Expected Credit Losses under IFRS 9

Introduction

Overview of IFRS 9 and its significance:

IFRS 9 Financial Instruments is a standard issued by the International Accounting Standards Board (IASB). It introduces significant changes to the financial reporting landscape, particularly through the Expected Credit Loss (ECL) framework. The ECL model fundamentally shifts impairment recognition from the backward-looking incurred loss approach to a forward-looking methodology. Under IFRS 9, entities must recognize credit losses from the point of initial recognition, considering past events, current conditions, and reasonable and supportable forecasts of future economic scenarios. This ensures more timely and realistic provisioning for credit losses by improving transparency and addressing criticisms that the incurred loss model delayed recognition during economic downturns such as the Global Financial Crisis (GFC) of 2008. 

Evolution of IFRS 9

• 2008-09:  Following the GFC, the IASB and the US Financial Accounting Standards Board (FASB) initiated a joint project to overhaul financial instruments accounting, focusing on enhancing transparency and aiming for fair value reporting.
  In November 2009, the IASB issued the first part of IFRS 9, focusing on classification and measurement of financial assets, thereby replacing parts of IAS 39.
• 2010: IASB reissued IFRS 9, adding requirements for classification and measurement of financial liabilities, while retaining IAS 39’s derecognition rules.
• 2011-13: Further exposure drafts and additions were made, including hedge accounting rules in 2013, allowing entities to choose between IFRS 9 or IAS 39 hedge accounting.
• 2014: The complete version of IFRS 9 was published on 24 July 2014, consolidating all components and fully replacing IAS 39 guidance.
• 2016: The IASB issued amendments to IFRS 4 Insurance Contracts, allowing insurers to defer application of IFRS 9 until the effective date of the new insurance standard (IFRS 17), to mitigate accounting mismatches.
• 2017: Repayment Features with Negative Compensation (Amendments to IFRS 9) was issued by the IASB to address the concerns about how IFRS 9 classifies particular prepayable financial assets.
• 2018: IFRS 9 became effective for annual periods beginning on or after 1 January 2018, with early adoption allowed.
• 2020: The standard was amended by Annual Improvements to IFRS Standards 2018–2020 (fees in the ‘10 per cent’ test for derecognition of financial liabilities). 
• 2021: IASB issued Initial Application of IFRS 17 and IFRS 9 — Comparative Information (Amendment to IFRS 17) to permit entities that first apply IFRS 17 and IFRS 9 at the same time to present comparative information about a financial asset as if the classification and measurement requirements of IFRS 9 had been applied to that financial asset before.
• 2024: Amendments to the Classification and Measurement of Financial Instruments (Amendments to IFRS 9 and IFRS 7, Annual Improvement to IFRS Accounting Standards – Volume 11 and Contracts Referencing Nature-dependent Electricity (Amendments to IFRS 9 and IFRS 7) were published.

These developments reflect the IFRS 9 framework’s continuous evolution to address practical implementation issues, emerging risks, and sustainability-linked financial instruments.

Understanding the expected credit loss framework

Definition and scope of ECL

The unbiased and probability-weighted amount of credit losses, determined by evaluating a range of possible outcomes. ECL represents the present value of all cash shortfalls, i.e.,the difference between the contractual cash flows and the expected cash flows to be received, discounted using the effective interest rate (EIR) determined at initial recognition. The scope of ECL applies broadly to financial assets measured at amortized cost or fair value through other comprehensive income (FVTOCI), lease receivables, loan commitments, and financial guarantees.

Exclusions:

While IFRS 9’s ECL model has wide applicability, certain instruments or scenarios follow specific guidance or are excluded. 

• Financial Instruments Measured at Fair Value Through Profit or Loss (FVTPL): ECL does not apply; their fair value changes are already recognized in profit or loss
• Trade Receivables and Contract Assets: For these, entities can choose to apply a simplified approach where lifetime ECLs are recognized 
• Without a significant financing component
  Entities must apply the simplified approach, which requires recognizing lifetime ECL from initial recognition, without the need to track changes in credit risk over time. Since there is no significant financing component, the effective interest rate is considered zero, and discounting of expected cash shortfalls is generally not required.
• With  Significant Financing Component
  Entities can choose between the general approach (three-stage model) or the simplified approach (lifetime ECL). Under the general approach, entities assess whether there has been a significant increase in credit risk since initial recognition and measure ECL as either 12-month or lifetime ECL accordingly. The present value of expected cash shortfalls is discounted using the original effective interest rate, reflecting the time value of money
• Lease Receivables: Similar to trade receivables, entities can apply a simplified approach for lease receivables
• Financial Guarantee Contracts and Loan Commitments: These are included in the scope of the ECL model, but specific guidance is provided on how to measure ECLs for these instruments as follows-
• ECL for financial guarantee contracts and loan commitments is measured as the present value of expected credit losses over the period of exposure to credit risk. For financial guarantees, this reflects expected payments to reimburse the holder; for loan commitments, it considers expected losses from possible loan drawdowns. In both cases, ECL must be unbiased, probability-weighted, and incorporate all reasonable and supportable information.
• Short-term Trade Receivables and Contract Assets: For these, entities may not need to consider forward-looking information extensively due to their short-term nature.

Three-Stage Impairment Model

Under IFRS 9, the Expected Credit Loss (ECL) model is structured into a three-stage impairment framework based on the level of deterioration in credit quality since initial recognition.

How are the 3 stages determined?

• Stage 1: 12-month ECL:At initial recognition, and for financial instruments where there has not been a significant increase in credit risk since initial recognition, entities recognize 12-month ECL. This represents the portion of lifetime ECLs that result from default events possible within the next 12 months.
• Stage 2: Lifetime ECL (Not Credit-Impaired):If there is a significant increase in credit risk (SICR) since initial recognition but the financial instrument is not yet credit-impaired, entities recognize lifetime ECL. This means recognizing the expected losses over the entire remaining life of the financial instrument.
• Stage 3: Lifetime ECL (Credit-Impaired):When a financial instrument becomes credit-impaired, entities continue to recognize lifetime ECL, but interest revenue is calculated based on the net carrying amount (i.e., the gross carrying amount less the loss allowance) rather than the gross carrying amount.

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Criteria for transitioning between stages

Transition from Stage 1 to Stage 2

• Significant Increase in Credit Risk: A financial asset moves from Stage 1 to Stage 2 when there is a significant increase in credit risk since initial recognition. This is typically assessed using various indicators such as changes in credit ratings, significant changes in the terms of the financial instrument, or adverse changes in the economic environment.
• Quantitative and Qualitative Factors: Entities must consider both quantitative factors (e.g., changes in default probabilities) and qualitative factors (e.g., changes in management’s expectations) to determine if there has been a significant increase in credit risk.

Transition from Stage 2 to Stage 3

• Credit-Impaired Status: A financial asset moves to Stage 3 when it becomes credit-impaired. This occurs when one or more events that have a detrimental impact on the estimated future cash flows of the financial asset have occurred.
• Indicators of Credit Impairment: Indicators include significant financial difficulty of the issuer or borrower, a breach of contract (e.g., default or past due status), or the lender granting concessions that it would not otherwise consider.

Reversal of Stages:

• Improvement in Credit Risk: If the credit risk of a financial asset improves such that the significant increase in credit risk since initial recognition no longer exists, the asset can move back from Stage 2 to Stage 1.
• Recovery from Credit-Impaired Status: : If a credit-impaired financial asset shows significant improvement and no longer meets the criteria for being credit-impaired, it can transition from Stage 3 to Stage 2.

ECL framework

While IFRS 9 permits flexibility in selecting methodologies, real-world application demands judicious design, calibration, and governance of the ECL model. The standard components—Probability of Default (PD), Loss Given Default (LGD), and Exposure at Default (EAD)—are just the starting point. Their interaction with business models, data infrastructure, and regulatory expectations defines the quality of the resulting ECL estimate.

The standard formula for calculating ECL is: ECL = PD * LGD * EAD

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How the Flow Rate Method Works

• Tracking Aging Buckets: Receivables are categorized into aging buckets based on how long they have been outstanding (e.g., 0-30 days, 31-60 days, 61-90 days, 91+ days).
• Calculating Flow Rates: The flow rate is the probability that receivables in one aging bucket will move into the next, more delinquent bucket in the subsequent period. For example, the flow rate from 0-30 days to 31-60 days is calculated by observing historical data on how much of the receivables in the 0-30 days bucket aged into the 31-60 days bucket in the next period.   
• Deriving Loss Rates: The loss rate is derived from the flow rates and represents the probability that receivables in a given bucket will eventually default (often proxied by reaching the 91+ days bucket, which is assumed to represent default or loss). The loss rate is typically calculated as the cumulative probability of flowing into the default bucket.
• Applying to Exposure: The calculated PD (loss rate) is then applied to the total exposure (e.g., the outstanding receivables balance at the reporting date) to estimate the ECL.   

Integration of Macroeconomic Variables (MEVs)

In line with IFRS 9’s forward-looking principle, entities are required to incorporate macroeconomic variables (MEVs) into the estimation of Probability of Default (PD). These variables — such as GDP growth, unemployment rates, interest rates, inflation, and sector-specific indicators — are factored into credit risk models to reflect expected changes in the economic environment. Typically, entities use multiple economic scenarios (e.g., base, upside, downside), assign probability weightings, and model the impact of MEVs on default probabilities through regression techniques or macro-overlay adjustments. This ensures that PDs are responsive to future economic conditions rather than being solely historical. The choice of variables, scenario assumptions, and overlay methodology should be well-documented, justified, and regularly back-tested. 

Key Points in Application

• The PD derived from flow rates is conditional on information available at the reporting date. Post-reporting date collections or payments should not reduce the exposure on which PD is applied, as the calculation is based on the balance as of the reporting date.
• The flow rate method produces a historical, point-in-time estimate of default probability, which must need adjustment for forward-looking information as required by IFRS 9.
• The method assumes that the 91+ days bucket represents default, consistent with IFRS 9’s rebuttable presumption that a default occurs no later than 90 days past due.  
• The flow rate approach is often combined with a provision matrix, where loss rates for each aging bucket are used to calculate expected credit losses by multiplying the exposure in each bucket by the corresponding loss rate.

Loss Given Default (LGD)

LGD is the estimated percentage of loss an organization will incur if a customer, counterparty, or borrower defaults on a financial obligation. It reflects the portion of the exposure that is not expected to be recovered, after considering factors like collateral realization, contractual rights, legal recovery, and any credit enhancements

In an IFRS 9 context, LGD is applied in both 12-month and lifetime ECL calculations and should be forward-looking, incorporating expectations of recovery outcomes over time.

Practical Insights across Industries

• In real estate or leasing, recovery depends on market resale value, asset condition, and transaction costs (e.g., liquidation expenses, brokerage).
• In telecom and utilities, LGD may reflect contract enforcement limitations, write-offs of unpaid usage, or the cost of reacquiring customers.
• For industrial or project-based businesses, LGD may be influenced by supply chain disruptions, customer concentration, or project abandonment risk.
• LGD should account for historical recovery experience, but also adjust for
  forward-looking risks, such as economic conditions, legal delays, or technological obsolescence of underlying assets.
• Credit enhancements like guarantees, retention money, or deposits can reduce LGD, but must be legally enforceable and supported by recovery evidence.

Exposure at Default (EAD)

Exposure at Default (EAD) refers to the total amount an entity expects to be exposed to when a counterparty or customer defaults. It includes: In telecom and utilities, LGD may reflect contract enforcement limitations, write-offs of unpaid usage, or the cost of reacquiring customers.

• For industrial or project-based businesses, LGD may be influenced by supply chain disruptions, customer concentration, or project abandonment risk.
• LGD should account for historical recovery experience, but also adjust for forward-looking risks, such as economic conditions, legal delays, or technological obsolescence of underlying assets.
• Credit enhancements like guarantees, retention money, or deposits can reduce LGD, but must be legally enforceable and supported by recovery evidence.

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Forward-Looking Approach in ECL & challenges in the implementation of the same

The forward-looking ECL approach under IFRS 9 represents a paradigm shift in credit risk management by emphasizing early loss recognition based on comprehensive data analysis. 

While this  approach improves transparency and timeliness in financial reporting, it’s implementation poses significant challenges.

• Data Challenges -Institutions must collect extensive internal and external data (e.g., payment history, unemployment rates). Ensuring data quality and accessibility is a major challenge.
• Model Development -Creating robust models that incorporate historical trends, current conditions, and future forecasts requires advanced expertise in quantitative modelling. Regular calibration is necessary due to changing economic environments. 
• IT Infrastructure -Existing systems often require upgrades to handle complex calculations and large datasets efficiently. Integration of forecasting tools is resource-intensive.
• Cost Implications -Implementation involves high costs related to system upgrades, staff training, external consulting services, and increased audit requirements.
• Governance -Effective governance frameworks are needed for scenario selection, model validation, and ensuring compliance with IFRS 9 standards.
• Cross-Departmental Collaboration -Implementation requires coordination between finance, risk management, IT teams, and external auditors—adding complexity to organizational workflows.

Sector-Specific Considerations

Sector-specific considerations for ECL under IFRS 9 require a deep understanding of the unique risks and factors affecting each industry, alongside general macroeconomic considerations.

Financial Institutions:

• Loan Commitments and Financial Guarantees: Financial institutions need to consider the potential drawdowns on loan commitments and the increased probability of default during economic stress. Financial guarantee contracts also require careful assessment as they may exhibit higher risk in periods of economic downturn.
• Revolving Credit Facilities: For revolving credit facilities, entities must consider factors like historical exposure to credit risk, the time it takes for defaults to occur after a significant increase in credit risk, and credit risk management actions.

Retail and Consumer Finance:

• Credit Cards and Consumer Loans: These sectors often involve revolving credit facilities where customers may frequently draw down and repay balances. The ECL calculation should account for the likelihood of customers failing to pay off future balances, especially if they transition from being transactors to revolvers.

Real Estate and Construction:

• Property Market Fluctuations: Changes in property values and rental income can significantly impact the creditworthiness of borrowers in this sector. ECL models should incorporate these market dynamics. However, it is pertinent to note that Property, Plant and equipment will be still impaired under IFRS 36.

Energy and Commodities:

• Price Volatility: Fluctuations in commodity prices can affect the financial health of companies in these sectors. ECL assessments should consider the impact of price volatility on borrowers’ ability to repay debts.

Technology and Telecommunications:

• Rapid Technological Changes: The rapid pace of technological advancements can lead to obsolescence and changes in market demand, affecting the creditworthiness of companies in these sectors.

Some of the general considerations across sectors are as follows:

• Macroeconomic Scenarios: All sectors should consider macroeconomic conditions and how they might affect credit risk. This includes factors like GDP growth, interest rates, and unemployment levels.
• Sector-Specific Factors: Besides macroeconomic scenarios, financial institutions are encouraged to incorporate expected changes in sector-specific factors into their ECL assessments.
• Regular Review and Documentation: The methodology for calculating ECL should be regularly reviewed to ensure it remains effective and aligned with actual loss experiences. Documentation of these reviews and any changes to methodologies is crucial.

Climate Risk Integration

Climate-related physical and transition risks can have direct and material implications for credit exposures. Under IFRS 9, entities should consider these risks when estimating Expected Credit Losses (ECL), based on reasonable and supportable information available at the reporting date. 

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Disclosures Requirements for ECL Under IFRS 7

The transition to IFRS 9 from previous standards like IAS 39 involves significant changes in disclosure practices. 

Entities must provide detailed information about how they have applied the new classification and measurement requirements, including any changes in financial asset classifications and their impact on ECLs. 

The disclosure requirements for ECL under IFRS 9 are designed to enhance transparency and stakeholder understanding by providing detailed insights into credit risk management practices, the measurement of ECLs, and their impact on financial statements. These requirements support more informed decision-making by stakeholders and improve the comparability of financial reporting across entities.

New Requirements for Transparency

1. Credit Risk Management Practices: Entities must disclose information about their credit risk management practices, including methods used to measure ECLs, assumptions made, and data used in these calculations. This helps stakeholders understand how credit risks are identified and managed.
2. Quantitative and Qualitative Information: Disclosures must include both quantitative data (e.g., amounts of ECLs recognized) and qualitative explanations (e.g., reasons for changes in ECLs over time). This dual approach ensures that stakeholders can assess both the magnitude and underlying causes of credit losses.
3. Credit Risk Exposure: Entities are required to disclose information about their credit risk exposure, including significant concentrations of credit risk. This helps stakeholders evaluate the potential impact of credit losses on the entity’s financial position.
4. Reconciliation of Loss Allowances: A reconciliation of movements in loss allowances is necessary to show how ECLs have changed over time. This provides transparency into how credit conditions and management decisions affect provisioning.

Enhancing Stakeholder Understanding Through Detailed Reporting

1. Understanding Financial Position and Performance: The disclosures under IFRS 9 are designed to enable stakeholders to evaluate the significance of financial instruments for an entity’s financial position and performance6. By understanding how ECLs are calculated and managed, stakeholders can better assess an entity’s financial health.
2. Nature and Extent of Risks: Detailed reporting helps stakeholders understand the nature and extent of risks arising from financial instruments, both during the reporting period and at the reporting date. This includes insights into how economic conditions and borrower-specific factors influence credit risk.
3. Risk Management Practices: By disclosing credit risk management practices, entities provide stakeholders with insights into how they manage these risks. This transparency can enhance stakeholder confidence in the entity’s ability to navigate challenging credit environments.
4. Comparability Across Entities: The standardized disclosure requirements under IFRS 9 improve comparability across different entities. This allows stakeholders to compare the credit risk management strategies and outcomes of various companies, facilitating more informed investment decisions.

Practical Considerations for ECL Implementation

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Future Trends in ECL Modeling under IFRS 9

The future of ECL modeling under IFRS 9 is defined by technological innovation and adaptability. AI-driven solutions are transforming risk assessment processes by improving accuracy, scalability, and efficiency while addressing regulatory challenges through robust governance frameworks. Institutions that invest in these advancements will be better equipped to navigate the complexities of economic uncertainties and stringent compliance requirements.

Advances in Data Analytics and AI-Driven Risk Models

• Integration of AI and Machine Learning: Entities must disclose information about their credit risk management practices, including methods used to measure ECLs, assumptions made, and data used in these calculations. This helps stakeholders understand how credit risks are identified and managed.
• Model Risk Management: AI models introduce complexities that require robust risk management frameworks. This includes ensuring explainability, transparency, and compliance with regulatory requirements. Tools like Arthur AI’s monitoring platform automate reporting and help manage biases or errors in AI models. Centralized
  model-building processes are being adopted to enhance speed and collaboration among stakeholders during AI model development.
• Improved Data Governance: High-dimensional data and feature engineering are critical for AI models. Institutions are investing in data governance infrastructure to ensure quality, consistency, and security. This supports the dynamic training of models that adapt to evolving data inputs.
• Adapting to Evolving Economic and Regulatory Landscapes:

1. Dynamic Economic Scenarios: IFRS 9 requires the incorporation of forward-looking macroeconomic scenarios into ECL calculations. Advanced tools now allow institutions to integrate multiple weighted scenarios dynamically, addressing economic uncertainties like inflation or market volatility. The ability to quickly adapt models to reflect changes in economic conditions is becoming a competitive advantage for financial institutions.
2. Regulatory Compliance: As regulators closely monitor AI/ML applications in financial services, firms must align their model risk management (MRM) frameworks with supervisory expectations. This includes validating models for compliance, ensuring ethical use of alternative data, and managing risks associated with model opacity. Enhanced disclosures and controls are required under IFRS 9 to capture extensive data for lifetime ECLs and significant credit risk changes.
3. Flexible Model Design: Institutions are developing adaptable methodologies that can respond effectively to regulatory changes or economic shifts. This includes designing systems capable of handling both statistical models and expert judgment for low-default asset classes.

Conclusion

In conclusion, the adoption of the ECL model under IFRS 9 marks a significant advancement in credit risk management across industries in Saudi Arabia. By emphasizing a forward-looking and data-driven approach, ECL enhances the accuracy and timeliness of loss recognition, promoting greater financial transparency and resilience. While its implementation requires organizations to strengthen their data capabilities and risk assessment processes, the long-term benefits include improved decision-making, enhanced stakeholder confidence, and alignment with global accounting standards. As Saudi Arabia continues to evolve its economic landscape, embracing the ECL framework will be instrumental in fostering sustainable growth and ensuring robust financial health across sectors.