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Report on the Audit of the Separate Financial Statements (Continued) FOR THE YEAR ENDED 31 DECEMBER 2022
Key audit matter
How our audit addressed the key audit matter
Expected credit loss allowance against project assets
As at 31 December 2022, the Corporation’s project assets amounted to USD 780.9 million (2021: USD 664.5 million) representing 27% (2021: 22%) of total assets. The expected credit loss (ECL) allowance as at 31 December 2022 was USD 99.4 million (2021: USD 108.3 million).
The audit of impairment allowances for project assets is a key area of focus because of its size and due to the significance of the estimates
and judgments used in classifying project assets into various stages, determining related allowance requirements, and the complexity of the judgements, assumptions and estimates used in the Expected Credit Loss models.
The Corporation recognizes allowances for expected credit losses (ECLs) at an amount equal to 12-month ECL (Stage 1) or full lifetime ECL (Stage 2). A loss allowance for full lifetime ECL is required for a financial instrument if the credit risk on that financial instrument has increased significantly since initial recognition.
ECLs are a probability-weighted estimate of the present value of credit losses. These are measured as the present value of the difference between the cash flows due to the Corporation under the contract and the cash flows that the Corporation expects to receive arising from the weighting of multiple future economic scenarios, discounted at the asset’s effective profit rate. The Corporation employs statistical models for ECL calculations and the key variables used in these calculations are probability of default (PD), loss given default (LGD), and exposure at default (EAD), which are defined in Note 3 to the separate financial statements.
The material portion of the project assets is assessed for the significant increase in credit risk (SICR) and measurement of ECL. This requires management to capture all qualitative and quantitative reasonable
and supportable forward-looking information while assessing SICR, or while assessing credit-impaired criteria for the exposure. Management judgement may also be involved in manual staging override in accordance with the Corporation’s policies.
1. We obtained an understanding of the project assets business process, the credit risk management process, the policy for impairment and credit losses and the estimation process of determining impairment allowances for project assets to counterparties and the ECL modelling methodology and evaluated the design and implementation of relevant controls within these processes.
2. We assessed and evaluated the design and implementation of automated and / or manual controls over:
• approval, accuracy and completeness of impairment allowances
and governance controls over the monitoring of the model, through key management and committee meetings that form part of the approval process for project asset impairment allowances;
• model outputs; and
• the recognition and measurement of impairment allowances
3. On a sample basis, we selected project assets and assessed and evaluated:
• the Corporation’s identification of SICR (Stage 2), the assessment of credit-impaired classification (Stage 3) and whether relevant impairment events had been identified in a timely manner and classification of project assets into various stages and the determination of defaults/individually impaired exposures.
• The forward-looking information incorporated into the impairment calculations by involving our specialists to challenge the multiple economic scenarios chosen and related weighting applied.
• the assumptions underlying the impairment allowance calculation, such as estimated future cash flows and estimates of recovery period.
• the calculation methodology to determine if it complied with the requirements of AAOIFI FAS 30
• the post model adjustments and management overlays (if any) in order to assess these adjustments and assessed the qualitative factors which were considered by the Corporation to recognize any post model adjustments, in case of data or model limitations. Where such post model adjustments were applied, we assessed those post model adjustments and the governance process around them.
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