Implementing IFRS 9 (International Financial Reporting Standard 9) in Kenya has been a transformative journey for the financial sector, shifting the focus from historical “incurred” losses to a forward-looking Expected Credit Loss (ECL) framework. While the standard is designed to be quantitative and data-driven, the reality of the Kenyan economic landscape often requires a delicate balance of statistical precision and Management Judgement, frequently exercised through Overlay Adjustments.
Understanding Overlay Adjustments
In the context of IFRS 9, an Overlay Adjustment (also known as a Post-Model Adjustment or PMA) is a manual intervention to the output of an ECL model. These adjustments are necessary when the underlying statistical models fail to capture specific risks or emerging economic realities.
Why Overlays are Critical in Kenya
- Data Gaps: Many institutions, particularly SACCOs and mid-tier banks, may lack long-term historical data for specific loan products, requiring management to “layer” expert judgment over model outputs.
- Macroeconomic Volatility: Kenya’s economy can be sensitive to external shocks—such as interest rate cap fluctuations, election cycles, or climate-related events (e.g., droughts impacting the agricultural sector). Models trained on “normal” years may not accurately predict losses during these outliers.
- Late-Breaking Events: If a significant economic event occurs just before the reporting date, there may not be enough time to recalibrate the formal model. An overlay allows management to reflect this risk immediately.
The Role of Management Judgement
Management judgement is not about “adjusting numbers to meet targets.” Rather, it is a structured, governed process intended to ensure the financial statements represent a true and fair view.
Key Areas of Judgement
- Significant Increase in Credit Risk (SICR): Deciding exactly when a loan moves from Stage 1 to Stage 2 requires defining “significant.” Management must judge whether a 30-day delinquency or a specific macro-indicator (like a drop in tea prices for a farmer) constitutes a permanent shift in risk.
- Scenario Weighting: IFRS 9 requires multiple forward-looking scenarios (Optimistic, Base, and Pessimistic). Management must assign a probability (e.g., 10%, 60%, 30%) to these scenarios, which significantly impacts the final provision.
- Qualitative Factors: Factors like management quality in a borrowing company or changes in the regulatory environment in Kenya cannot always be quantified by a PD (Probability of Default) model.
Governance and Regulatory Oversight
The Central Bank of Kenya (CBK) and the Institute of Certified Public Accountants of Kenya (ICPAK) place high importance on the governance of these adjustments.
| Feature | Requirement |
| Documentation | Every overlay must have a clear “basis of estimate” document explaining why the model was insufficient. |
| Audit Trail | Auditors must be able to trace the manual adjustment back to a specific risk factor or data limitation. |
| Reversal | Overlays should be temporary. If a risk becomes permanent, it should eventually be integrated into the core model rather than remaining an overlay. |
Challenges in the Kenyan Context
- Model Over-Reliance: There is a risk that management becomes too reliant on overlays to manage earnings volatility, which can lead to “transparency” issues with regulators.
- Skill Gap: Developing and validating complex ECL models requires high-level actuarial and statistical skills, which can be expensive for smaller Kenyan firms.
- Procyclicality: There is concern that during Kenyan economic downturns, the combined effect of models and overlays could lead to a “cliff effect,” where provisions spike so sharply that they restrict further lending.
Conclusion
Overlay adjustments and management judgement are not “loopholes” but essential tools for navigating the complexities of the Kenyan credit market. For IFRS 9 to be effective, these judgements must be grounded in robust governance, transparent disclosure, and reliable data. As the Kenyan financial sector matures under IFRS 9, the goal is to move from “reactive overlays” to “predictive models” that more naturally capture the unique pulse of the East African economy.
At FineIT, we help Kenyan banks, SACCOs, MFIs, and insurers implement IFRS 9 with confidence by combining robust ECL models, well-governed management overlays, and CBK- and audit-ready documentation. From data gap solutions and macroeconomic scenario design to overlay governance frameworks, our IFRS 9 specialists ensure your provisions are accurate, transparent, and regulator-aligned.
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