IFRS 9 Impairment How to calculate ECL

IFRS 9 is a reporting standard for financial instruments that replaces IAS39 (the previous incurred loss standard) with the introduction of provisions for expected credit losses (ECLs) on all financial assets, such as those held to collect contractual cash flows, or held with the possibility of being sold.

The date for adoption was January 1, 2018 and is mandatory for public non-financial corporations (and financial institutions) across a number of jurisdictions outside the United States, including many European countries.

The two key changes introduced by the IFRS 9 accounting standard are:

a. Calculation and provisions must be performed on all affected financial assets, not just the impaired ones, as per the standard it replaces

b. New expected credit loss calculations

Additional challenges will be presented when making assessments for low default asset classes, and companies may find it difficult to access models and sufficient data history.

Calculation of Expected Credit loss for Trade Receivables:

  1. Applying the ‘simplified approach’ using a provision matrix:

For short term trade receivables, e.g. trade debtors with 30-day terms, the determination of forward looking economic scenarios may be less significant given that over the credit risk exposure period a significant change in economic conditions may be unlikely, and historical loss rates might be an appropriate basis for the estimate of expected future losses. A provision matrix is nothing more than applying the relevant loss rates to the trade receivable balances outstanding (i.e. a trade receivable aged analysis). For example, an entity would apply different loss rates depending on the number of days that a trade receivable is past due. Depending on the diversity of its customer base, the entity would use appropriate groupings if its historical credit loss experience shows significantly different loss patterns for different customer segments. Although it is a simplified approach, care should be taken in the following areas:

  • Determining appropriate groupings: Where historical loss rates are used as an input, sufficient due diligence should be performed on the historical loss data to validate the completeness and accuracy of key parameters, including shared credit risk characteristics (for example maturity dates). If material to the result, a separate provision matrix should be applied to appropriate groupings of receivables based on shared credit risk characteristics. Entities should examine historical credit loss rates to identify if there are significantly different loss patterns for different customer segments.
  • Adjusting historical loss rates for forward looking information: It should be determined whether the historical loss rates were incurred under economic conditions that are representative of those expected to exist during the exposure period for the portfolio at the balance sheet date. It is important to consider whether application of a loss rates approach is appropriate for the portfolio and whether the calculated historical loss rates have been appropriately adjusted to reflect the expected future changes in the portfolio condition and performance based on the information available as at the reporting date.

We will be using the following steps to calculate the Historical Rate:

Step 1 Determine the appropriate groupings

There is no explicit guidance or specific requirement in IFRS 9 on how to group trade receivables, however, groupings could be based on geographical region, product type, customer rating, collateral or trade credit insurance and type of customer (such as wholesale or retail).

To be able to apply a provision matrix to trade receivables, the population of individual trade receivables should first be aggregated into groups of receivables that share similar credit risk characteristics. When grouping items for the purposes of shared credit characteristics, it is important to understand and identify what most significantly drives each different group’s credit risk.

Step 2 Determine the period over which observed historical loss rates are appropriate

Once the sub-groups are identified, historical loss data needs to be collected for each sub-group. There is no specific guidance in IFRS 9 on how far back the historical data should be collected. Judgment is needed to determine the period over which reliable historical data can be obtained that is relevant to the future period over which the trade receivables will be collected. In general, the period should be reasonable – not an unrealistically short or long period of time. In practice, the period could span two to five years.

Step 3 Determine the historical loss rates

Now that sub-groups have been identified and the period over which loss data will be captured has been selected, an entity determines the expected loss rates for each sub-group sub-divided into past-due categories. (i.e. a loss rate for balances that are 0 days past due, a loss rate for 1-30 days past due, a loss rate for 31-60 days past due and so on). To do so, entities should determine the historical loss rates of each group or sub-group by obtaining observable data from the determined period.

IFRS 9 does not provide any specific guidance on how to calculate loss rates and judgement will be required.

Step 3.1 Determine the total credit sales and total credit loss over the selected historical period

Once an entity has selected the period over which it will collect data, it should identify the total credit sales made and the total losses suffered on those sales. The data captured over the relevant period should be combined and averages should be calculated. However, for simplicity the example used reflects information obtained for one financial year.

Once  the total credit sales and credit losses are known, the relevant ‘aging’ needs to be determined. An entity will need to analyses its data to determine how long it took for it to collect all of its receivables (i.e. migration of balances through the ageing bands) and to determine the proportion of balances in each past-due category that was ultimately not received. To put it another way, what proportion of debtors that reach each past-due metric were ultimately collected? The reason this is done is to determine an expectation based on past history of the proportion of receivables that “go bad” once they get to a specific point past due.

Once the cash receipts have been analysed and the balances outstanding have been grouped, the historical loss rates should be calculated. The historical loss rate is calculated below by taking the total credit loss and dividing it by the balance outstanding in each aging grouping

Step 4 Consider forward looking macro-economic factors and conclude on appropriate loss rates

The historical loss rates calculated in Step 3 reflect the economic conditions in place during the period to which the historical data relates. While they are a starting point for identifying expected losses they are not necessarily the final loss rates that should applied to the carrying amount. Using the example we have used throughout, the historical loss rates were calculated from the 2017 financial year. However, what if at the 2018 reporting date information was available that in one specific geographical region unemployment was expected to rise because of a sudden economic downturn and that increase in unemployment was expected to result in increases in defaults in the short term? In this circumstance the historical loss rates will not reflect the appropriate expected losses and will need to be adjusted. In this will be an area of significant judgement and will be a function of reasonable and supportable forecasts of future economic conditions.

Step 5 Calculate the expected credit losses

The expected credit loss of each sub-group determined in Step 1 should be calculated by multiplying the current gross receivable balance by the loss rate

To learn more please contact SKGY Financial accounting team.

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