How to calculate bad debt provision under IFRS 9
If you have a large portfolio of trade receivables, then you face the same issue over and over again:
How to calculate bad debt provision to these receivables?
When I worked as an auditor, I used to discuss this issue with my colleagues very frequently.
Everyone of them agreed that yes, there is always some bad debt hidden among “healthy” receivables and it’s necessary to recognize some provision for that.
However, everyone had a different opinion on how to do it.
The most common approach was, to my surprise and disagreement, to create a provision in a few steps:
- Analyze receivables at the reporting date and sort them according to their aging structure
- Apply certain percentages of provision to the individual aging groups
Sounds easy, right?
In most cases, auditors applied something like 2% to trade receivables within maturity, 10% to trade receivables that were 1-30 days overdue… 100% to receivables more than 360 days overdue.
It always amazed me.
How the hell do you know that this particular company will suffer 10% credit loss on receivables that are 1-30 days overdue???
For me, it always seemed that these numbers were made out of thin air.
It was long time before IFRS 9 was adopted.
Now, luckily, IFRS 9 tells us how to create bad debt provision for trade receivables and how to get these percentages.
In this article, I’d like to explain this methodology and illustrate it on a simple example.
What do the rules in IFRS 9 say?
IFRS 9 requires you to recognize the impairment of financial assets in the amount of expected credit loss.
In fact, there are 2 approaches for doing so:
- General approach
In general approach, there are 3 stages of a financial asset and you should recognize the impairment loss depending on the stage of a financial asset in question.
Thus, the impairment loss is either in the amount of a 12-month expected credit loss (ECL) or a lifetime expected credit loss (ECL).You can read more about the general approach here.There are a lot of implementation troubles and challenges, for example:
- How do you determine in which stage the financial asset is?
- How do you determine when the credit risk in some financial asset has significantly increased?
- How do you calculate 12-month ECL and lifetime ECL?
- How do you get and update your inputs into the ECL calculations?
Therefore, IFRS 9 permits an alternative for some type of financial assets:
- Simplified approach
In simplified approach, you don’t have to determine the stage of a financial asset because the impairment loss is measured at lifetime ECL for all assets.This is great news because lots of troubles simply disappear.
However, let me warn you that the simplified approach is not for everybody and even if it’s simplified, you still need to make some calculations and effort.
Who can apply simplified approach?
OK, that’s not the best question in the world, because everybody can apply simplified approach.
Type of financial asset is more important here.
You have to apply simplified approach for:
- Trade receivables WITHOUT significant financing component, and
- Contract assets under IFRS 15 WITHOUT significant financing component
For these two types of assets you have no choice – just apply simplified approach.
On top of that, you can make a choice for:
- Trade receivables WITH significant financing component,
- Contract assets under IFRS 15 WITH significant financing component, and
- Lease receivables (IAS 17 or IFRS 16)
For these three types of financial assets, you can apply either simplified approach or general approach.
Can one entity apply both models?
Yes, of course – but not to the same type of financial asset.
Take a bank, for example.
Banks usually provide lots of loans and under IFRS 9, they have to apply general models to calculate impairment loss for loans.
But occasionally, banks can have other financial assets, too.
For example, they may rent redundant offices and have lease receivables.
Or, they can provide advisory services and charge fees for that – thus they can have typical trade receivables.
For these types of assets, the same bank can apply simplified approach.
How to apply simplified approach?
As written above, under simplified approach, you measure impairment loss as lifetime expected credit loss.
IFRS 9 permits using a few practical expedients and one of them is a provision matrix.
What is a provision matrix?
Simply said, it is a calculation of the impairment loss based on the default rate percentage applied to the group of financial assets.
Here, we have 2 important elements:
- Group of financial assets
- Default rates
Let’s break it down.
How to group the financial assets?
When you are using provision matrix for simplification, you still need to be as close to reality as possible.
Therefore, before applying any loss rates, you should group your financial assets first.
The reason is that all trade receivables do not necessarily share the same characteristics and therefore, it would not be reasonable to put them into the same pocket.
How to group them?
It depends on what factors affect the repayment of your receivables.
Maybe you noted that your retail customers (individuals) are less reliable and slower in payments than your business customers (companies).
Therefore, your segments or groups would naturally be retail customers and business customers.
Or, maybe you sell in a few geographical regions and you noted that customers from the capital city pay more reliably than customers in the rural areas (maybe it has something to do with unemployment rate…)
So, your segments would be customers from cities and customers from countryside.
I think you get the point – you should select the grouping of your trade receivables (or other financial assets in questions) depending on your circumstances.
Just a few suggestions for segmenting:
- By product type;
- By geographical region;
- By currency;
- By customer rating;
- By dealer type or sales channel;
The important point here is that the customers within one group should have the same or similar loss patterns.
How to get the default rates?
Remember – do NOT just trump the default rates up, just like auditors from the intro of this article.
You should really calculate them based on your own data.
IFRS 9 says that you should:
- Derive the default rates from your own historical credit loss experience; and
- Adjust them for forward-looking information.
Historical default rates
First, you need to analyze the historical credit losses.
You should take the appropriate period of time and analyze which portion of trade receivables created during that period went default.
Just be careful when selecting the appropriate period.
It should not be too short in order to make sense and it also should not be to long because market changes quickly and long period might incorporate market effects that are no longer valid.
I recommend selecting one or two years.
Then you are going to select the time buckets, or periods when the receivables were paid.
Finally, you would calculate the default rate for each bucket.
No worries if this seems too unclear – you can find the illustrative example below.
Once you have your historical default rates, you need to adjust them by the forward-looking information.
What is it?
They are all information that could affect the credit losses in the future, for example macroeconomic forecasts of unemployment, housing prices, etc.
You should adjust historical default rates for the information that is relevant for your financial assets.
For example, let’s say the telecom company has 2 segments of receivables:
- Retail customers, or individuals and for this group, unemployment rates are important factor affecting the payment rate.
If unemployment goes up, the credit quality of trade receivables to retail customers worsens.
- Business customers: for this group, GDP (gross domestic product) and inflation rate are important factors in this particular country.
How to incorporate the forward-looking information?
When there is a linear relationship between the macroeconomic factor (i.e. unemployment rate) and the input (i.e. increase/decrease in collection of receivables), then the incorporation is quite simple.
In this case, you need to observe what effect has the change in the parameter on your default rates and make simple adjustment (see illustration below).
However, when the relationship is not linear, then the adjustment might require some modeling using either Monte Carlo simulation or other similar methods.
Example: Impairment of trade receivables under IFRS 9
ABC wants to calculate the impairment loss of its trade receivables as of 31 December 20X1. ABC’s policy is to give 30 days for the repayment of receivables.
Note: This is an important point – 30 days credit period means that these receivables have NO significant financing component and therefore, you don’t have to worry about the present values.
The aging structure of trade receivables as of 31 December 20X1 is as follows:
|Days after issuing invoice||Amount outstanding|
|Within maturity (0-30 days)||800|
|> 360 days||100|
ABC decided to apply the simplified approach in line with IFRS 9 and calculate impairment loss as lifetime expected credit loss.
As a practical expedient, ABC decided to use the provision matrix.
First, ABC needs to calculate historical default rates.
In order to have sufficient historical data, ABC selected the period of 1 year from 1 January 20X0 to 31 December 20X0.
During this period, ABC generated sales of CU 20 000, all on credit.
Then, we can split the whole analysis process into a few steps.
Step 1: Analyze the collection of receivables by the time buckets
ABC needs to analyze when the receivables were paid and sort them out into table based on number of days from creation of invoice until the collection of the receivable:
|When paid?||Paid amount||Paid amount (cumulative)||Unpaid amount|
|Within maturity (0-30 days)||7 500||7 500||12 500|
|31-60 days||6 800||14 300||5 700|
|61-180 days||3 000||17 300||2 700|
|180-360 days||2 200||19 500||500|
|> 360 days||500 = write-off||19 500||500 = write-off|
- The amount of CU 500 in the column “Paid amount” for > 360 days represents in fact defaulted, unpaid amount.
- Paid amount cumulative is calculated as paid amount in certain time bucket plus paid amount in the previous bucket, i.e. cumulative paid amount in 31-60 days is calculated as 6 800+7 500. The exception is > 360 days – here, we can’t include CU 500 because it is not paid.
- Unpaid amount in the last column = total of 20 000 less cumulative paid amount.
Step 2: Calculate the historical loss rates
Then, ABC can calculate the historical default loss rates as the loss amount of CU 500 divided by the amount unpaid (outstanding) at the end of each time bucket:
|When paid?||Unpaid amount||Loss||Default rate|
|Within maturity (0-30 days)||20 000||500||2.5%|
|31-60 days||12 500||500||4.0%|
|61-180 days||5 700||500||8.8%|
|180-360 days||2 700||500||18.5%|
|> 360 days||500||500||100.0%|
Note: Default rate = loss divided by the unpaid amount.
Here you might note that data shifted a bit.
Unpaid amount for “within maturity” row amounting to CU 12 500 is now in the “31-60 days” row.
That’s OK because we are calculating amounts that fell into certain time bucket – that is, in the beginning of that bucket, not at its end.
So, in “within maturity” bucket, ABC created CU 20 000 of trade receivables; in “31-60 days” bucket, ABC created CU 12 500, etc.
Also, why did we apply the loss of CU 500 to all buckets?
The reason is that all receivables that were written off (CU 500) were in each stage over their life.
For example, all written off receivables amounting to CU 500 were current (within maturity), or within those CU 20 000 and therefore we can say that the loss generated during 20X0 (tested period) is 500/20 000.
The same applies for any other time bucket.
Now, we are not done yet.
We have only calculated the historical loss or default rates.
We still need to incorporate the forward-looking information.
Step 3: Incorporate forward-looking information
This is more difficult, but let me just outline one very simple approach.
Let’s say that ABC’s credit losses show almost linear relationship with unemployment rates.
Please note that “unemployment rate” is NOT a prescription for you – you should find your own macroeconomic factors that could affect your credit losses.
And, let’s say that the statistical office in ABC’s country assumes that unemployment rate will go up from 5% to 6% in 20X2.
ABC’s experience is that when unemployment rate increases by 1%, it triggers the increase in default losses by 10% (note – you should be able to prove that).
Therefore, ABC may reasonably assume that the loss of CU 500 can increase by 10% because of the increase in the unemployment rate – that is, to CU 550.
Thus, the calculation of loss (default) rates adjusted by forward-looking information is as follows:
|When paid?||Unpaid amount||Loss||Default rate|
|Within maturity (0-30 days)||20 000||550||2.75%|
|31-60 days||12 500||550||4.40%|
|61-180 days||5 700||550||9.60%|
|180-360 days||2 700||550||20.40%|
Step 4: Apply the loss rates to the current trade receivables portfolio
And finally, coming to the end of this exercise, let’s apply these loss rates to actual portfolio of trade receivables as of 31 December 20X1:
|Days after issuing invoice||Amounts outstanding||Loss rate||Expected credit loss|
|Within maturity (0-30 days)||800||2.75%||22.0|
|> 360 days||100||100.00%||100|
ABC can recognize the impairment loss on trade receivables as
- Debit P/L Impairment loss on trade receivables: CU 199
- Credit Trade receivables – adjustment account: CU 199
Any questions? Please let me know in the comments below this article. Thank you!
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