Since the adoption of IFRS 9 across the Gulf Cooperation Council (GCC) countries, banks and financial institutions have transformed their approach to credit risk assessment by implementing Expected Credit Loss (ECL) models. Unlike the incurred loss model under IAS 39, IFRS 9 requires forward-looking ECL software estimation, compelling institutions to incorporate macroeconomic forecasts, credit deterioration trends, and internal risk assessment frameworks.
This article explores current ECL modeling practices in GCC countries—including Saudi Arabia, the UAE, Qatar, Bahrain, Kuwait, and Oman—highlighting regulatory expectations, implementation challenges, and emerging best practices.
1. Regulatory Landscape in the GCC
Each central bank in the GCC has issued its own guidance on IFRS 9 implementation while maintaining alignment with international standards:
Saudi Arabia – SAMA
The Saudi Central Bank (SAMA) issued detailed implementation guidelines and conducted impact assessment exercises across banks. It emphasizes conservative provisioning, especially for retail and SME portfolios.
UAE – CBUAE
The Central Bank of the UAE (CBUAE) focuses on model validation and governance. It has issued circulars for periodic stress testing, provisioning adequacy, and ECL disclosures.
Qatar – QCB
The Qatar Central Bank requires banks to document model assumptions, validate risk parameters, and ensure that macroeconomic overlays are evidence-based.
Bahrain – CBB, Kuwait – CBK, Oman – CBO
These regulators have encouraged granular segmentation, use of internal and external credit ratings, and back-testing of ECL estimates to ensure reliability.
2. Key Components of ECL Models in GCC
Under IFRS 9, the ECL model requires three key parameters:
- Probability of Default (PD) – Likelihood that a borrower defaults in a given time horizon.
- Loss Given Default (LGD) – Proportion of exposure lost if a default occurs.
- Exposure at Default (EAD) – Outstanding exposure at the time of default.
Staging Criteria
- Stage 1: Performing loans (12-month ECL)
- Stage 2: Significant increase in credit risk (lifetime ECL)
- Stage 3: Credit-impaired loans (lifetime ECL with higher provisions)
3. ECL Modeling Approaches in the GCC
a) Internal Rating-Based Models
Many GCC banks rely on internal rating systems to assess PDs, particularly for corporate and sovereign exposures. These models use financial ratios, management quality, and sectoral risk factors.
b) Behavioral Scoring Models
For retail portfolios (credit cards, personal loans), statistical scoring models are widely used to assess risk and forecast defaults.
c) Macroeconomic Forecast Integration
Banks are required to incorporate forward-looking macroeconomic variables such as oil prices, interest rates, GDP growth, inflation, and unemployment into ECL models. Given the GCC’s reliance on hydrocarbons, oil price forecasts are particularly critical.
d) Use of Scenario Analysis
ECL models use multiple economic scenarios (baseline, adverse, optimistic), with weighted probabilities to reflect possible future outcomes. Some banks use three scenarios, while others adopt more granular approaches with five or more.
4. Common Challenges Faced in the GCC
a) Data Limitations
Limited historical default data, especially for long-term PD calibration, is a challenge for smaller banks or newer portfolios.
b) Incorporating Forward-Looking Information
Aligning macroeconomic forecasts with ECL estimates has been complex due to high volatility in oil prices and regional economic uncertainties.
c) Model Risk Management
Some institutions face difficulties in model validation, back-testing, and governance—especially where ECL models are outsourced or adapted from foreign models.
d) IFRS 9 vs Prudential Requirements
Discrepancies between accounting provisions (IFRS 9) and regulatory capital requirements have required banks to maintain parallel reporting frameworks.
5. Best Practices Emerging in the Region
a) Establishment of ECL Governance Committees
Banks have created IFRS 9 oversight committees to ensure cross-functional alignment between Risk, Finance, and Compliance.
b) Regular Model Calibration and Validation
Annual or semi-annual recalibration of ECL models, along with independent validation, is becoming a regulatory expectation.
c) Enhanced Disclosure and Audit Trails
Improved transparency in model assumptions, staging criteria, and overlays is helping in regulatory reviews and external audits.
d) Use of Advanced Analytics
Larger banks in the UAE and Saudi Arabia are integrating machine learning algorithms and AI-based early warning systems into their risk models to improve credit risk predictions.
6. Case Study: ECL Implementation in Saudi Arabia
A leading Saudi bank implemented an internally developed PD model using 5 years of historical corporate loan data. It integrated oil price volatility and government spending levels as macro drivers. The bank applied a three-scenario framework (baseline, optimistic, stressed) with periodic back-testing and adjustments.
Result: The ECL provision accuracy improved by 18%, and SAMA acknowledged the bank’s model as a benchmark for regional peers.
7. Outlook for ECL Modeling in the GCC
As IFRS 9 matures in the region, future developments in ECL modeling may include:
- More granular segmentation by industry and geography.
- Increased regulatory scrutiny on overlays and post-model adjustments.
- Integration of climate risk into forward-looking assessments.
- Automation of staging triggers based on real-time data and system integration.
Conclusion
Expected Credit Loss modeling in the GCC has evolved significantly post-IFRS 9 implementation. While banks have made strong progress in aligning with global best practices, they continue to face challenges in data availability, model governance, and macroeconomic forecasting. Through ongoing regulatory collaboration, adoption of advanced analytics, and robust validation practices, the region is steadily building a resilient credit risk framework that supports financial stability and transparency.