Applying the expected credit loss model to trade receivables using a provision matrix

As published in 'Accountancy Cyprus', the Journal of the Institute of Certified Public Accountants of Cyprus, March 2019 edition.


IFRS 9 Financial Instruments is effective for annual periods beginning on or after 1 January 2018. IFRS 9 introduces a new impairment model based on expected credit losses. This is different from IAS 39 Financial Instruments: Recognition and Measurement where an incurred loss model was used.

Many assume that the accounting for financial instruments is an area of concern only for large financial entities like banks. This is not the case. Almost every entity has financial instruments that they need to account for. In particular, almost every entity has trade receivables and the new financial instruments standard changes the way entities must think about impairment.

In this article, we focus on the new impairment requirements of IFRS 9 and specifically on applying the simplified approach using a provision matrix for trade receivables, contract assets recognised under IFRS 15 and lease receivables under IAS 17 (or IFRS 16).


IFRS 9 introduces a new impairment model based on expected credit losses, resulting in the recognition of a loss allowance before the credit loss is incurred. Under this approach, entities need to consider current conditions and reasonable and supportable forward-looking information that is available without undue cost or effort when estimating expected credit losses. IFRS 9 sets out a 'general approach' to impairment. However, in some cases, this 'general approach' is overly complicated and therefore some simplifications were introduced.

Under IFRS 9's 'general approach', a loss allowance for lifetime expected credit losses is recognised for a financial instrument if there has been a significant increase in credit risk (measured using the lifetime probability of default) since initial recognition of the financial asset. If, at the reporting date, the credit risk on a financial instrument has not increased significantly since initial recognition, a loss allowance for 12-month expected credit losses is recognised. In other words, the 'general approach' has two bases on which to measure expected credit losses; 12-month expected credit losses and lifetime expected credit losses.

However is not practical or of any benefit to require entities to apply the general approach for short-term receivables. Consequently, IFRS 9 allows entities to apply a 'simplified approach' for trade receivables, contract assets and lease receivables. The simplified approach allows entities to recognise lifetime expected losses on all these assets without the need to identify significant increases in credit risk.


The table below summarises the available accounting policies:


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.
  • Adjusting historical loss rates for forward looking information.

Below we provide a stepped approach for applying a provision matrix.

Step by Step approach

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.

In our example we consider a telecommunication company that sells both handsets and network access on 24-month contracts. It might group receivables from wholesale customers and retail customers separately because they have different credit risk characteristics. Furthermore, it might group receivables related to handsets (representing a receivable due over 24 months) separately from receivables related to month-to-month network access charges because the risk characteristics related to the period of credit exposure will be different. It could then group each of the above sets of receivables by geography if it was relevant to do so.

On this basis, it might determine that a provision matrix is appropriate for only the trade receivables related to the month-to-month network access and that a different approach is needed for the trade receivables related to handset sales (which reflects a receivable over 24-months).

Furthermore, assume that two relevant geographical areas have been identified each with their own credit characteristics.

That would result in eight sub-groups with shared credit characteristics for the telecommunication company in this example.

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.

Continuing with the telecommunications company example from Step 1, let's consider network charges for retail customers in geography 1. How would this entity go about calculating a loss rate?

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 credit 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.

For example, assuming the telecommunications company used the data from its 2017 financial year, it determined the following:

  • Total credit sales recorded in 2017: $10,500,000
  • Total credit losses relating to those sales: $125,000

Once the total credit sales and credit losses are known, the relevant 'ageing' needs to be determined. An entity will need to analyse 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.

The analysis will require an accounting system to identify when a customer paid their credit sale invoice. This information is then sorted into the different timeframes as indicated in the table below.

Step 3.2 When was the cash received?

Step 3.2 When was the cash received? Sales that reach the ageing grouping Amount received during the ageing grouping Sales that reach the next ageing grouping
0 days overdue $10,500,00 $5,000,000 $5,500,000
Between 1 and 30 days past due $5,500,000 $2,750,000 $2,750,000
Between 31 and 60 days past due $2,750,000 $1,350,000 $1,400,000
Between 61 and 90 days past due $1,400,000 $750,000 $650,000
Later than 90 days past due $650,000 $525,000 $125,000
Never paid (written off) $125,000 - (written off)

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 total credit loss and dividing it by the credit sales amounts that reach each ageing grouping.

Step 3.3 Determine the historical loss rate

Step 3.3 Determine the historical loss rate 0 days past due 30 days past due 60 days past due 90 days past due More than 120 days past due
Balances outstanding $10,500,000 $5,500,000 $2,750,000 $1,400,000 $650,000
Total credit loss $125,000 $125,000 $125,000 $125,000 $125,000
Historical loss rate 1% 2% 5% 9% 19%

The logic for dividing the total credit loss by the outstanding balance at each age band can be explained by following the loss allowance as it moves through the different ageing bands. Applying the loss rates calculated above to the outstanding credit sales at any point in time results in a loss allowance of $125,000 being the lifetime expected loss on the total credit sales of $10,500,000.

The calculation performed above follows one year's credit sales through the different ageing bands to serve as an indicator of historical losses. At a reporting date, the trade receivable age analysis is a summary of how credit sales have progressed through the ageing bands. In other words, it is a snapshot at a moment in time. Consequently, the historical loss rates calculated above serve as a good starting point for the estimate of expected credit losses under IFRS 9.

The telecommunications company will have to repeat this exercise for each one of the sub-groups it identified in Step 1 for which it is appropriate to use a provision matrix to measure the expected credit losses.

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.

To illustrate the need to update the historical loss rate we refer back to the historical loss rates calculated in Step 3. The last time that there was a significant downturn in employment in the specific region trade receivable losses increased on average by 20%. This could be based on an analysis of historical loss patterns compared to points in time in the economic cycle.

It is worth noting that the increase of 20% may not necessarily be the same across all bands. For the purpose of this example we assume it is. Consequently, the historical loss rates would have to be increased by 20% to reflect the current economic forecast.

Updating historical loss rates for forward looking information Current 30 days past due 60 days past due 90 days past due Later than 90 days
Historical loss rate increased by 20% 1.2% 2.4% 6% 10.8% 22.8%

For illustrative purposes there is only one adjustment to the loss rate to reflect the higher risk of credit losses arising from higher unemployment. Multiple adjustments may be needed to reflect the unique characteristics of the credit risk environment at the reporting date compared to the average historical loss rates in Step 3.

Once the rate is determined in Step 3 and adjusted accordingly in Step 4 for forward looking macro-economic factors, the rate then will be used to measure the expected credit loss in a manner that is consistent with the groups for which the rates were determined.

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. For example, the specific adjusted loss rate should be applied to the balance of each age-band for the receivables in each group. Once the expected credit losses of each age-band for the receivables have been calculated, then simply add all the expected credit losses of each age-band for the total expected credit loss of the portfolio. If we assume a trade receivable balance outstanding at the reporting date of $1,652,000 and an age analysis as detailed below, the expected credit loss would be calculated at $55,416. The table below illustrates how the ultimate expected credit loss allowance would be calculated using the loss rates calculated in Step 4.

Determine the expected credit loss 0 days past due 30 days past due 60 days past due 90 days past due More than 120 days past due Total
Balances outstanding at reporting date $875,000 $460,000 $145,000 $117,000 $55,000
Expected credit loss rate 1.2% 2.4% 6% 10.8% 22.8%
Expected credit loss allowance $10,500 $11,040 $8,700 $12,636 $12,540 $55,416


The new impairment requirements will affect almost all entities and not just large financial institutions. Where entities have material trade receivable, contract asset and lease receivable balances care is needed to ensure that an appropriate process is put in place to calculate the expected credit losses.

Furthermore, the effort required to implement the enhanced disclosure requirements related to credit risk in IFRS 7 Financial Instruments: Disclosures should also not be underestimated. Entities should consider what level of disclosure will be required, especially in the first year of applying IFRS 9. It will be important for users of the financial statements to understand any increases in impairments, accounting policies applied and significant areas of judgement applied in adopting IFRS 9.

The content of this article is intended to provide a general guide to the subject matter. Specialist advice should be sought about your specific circumstances.