Page 158 - Capricorn IAR 2020

GLOSSARY OF TERMS ANNUAL FINANCIAL GLOSSARY OF TERMS STATEMENTS
NOTES TO THE CONSOLIDATED AND SEPARATE ANNUAL FINANCIAL STATEMENTS (continued)
for the year ended 30 June 2020
3. FINANCIAL RISK MANAGEMENT (continued)
3.2 Credit risk (continued)
3.2.2 Expected credit loss measurement (continued)
3.2.2.4 Forward-looking information incorporated in the ECL models
The measurement of the expected credit loss (“ECL”) allowance for financial assets requires the use of significant assumptions about future economic conditions and credit behaviour (e.g. the likelihood of customers defaulting and the resulting losses).
A number of significant judgements are required in applying the accounting requirements for measuring ECL, including:
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Determining criteria for significant increase in credit risk
Choosing appropriate models and assumptions for the measurement of ECL
Establishing the number and relative weightings of forward-looking scenarios for each type of product/market and the associated ECL
Establishing groups of similar financial assets for the purposes of measuring ECL
FRS 9 outlines a ‘three-stage’ model for impairment based on changes in credit quality since initial recognition as summarised below:
• A financial instrument that is not credit-impaired on initial recognition is classified in ‘Stage 1’ and has its credit risk
continuously monitored by the bank
• If a significant increase in credit risk (“SICR”) since initial recognition is identified, the financial instrument is moved to ‘Stage 2’
but is not yet deemed to be credit impaired
• If the financial instrument is credit-impaired, the financial instrument is then moved to ‘Stage 3’
Stage 3
The bank defines a financial instrument as in default, which is fully aligned with the definition of credit-impaired, when it meets one or more of the following criteria:
Qualitative criteria
The borrower is more than 90 days past due on its contractual payments.
Quantitative criteria
The borrower meets unlikeliness to pay criteria, which indicates the borrower is in significant financial difficulty. These are instances where:
• The borrower is in long-term forbearance
• The borrower is deceased
• The borrower is insolvent
• The borrower is in breach of financial covenants
• It is becoming probable that the borrower will enter bankruptcy
The criteria above have been applied to all financial instruments held by the Group and are consistent with the definition of default used for internal credit risk management purposes.
The Group estimates provision for impairments for stage 3 (non-performing loans) on an individual loan basis. Each loan’s impairment is calculated as exposure less a discounted value of collateral held.
Stage 1 and 2
The assessment and calculation of ECL incorporates forward-looking information (“FLI”). The forecast of economic variables, regression analysis and expert judgement is applied and confirmed through internal governance structures to apply a forward-looking view for the ECL calculation. With the simultaneous impact of a multiyear recession as well as COVID-19 pandemic on the southern African region, statistical inference needs to be supplemented by qualitative expert judgment and input to ensure reliable and plausible forecasts are achieved. The Group has performed historical analysis and identified key macroeconomic inputs impacting the default rates of the Group’s assets and in determining key credit risk ratios and overlays. Historical relationships between macroeconomic data and default rates have been identified as inputs into the FLI model. These relationships are used to project future default rates based on current macroeconomic forecasts. The Group mainly applied forecasted domestic macroeconomic conditions as FLI. Regression modelling techniques were used for this purpose.
The Group applied GDP changes as the main macroeconomic indicator in the FLI modeling process. Changes in monetary interest rates were excluded from the modelling process. As part of COVID-19 stimulus packages, the central banks of Botswana and Namibia reduced interest rates to stimulate GDP growth. The effect of monetary policy rates is therefore encapsulated in the GDP forecasts applied in the modelling process.