When IFRS 9 came into force, it changed something fundamental about how financial institutions think about credit losses. The old model was backward-looking: you provisioned for losses that had already occurred. IFRS 9 introduced a forward-looking requirement. Institutions must now estimate expected credit losses based on where the economy is going, not just where it has been.
That shift created a new question: where do you get the macroeconomic assumptions that feed your ECL models, and how do you ensure they reflect the full range of possible outcomes?
For many institutions, the answer has been a single economist or a single-source forecast. That approach carries growing audit risk and is an increasingly harder position to defend at model review.
The Forward-Looking Requirement in Practice
IFRS 9 does not specify exactly which macroeconomic variables to use, or which data sources to rely on. What it requires, under §5.5.17, is that institutions incorporate reasonable and supportable forward-looking information into their ECL estimates. In practice, that means projecting how variables such as GDP growth, unemployment, inflation, policy interest rates, and exchange rates will affect the probability of default across a loan portfolio.
For many institutions this translates into three concrete deliverables: a baseline scenario, an upside scenario, and a downside scenario. Each scenario requires a set of macroeconomic assumptions. And each assumption needs to be documented, justifiable, and increasingly independently sourced.
A Single Forecast is a Single Opinion
The limitation of a single-source forecast is straightforward. A single-source forecast gives auditors one analytical view. It may be systematically optimistic or pessimistic, and it may not capture the range of plausible outcomes that a well-governed IFRS 9 model should consider. IFRS 9 requires estimates to reflect an unbiased, probability-weighted amount determined by evaluating a range of possible outcomes. A single baseline forecast can therefore create model-review challenges unless it is supplemented with defensible alternative scenarios, probability weights, and documentation.
This is not a hypothetical concern. Across the markets where IFRS 9 adoption is most active, including Sub-Saharan Africa, MENA, and Latin America, audit firms are increasingly asking credit risk teams to demonstrate that their macro inputs come from an independent, named, third-party source. Internal estimates and single-source forecasts are facing a higher bar at model review.
The requirement is explicit. §5.5.17 requires ECL estimates to reflect “an unbiased and probability-weighted amount determined by evaluating a range of possible outcomes.” A single point forecast, by itself, is unlikely to satisfy the range-of-outcomes requirement. Unless the provider supplies defensible alternative scenarios or probability weights, it gives one view rather than a distribution.
What a Consensus Forecast Gives Your IFRS 9 Model That a Single Source Cannot
A Consensus forecast is not a single economist’s view. It is an aggregation of independent contributions from a broad panel of forecasters, including economists at banks, research institutions, multilateral organizations, and independent consultancies, synthesized into a single output that captures both the most likely outcome and the range of disagreement across contributors.
The minimum and maximum across contributors provide a natural basis for downside and upside scenario calibration. The spread between panelists reflects genuine uncertainty, which is exactly what IFRS 9’s forward-looking requirement is designed to capture.
There are four specific ways Consensus forecasts strengthen an IFRS 9 ECL model:
- Independence and audit credibility. A named, third-party Consensus source is demonstrably independent of the institution’s internal assumptions. It supports audit credibility and model governance by providing an independent third-party benchmark, which can help address auditor concerns around internal bias.
- Scenario range built in. Consensus outputs, including median, mean, minimum, and maximum, map directly to the baseline, optimistic, and pessimistic scenarios commonly used in IFRS 9 models. Consensus forecasts can provide a defensible starting point for scenario calibration, reducing, but not eliminating, the need for additional analytical judgment.
- Frequency and recency. IFRS 9 models should reflect current macroeconomic conditions. Monthly Consensus updates ensure that ECL assumptions are not based on stale projections, a limitation that annual official publications cannot address.
- Geographic breadth. For institutions with exposure in emerging and frontier markets, Consensus forecasts with genuine panelist depth are essential. This is where the gap between generalist data providers and Consensus providers is most significant.
The Geography Factor
IFRS 9 adoption is no longer concentrated in Europe and major international banks. It is accelerating across Sub-Saharan Africa, the Middle East, and Latin America, driven by banking sector modernization and tightening audit standards.
In these markets, two things are true simultaneously: the demand for external macro data is growing rapidly, and the supply of quality Consensus coverage from most providers is weakest. For credit risk teams at institutions operating in frontier markets, the choice of macro data provider is not just a question of analytical quality. It is a question of whether the data can withstand regulatory and audit scrutiny.
FocusEconomics aggregates forecasts from over 1,000 contributing economists and covers 198+ countries, including frontier markets where alternative providers offer limited or no Consensus data. FocusEconomics’ Consensus is particularly strong in the regions where IFRS 9 adoption is newest and the need for credible, independent macro inputs is most critical.
A Practical Starting Point
If you are building or reviewing an IFRS 9 ECL model and evaluating your macroeconomic data sources, the questions to ask are straightforward. Can you name the source in your model documentation? Does it reflect an independent, multi-contributor view rather than a single analytical perspective? Does it cover the specific countries in your loan book with sufficient panelist depth? Finally, does it provide a scenario range, not just a point estimate?
If the answer to any of those questions is uncertain, a Consensus forecast is the stronger foundation.
Get in touch to talk about how we can provide the best macro data solutions according to your specific needs: https://www.focus-economics.com/strengthen-your-ifrs-9-compliance-with-consensus-forecast-data/