loss (ECL) model, even though Beta Corp. is a new, performing loan (Stage 1), Alpha Bank must recognize 12-month expected credit losses:

This $2,000 is recognized as a loan loss provisioning expense on Alpha Bank's profit or loss statement and an allowance for expected credit losses on its balance sheet.

Now, suppose in the second year, Beta Corp. experiences a downturn, and its industry faces significant headwinds. While Beta Corp. is still making payments, Alpha Bank's internal models indicate a significant increase in its default risk since origination. The loan moves to Stage 2, and Alpha Bank now needs to estimate lifetime expected credit losses. They re-assess:

• Lifetime Probability of Default (PD): 8%
• Loss Given Default (LGD): 45% (slightly higher due to industry distress)
• Exposure at Default (EAD) at that point: $900,000 (after some principal repayments)

Alpha Bank would then adjust its allowance to reflect this new lifetime expected credit loss, recognizing any increase as a further expense. This example highlights the proactive nature of ECL, requiring adjustments as credit quality changes, even before an actual default occurs.

Practical Applications

Expected credit losses are fundamental to financial institutions, impacting various aspects of their operations and reporting. Banks use ECL models for calculating their loan loss provisioning, which directly affects their reported profit or loss and, consequently, their equity. This has significant implications for capital adequacy and regulatory compliance.

Beyond accounting, ECL models inform a bank's internal credit risk management practices. They guide lending decisions, pricing of loans, and portfolio management strategies. Institutions utilize these models to perform stress testing to assess how their portfolios would perform under adverse economic scenarios, thereby helping to gauge their resilience and maintain adequate capital requirements. The Federal Reserve, for instance, has emphasized the importance of robust capital frameworks and financial institution resilience.⁷

Furthermore, the methodologies used for calculating expected credit losses can be quite diverse, including discounted cash flow methods, loss-rate methods, roll-rate methods, and probability-of-default methods.⁶ The implementation of ECL models requires sophisticated data analytics and forecasting capabilities, integrating macroeconomic variables into their assessments of future credit quality.

Limitations and Criticisms

Despite the intent of expected credit losses to improve financial reporting transparency, the model has faced several limitations and criticisms:

One primary concern is the significant judgment required in estimating expected credit losses. The forward-looking nature necessitates assumptions about future economic conditions, which can be highly subjective and complex. This can lead to variability in ECL estimates across institutions, potentially impacting the comparability of financial statements.⁵ The IMF has noted that banks may have leeway in implementing the framework, which could influence impairment charges.⁴

Another criticism revolves around the potential for procyclicality. In an economic downturn, forward-looking forecasts naturally become more pessimistic, leading to higher expected credit loss provisions. This increased provisioning reduces banks' capital, which could, in turn, constrain their lending activities, potentially exacerbating the economic downturn. Conversely, in good economic times, lower provisions could encourage excessive lending. While regulators aim to mitigate this through counter-cyclical buffers, it remains a recognized challenge.³

Data management also presents a significant hurdle. Accurate ECL calculation demands extensive historical data on defaults, recoveries, and macroeconomic indicators, which some institutions, particularly smaller ones, may lack or find challenging to collect and manage effectively.² The complexity and model risk associated with these new standards have been highlighted by various financial bodies.¹

Expected Credit Losses (ECL) vs. Incurred Credit Losses (ICL)

The fundamental difference between expected credit losses (ECL) and incurred credit losses (ICL) lies in their timing and triggers for recognition.

The Incurred Credit Loss (ICL) model, which was the prevailing standard (e.g., under IAS 39) before the introduction of ECL, only allowed for the recognition of a loss when there was objective evidence that a loss event had already occurred. This meant that a bank could not recognize a potential loss on a loan until an actual trigger event, such as a missed payment, a breach of covenant, or the borrower entering bankruptcy, had transpired. This approach was criticized for being backward-looking and for leading to the delayed recognition of losses, potentially masking the true financial health of a financial institution until it was too late.

In contrast, the Expected Credit Loss (ECL) model is forward-looking. It requires entities to estimate and provision for anticipated losses over the entire lifetime of a financial asset from the moment it is originated or acquired. This means that even perfectly performing loans will have an expected credit loss provision based on the likelihood of future default risk and the potential loss given that default. The ECL model seeks to incorporate all available information, including historical data, current conditions, and reasonable and supportable forecasts of future economic conditions. This proactive approach aims to provide more timely and relevant information to users of financial statements, thereby enhancing transparency and stability in the financial system. The confusion often arises because both models deal with credit losses, but their fundamental philosophical approaches to when those losses are recognized are diametrically opposed.

FAQs

What types of financial instruments are subject to Expected Credit Losses (ECL)?

The ECL model applies broadly to financial assets measured at amortized cost and fair value through other comprehensive income (FVOCI) for debt instruments. This includes, but is not limited to, loans, debt securities, lease receivables, trade receivables, and certain loan commitments and financial guarantee contracts.

How do macroeconomic forecasts influence Expected Credit Losses (ECL)?

Macroeconomic forecasts are a critical input to the ECL calculation. Factors such as GDP growth, unemployment rates, interest rates, and commodity prices can significantly impact a borrower's ability to repay debt. The ECL model requires entities to incorporate reasonable and supportable future forecasts of these macroeconomic variables into their probability of default and loss given default estimates, ensuring a more dynamic and realistic assessment of credit risk.

Is Expected Credit Loss (ECL) the same as CECL?

No, while both operate on an expected loss principle, Expected Credit Losses (ECL) refers specifically to the impairment model introduced by IFRS 9, the international accounting standard. CECL stands for Current Expected Credit Losses and is the equivalent accounting standard under U.S. GAAP (Generally Accepted Accounting Principles), as per FASB ASC Topic 326. Both aim to recognize credit losses earlier than previous incurred loss models but have some differences in their specific application and scope.

Does the ECL model apply to non-financial companies?

Yes, while most prominent in the banking sector due to their extensive lending activities, the ECL model applies to any entity that holds financial instruments in scope of IFRS 9. This means non-financial companies with trade receivables, contract assets, or intercompany loans must also assess and recognize expected credit losses on these assets.