The implementation of IFRS 9 Financial Instruments has transformed how banks in Saudi Arabia, like their global counterparts, manage and account for credit risk. The cornerstone of this change is the Expected Credit Loss (ECL) model, a forward-looking approach replacing the backward-looking incurred loss model. For Saudi banks, navigating the intricacies of ECL modeling requires sophisticated data infrastructure, advanced analytical techniques, and a clear understanding of the domestic economic environment.
1. The Three Pillars of the ECL Model
ECL is fundamentally a function of three main components, each requiring dedicated modeling and calibration specific to the Saudi banking context:
- Probability of Default (PD): This is the estimate of a borrower defaulting over a given time horizon. Saudi banks must develop granular PD models, often utilizing statistical techniques like logistic regression or machine learning, calibrated using a mix of internal historical default data and external market information.
- Loss Given Default (LGD): This represents the economic loss a bank will incur if a default occurs, taking into account collateral and recovery processes. LGD models are particularly complex in Saudi Arabia due to variations in legal recovery timelines and the nature of accepted collateral (e.g., real estate, equities).
- Exposure at Default (EAD): This is the expected amount of the exposure at the time of default, especially crucial for off-balance sheet items like undrawn commitments and guarantees. EAD models must project the potential usage of revolving credit facilities based on borrower behavior and economic stress.
2. Incorporating Forward-Looking Information (FLI)
The key distinguishing feature of IFRS 9 is the mandatory inclusion of Forward-Looking Information (FLI). This necessitates banks moving beyond historical data and integrating macroeconomic forecasts into their models.
- Scenario Analysis: Saudi banks typically develop at least three economic scenarios: Base, Optimistic, and Pessimistic. These scenarios are based on key variables relevant to the Saudi economy, such as:
- Oil Price Fluctuations: A major driver of government spending and economic activity.
- Non-Oil GDP Growth: Reflecting progress under the Vision 2030 diversification efforts.
- Interest Rates (SAIBOR/SAMA Repo Rate): Influencing debt service capacity.
- Unemployment Rates: Impacting consumer credit quality.
- Weighted Averages: The final ECL is calculated as a probability-weighted average of the losses under each scenario. This introduces significant judgment and requires robust governance to validate the scenario weights and the underlying econometric linkages.
3. Staging and Significant Increase in Credit Risk (SICR)
IFRS 9 mandates classifying financial assets into three stages, impacting whether 12-month or lifetime ECL is recognized:
| IFRS 9 Stage | Definition | ECL Calculation |
| Stage 1 | No significant increase in credit risk (SICR) since origination. | 12-Month ECL |
| Stage 2 | Significant increase in credit risk (SICR) but not credit-impaired. | Lifetime ECL |
| Stage 3 | Credit-impaired (default has occurred). | Lifetime ECL |
The definition of Significant Increase in Credit Risk (SICR) is critical. While IFRS 9 permits a rebuttable presumption that an asset is in Stage 2 if it’s 30 days past due (DPD), Saudi banks primarily rely on internal metrics. These often include:
- A significant increase in the Lifetime PD relative to the PD at inception.
- Deterioration in internal credit risk ratings.
- Negative qualitative factors (e.g., sector-specific stress, covenant breaches).
4. Governance and Regulatory Landscape
The Saudi Central Bank (SAMA) plays a pivotal role in supervising ECL model adherence. Banks are required to maintain strong model governance, which involves:
- Independent Model Validation: Ensuring models are working as intended and free from bias.
- Documentation and Auditability: Maintaining clear, comprehensive records of all modeling assumptions, data sources, and methodologies.
- Integration with Capital Planning: The ECL figures directly influence regulatory capital adequacy, making model accuracy a strategic imperative.
Conclusion
IFRS 9 ECL modeling is a complex risk management discipline that requires continuous refinement in the context of the evolving Saudi economic landscape. The successful and prudent application of these models is essential for ensuring the stability and resilience of the Saudi banking sector as it supports the nation’s ambitious economic transformation under Vision 2030.
Navigating IFRS 9 compliance and ECL modeling doesn’t have to be complex.
FineIT empowers Saudi banks with end-to-end IFRS 9 implementation, model development, validation, and automation solutions — tailored to meet SAMA’s regulatory expectations and Vision 2030 objectives.
Streamline your IFRS 9 journey with confidence and accuracy.