Provision matrix in the simplified approach

The provision matrix in the simplified approach is used for financial assets of little complexity and in a non-complicate operation environment, where it is common sense to use.

Forward looking

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.

Provision matrix in the simplified approach

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. Examples of criteria that might be used to group assets include geographical region, product type, customer credit rating, collateral or trade credit insurance and type of customer (such as wholesale or retail). [IFRS 9 B5.5.35]

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.

Trade receivables

0 days past due

30 days past due

60 days past due

90 days past due

More than 120 days past due

Loss rate

1%

2%

3%

20%

100%

Notes

Note past due is past what is contractually or implicitly agreed between the selling and purchasing entity.

Note: the loss rate needs to be properly documented based on historical data taking into account the changes (if any) in expected future losses

Stepped approach

It is fairly simple to state that loss rates need to be applied to a provision matrix. However, how are loss rates determined? To address this question, we provide a stepped approach for applying a provision matrix below. There are a number of ways in which an entity can go about building a provision matrix as IFRS 9 does not provide any specific guidance.

So here we go……

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). [IFRS 9 B5.5.35]

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.

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.

Provision matrix in the simplified approach

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. [IFRS 9 B5.5.53]

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 B5.5.53]

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. [IFRS 9 B5.5.53] However, for simplicity the example used reflects information obtained for one financial year.

For example, assuming the telecommunications company used the data from its 20×2 financial year, it determined the following:

  • Total credit sales recorded in 20×2: $10,500,000
  • Total credit losses relating to those sales: $125,000

Once the total credit sales and credit losses are known, the relevant ‘aging’ 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?

Provision matrix in the simplified approach

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 aging grouping. [IFRS 9 B5.5.53]

Step 3.3 Determine the historical loss rate

Provision matrix in the simplified approach

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 aging 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. This is demonstrated below.

$10,500,000 of credit sales made with credit losses of

$125,000

Total historical credit losses are 1% of total credit sales made. In other words, before overdue status is considered, historical losses indicate an expected loss of 1%

30 days later, the balance outstanding is $5,500,000

While the outstanding balance has reduced, we know the total loss that will result in that balance is $125,000. Consequently, at 30 days past due the historical loss rate is 2%

30 days later, the balance outstanding is $2,750,000

While the outstanding balance has reduced, the total loss that will result in that balance is $125,000. Consequently, at 31-60 days past due the historical loss rate is 5%

And on its goes ………………

The calculation performed above follows one year’s credit sales through the different aging 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 aging 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 apply to the carrying amount. Using the example we have used throughout, the historical loss rates were calculated from the 20×2 financial year. However, what if at the 20×3 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. [IFRS 9 B5.5.52]

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.

Trade receivables

0 days past due

30 days past due

60 days past due

90 days past due

More than 120 days past due1

Loss rate increased by 20%

1.2%

2.4%

3.6%

24.0%

100%

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.

Trade receivables

0 days past due

30 days past due

60 days past due

90 days past due

More than 120 days past due

Expected credit loss rate

1.2%

2.4%

3.6%

24.0%

100%

Balances outstanding at measurement date

$875,000

$460,000

$145,000

$117,000

$55,000

Expected credit loss allowance

$10,500

$11,040

$8,700

$12,636

$55,000

TOTAL LOSS ALLOWANCE

$97,876

See also: The IFRS Foundation

Provision matrix in the simplified approach

Leave a Reply

Your email address will not be published. Required fields are marked *