STEP 01
Choose relevant macro drivers
Map Gross Domestic Product (GDP) growth, inflation, policy rates and other available forecasts to the actual credit-risk drivers used in the model.
A CONSENSUS USE CASE
Economic assumptions that stand up to scrutiny.
Banking risk functions, credit analysts and credit rating agencies need to explain where their macroeconomic assumptions come from, when they were available and why they are appropriate. Independent professional forecasts offer a documented external benchmark for modelling of forward-looking Expected Credit Losses (ECL) under International Financial Reporting Standard 9 (IFRS 9), credit risk analysis, stress testing and regulatory submissions. For sovereign and corporate credit analysts, they can also contextualise economic risk assessments. They are an input to professional judgement—not credit ratings, compliance approval or ready-made ECL models.
THE QUESTION
“How do we substantiate the economic assumptions behind credit and risk assessments?”
Banking, Credit & RiskCONSENSUS ECONOMICS
FORECASTS RELEVANT TO YOUR WORK
Economic forecasts for banking, credit and risk
Match the information to your decision, then choose an appropriate publication or data format.
01 / FORECASTS
GDP growth, inflation and interest rate assumptions for relevant portfolios and economies.
02 / EVIDENCE
Historical survey forecasts, changes over time and differences between economists’ estimates.
03 / PRACTICAL OUTPUT
An assumptions register for credit analysis, IFRS 9 review or model governance.
04 / ACCESS
Historical Excel series and publications; model scenarios remain the bank’s responsibility.
The examples below use illustrative figures. Available data and usage rights depend on the service.
01 / THE SITUATION
Here’s the decision.
The model governance review
A bank is preparing its IFRS 9 ECL assumptions for a quarterly model review.
The risk team has an internal baseline for growth, inflation and interest rates, plus management-designed economic scenarios. Reviewers want to see whether the assumptions are consistent with independent expectations, how they changed between survey vintages (the forecasts recorded at each original survey date) and where the bank has applied a documented overlay. The bank combines those independent expectations with its own portfolio assumptions, scenarios and model governance.
THE OBJECTIVE
Build a traceable external-assumptions pack and document the bank’s scenario and model judgements separately.
02 / THE WORKFLOWBenchmarking IFRS 9 assumptions
Benchmarking IFRS 9 assumptions
for model governance.
Benchmark the macroeconomic assumptions in the bank’s approved model against dated professional survey vintages, then document scenario design, overrides and governance decisions separately.
STEP 02
Benchmark the baseline
Compare internally approved assumptions with same-country, same-horizon professional consensus forecasts and record survey publication dates.
STEP 03
Challenge scenario choices
Use the professional range and revisions to question assumptions; construct internally coherent downside and upside paths and probability weights separately.
STEP 04
Document model governance
Maintain a record of source vintages, overrides, validation, scenario choices and approval responsibilities for reporting and review.
03 / A PRACTICAL EXAMPLEA worked example.
A worked example.
The numbers in context.
Where does the bank’s IFRS 9 macro baseline diverge from the external view?
A team overseeing Expected Credit Losses (ECL) models matches its internal assumptions with a professional survey from the same reference date and forecast year. The figures below are illustrative.
WORKING EXAMPLEIllustrative example
Swipe sideways to view the complete example table
| Matched model driver | Bank baseline | Outside benchmark |
|---|---|---|
| GDP growth | 1.80% | 2.35% |
| Consumer price inflation | 3.20% | 2.75% |
| Policy interest rate | 4.00% | 3.75% |
THE PRACTICAL DELIVERABLE
A model-review table with external source, vintage, bank assumption, variance, override rationale, reviewer and sign-off status.
SUITABLE STARTING POINTExplore historical forecast data
Regular survey histories support documented reviews of economic assumptions.
These example values are illustrative, not survey data. Explore an actual sample forecast publication
GO FURTHER / APPLY THE FULL DISTRIBUTION
The range behind
The range behind
the average.
Use the panel mean, ranges and historical revisions to challenge a bank-defined macroeconomic baseline. Modelled upside/downside paths and their likelihoods must be established independently by the institution. Standard deviation measures the spread of individual forecasts; “pp” means percentage points.
FORECAST DISTRIBUTIONIllustrative GDP growth forecasts for one country and horizon
Consensus mean2.40%
Low1.10%
High3.60%
Standard deviation0.60 pp
BEHIND THE AVERAGE
Illustrative individual views of 1.70% and 3.00% sit on either side of the mean. They signal disagreement among forecasters, not approved model scenarios or probabilities.
LOW · 1.10%HIGH · 3.60%
Consensus mean Selected individual forecasts
A PRACTICAL SCENARIO OR RESEARCH TEST
Illustrative exampleTranslate a forecast comparison into a controlled modelling review
Swipe sideways to review the scenario table
| Review lens | Reference input | Governance question |
|---|---|---|
| External benchmark | 2.40% GDP | Is the internally approved baseline consistent with the matched survey vintage? |
| Lower-growth sensitivity | 1.10% GDP | Which portfolio sensitivities need review? This survey low is not a standalone stress scenario. |
| Higher-growth sensitivity | 3.60% GDP | How would an internally consistent upside path affect ECL assumptions? |
All numbers are synthetic examples. Forecaster highs/lows and dispersion are not confidence intervals, joint macro scenarios, or probability weights. IFRS 9 ECL estimates require institution-defined scenarios, appropriate weighting, credit risk modelling and governance.
OTHER APPLICATIONS FOR THIS AUDIENCE
Further applications.
IFRS 9 Expected Credit Losses (ECL)
Challenge macroeconomic assumptions used in Probability of Default (PD) and Expected Credit Losses (ECL) modelling.
USE THE DATA
Match survey vintages to reporting dates and model horizons; record reasons for any internal overrides.
OUTPUT
An ECL assumptions register for governance review.
Stress testing and risk monitoring
Put institution-designed sensitivities in the context of external professional expectations.
USE THE DATA
Compare matched levels and revisions, then construct coherent multi-variable stress paths separately.
OUTPUT
A documented baseline and stress-scenario challenge memo.
Regulatory submissions and model validation
Support traceable economic inputs when models are reviewed or submitted to supervisors.
USE THE DATA
Maintain source provenance, variable definitions, vintages, rationale and validation evidence.
OUTPUT
An independently benchmarked macro input evidence pack.
04 / WHAT YOU TAKE AWAYA practical result.
A practical result.
Ready to review.
Build an output that can be explained, challenged and updated as forecasts change.
How the consensus is calculatedEXAMPLE DELIVERABLE↗
Illustrative banking deliverableAn auditable macro-assumptions register
A review pack matching internal ECL macroeconomic assumptions with external forecast benchmarks, survey dates and the rationale for overrides.
WHAT IT CONTAINS
Adapt the example to your own data and decision process.✓Internal baseline versus independent forecast and survey vintage
✓Scenario design, weights and limitations maintained by the bank
✓Model owner, validation notes and supporting evidence
05 / THE CONSENSUS ADVANTAGEWhy the data
Why the data
matter here.
Match the forecast inputs to the decision. The average, the distribution and the historical record serve different purposes.
RELEVANT FORECAST INPUTS
01
GDP, inflation and rate forecasts for credit-relevant economies
02
Historical vintages for macroeconomic and sovereign risk assessments
03
Individual forecast ranges and dispersion where available
WHY CONSENSUS ECONOMICS
Independent inputs with an identifiable vintage.
A comparable professional baseline helps credit rating analysts and banking risk teams challenge house economic assumptions. Historical releases show what was available at each reporting date; forecast ranges and revisions help flag uncertainty. Neither the consensus mean nor the panel high/low provides a credit rating, scenario probabilities, portfolio credit-loss outcomes or regulatory sign-off.
1,000+professional forecasters1989first survey
COMMON QUESTIONS
Practical questions, clear answers.
Do consensus forecasts supply IFRS 9 scenarios or probability weights?
No. They provide independent economic reference forecasts. The institution designs, weighs and validates its own scenarios and credit-loss models.
How can historical forecasts support model review?
Compare forecasts for the same country, indicator and future period across survey dates. Licensing options are available for professional modelling.