One common error in expected credit loss (ECL) evaluation is the failure to adequately consider forward-looking information. ECL estimation requires assessing not only historical data but also future expectations and economic conditions. Failing to incorporate forward-looking information can lead to an underestimation or overestimation of credit losses.
Another common error is the reliance on outdated or insufficient data or data not directly relevant to the case without proper calibration. ECL evaluation should consider the most recent and relevant data available. Using outdated or incomplete data can result in inaccurate estimations and may not reflect the current credit risk environment.
Inadequate consideration of risk factors and assumptions is another error to avoid. ECL estimation requires careful assessment of various risk factors such as default probabilities, loss given default, and exposure at default. Failure to adequately incorporate these factors or making unrealistic assumptions can distort the accuracy of the ECL evaluation.
Lastly, insufficient documentation and lack of transparency in the ECL estimation process can be a common error. It is important to document the methodology, assumptions, and data used in ECL estimation to facilitate audit trails, review, and understanding by stakeholders.
To mitigate these errors, it is crucial to follow the applicable accounting standards (such as ASC 326 or IFRS 9 (or HKFRS 9)), use reliable data sources, incorporate forward-looking information, regularly update ECL estimates, and maintain proper documentation of the estimation process. Additionally, seeking expert advice and proper auditors’ review and validation can help reduce the likelihood of errors in ECL evaluation.
ASC 326 Financial Instruments — Credit Losses
ASC 326 requires entities to estimate ECL by considering historical information, current conditions, and reasonable and supportable forecasts that may affect the collectability of the financial assets. It emphasizes the need for forward-looking information to assess credit risk and determine appropriate allowances for credit losses.
The estimation of ECL under ASC 326 involves developing a systematic methodology that incorporates relevant factors such as the asset’s credit quality, contractual terms, historical experience, industry-specific conditions, macroeconomic indicators, and other pertinent data. The standard encourages the use of reasonable and supportable information to form an unbiased and objective estimate of credit losses.
IFRS 9 and Expected Creit Loss Provisioning
The ECL estimation process under IFRS 9 (or HKFRS 9) involves a forward-looking approach that considers a range of possible outcomes. It requires entities to assess the probability of default and the potential loss given default over the expected life of the financial asset.
IFRS 9 (or HKFRS 9) requires entities to consider a variety of factors when estimating ECL, including historical data, current market conditions, forward-looking information, and macroeconomic factors. Entities are expected to use both quantitative and qualitative information to assess credit risk and determine appropriate provisions for expected credit losses.
The standard provides guidance on different measurement approaches for ECL estimation, including a simplified approach for certain financial assets with low credit risk. The expected credit losses should be updated regularly, taking into account changes in credit risk and other relevant factors.
Why Appoint Valtech as Valuation Adviser?
Valtech’s team has provided valuation advice to over 200 listed companies in Hong Kong, China, Singapore, Taiwan, Australia, the United Kingdom, the United States and Germany.
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Valtech Advantages:
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