The new IFRS9, effective from January 2018, establishes a new model to calculate provisions for credit losses: the so-called “expected credit losses“ (ECL) model. It focuses on the risk that a receivable will default rather than whether a loss has been incurred and therefore includes requirements to use forward looking information.
It replaces IAS 39 which was criticised for being too complex and deferring the recognition of credit losses until too late.
IFRS9 is not sector specific and impacts a wide range of entities. It applies to all companies reporting under FRS 100 and FRS 101 (you can check your customers latest published accounts to find out what standard they are using).
The equivalent US GAAP (ASU 2016-13) standard, effective from December 2019, follows the same principles (except for the timing of recognition) so SEC filers will be impacted as well.
Entities will have to start providing for possible future credit losses even if it is highly likely that the asset will be fully collectible.
Expected credit losses represent possible outcomes that a loss might incur weighted by the probability of their occurrence.
In effect, they represent measures of an asset’s credit risk and have to take into account current and forecasted information instead of relying on historical data. Because every receivable has at least some probability of defaulting in the future, every receivable has an expected credit loss associated with it. It also has to be updated at each reporting date.
To comply with the new standard entities will therefore be required to collect and collate credit information, which for many businesses, is likely to result in the need to significantly modify current credit information systems and processes.
Credit insurance can significantly reduce the expected credit loss in the receivables portfolio because it covers the non-performance of a buyer. As we are graded AA by S&P, our own risk of default is minimal. The debt collection services we offer increase significantly the efficiency of debt recovery and therefore reduce the expected loss.
The high quality credit risk information offered to customers can also streamline ECL calculations and reduce the need for major changes to internal processes or provide a benchmark for internal estimates.