Every entity with large portfolio of trade receivables always faces an issue on how to calculate bad debts provision on these receivables. The situation gets little more complex with adoption of Ind AS.
All of us agree that some bad debts are always hidden in the ‘healthy’ receivables and its imperative that the same is recognized to give a true and fair value of trade receivables.
In the pre Ind AS era, the most common approach used was:
1. Analyse the receivables basis the ageing
2. Apply certain percentage of provision to each ageing group of receivables based on management estimates
That is, the management may apply say 2% to all receivables from 30 to 60 days, 10% to all receivables from 61 to 180 days and 100% to all receivables with an ageing of more than 180 days. This was based mainly on the management estimates rather than any established and tested methodology.
In the post Ind AS era, Ind AS 109 elaborates on how to calculate bad debts provision for trade receivables and how to arrive at the default percentages for each age group of trade receivables.
What does Ind AS 109 say?
As per Ind AS 109, impairment losses of financial assets should be recognised in the amount of Expected Credit Loss (ECL). It advocates two approaches of doing so:
1. General approach:
In general approach, the financial asset is divided into 3 stages and the amount of ECL is recognized depending on the stage of the financial asset into consideration.
The losses under this approach is either based on the 12 months ECL or lifetime ECL. All financial assets falling in stage one is performing and requires 12 months ECL, whereas financial assets in stage 2 where the credit risk has increased significantly post recognition or financial assets in stage 3 which are credit impaired a lifetime ECL is required.
The general approach is complicated in terms of various challenges like:
2. Simplified approach
To avoid all the above complexities, Ind AS 109 permits an alternative ECL approach for certain type of financial assets called the Simplified approach. The simplified approach does not require any staging of financial asset as the impairment loss is lifetime ECL as against 12 months ECL. This helps overcome lots of complexities involved in the general approach.
Type of financial assets for which simplified model of ECL can be applied:
For the above-mentioned financial assets, an entity has no choice but to apply simplified approach.
Further, there is a choice given to apply simplified approach to:
Can an entity apply both the models simultaneously?
The answer is ‘YES’ but not to the same type of financial assets.
For example, take an entity which is into lending business. This entity advances huge loans and is required to follow general approach for ECL provisioning on these loans. However, such an entity may have other financial assets in the form of trade receivables for certain advisory or other services related activities. For such trade receivables a simplified approach can be followed.
Let us now analyse the simplified approach of ECL provisioning:
As already stated, simplified approach measure impairment loss as lifetime ECL. For this approach, Ind AS 109, permits use of practical expedients like provision matrix.
What is Provision Matrix?
In simple words, provision matrix is nothing, but impairment loss calculated based on default rate percentage applied to a particular group of financial assets.
The two important elements of provision matrix are:
a) Group of financial assets
b) Default rates
Let us understand each of the above:
a) Group of financial assets
Simply means that segment your financial assets. Even when an entity applies simplified approach for ECL provision, it needs to be as close to reality as possible.
Grouping is required as all the trade receivables do not necessarily share the same characteristics and therefore, it would not be reasonable to put them into the same bucket.
Grouping purely depends on the factors that effects the repayment of receivables. An entity may have noted that its retails customers (individuals) are less reliable and slower in payments as compared to its business customers (companies). This becomes the basis for the groupings.
Furthermore, an entity has observed that customers from urban areas are more reliable and pays faster as compared to customers in rural areas. Thus, in this case the geographical region becomes the basis of groupings.
Thus, grouping can be on the basis of:
The underlying is that the customers within one group should have the same or remarkably same loss patterns.
b) How to get the default rates?
This number is derived from the entities own past data. Ind AS 109 states that:
Historical Default rates:
The first step is to analyse the entity’s own historical credit losses. The period selected for this analysis can neither be too short so as to make no sense nor too long because market changes are frequent and longer period may result in considering market assumptions that may no longer be valid.
Once the analysis period is fixed, put all your receivables into different ageing buckets. Finally, calculate default rate for each bucket.
Forward looking information:
Once the default rate is applied, next step is to adjust them with the forward-looking information. These are all the information that could affect the credit losses in future, for example, macroeconomic forecasts of unemployment, sectoral inflation, etc.
Adjust the historical default rates for the information that is relevant for the entity’s receivables. For example, in case you have 2 segments of receivables:
1. Retail customer or individual: for this group, the unemployment rates are important factor affecting the payment rate. If unemployment goes up, the credit quality of this group of trade receivable worsens.
2. Business customer: for this group, the inflation may become a relevant factor for any adjustment.
When the relationship between macro-economic factors and receivables is linear and direct then calculation becomes simple. However, whenever the relationship is not linear, then the adjustment may require some modelling like a Monte Carlo Simulation or any other similar methods.
Example of ECL on trade receivables as per Ind AS 109:
XYZ Ltd wants to calculate ECL on its trade receivables as at 31 March 20X2. The policy of the company is to give a credit period of 30 days to all its customers.
A very important point to note here is that the credit period offered by the company is only 30 days and hence no significant financial component is involved.
The ageing structure of trade receivables as at 31 March 20X2 is as follows:
|Ageing from invoice date||Amount outstanding (in lakhs)|
|Within due date (0 – 30 days)||1000|
|31 – 60 days||500|
|61 – 180 days||380|
|181 – 365 days||200|
|Above 365 days||120|
XYZ Ltd. decided to apply the simplified approach in line with Ind AS 109 and calculate lifetime ECL. As a practical expedient, XYZ Ltd. uses the provision matrix.
First and foremost, the company needs to calculate historical default rates. In order to have enough historical data, XYZ Ltd. decides to use the period of 1 year i.e. from 1 April 20X0 to 31 March 20X1.
Let us assume that during the period selected, XYZ Ltd. made a sale of INR 20,000 (in lakhs) and all the sales are on credit.
With this example let us understand the steps discussed above.
Step 1: Analyse the trade receivables basis the ageing (all amounts in lakhs)
|Age of trade receivables when paid||Paid amount||Paid amount (cumulative)||Unpaid amount|
|Within due date (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|
|181 – 365 days||2,200||19,500||500|
|Above 365 days||500 which represent amount written off||19,500||500 – written off|
Step 2: Calculate the historical loss rates
The next step is to calculate the historical default loss rate. This is calculated by dividing the loss amount of INR 500 by the amount unpaid (outstanding amount) in each time bucket.
|Age of trade receivables||Unpaid amount (A)||Loss (B)||Default rate % (B/A)|
|Within due date (0 – 30 days)||20,000||500||2.5%|
|31 – 60 days||12,500||500||4%|
|61 – 180 days||5,700||500||8.8.%|
|181 – 365 days||2,700||500||18.5%|
|Above 365 days||500||500||100%|
It is pertinent to note that data has shifted in the above table i.e. amount of INR 12,500 has shifted from within due date bucket to the next bucket of 31 – 60 days. This is because we are calculating amount unpaid in each bucket at the beginning of that bucket and not at its end. So, at the beginning or on day 1 the amount unpaid is INR 20,000 and at the beginning of next bucket i.e. 31 – 60 days the amount unpaid in INR 12,500 (20,000 less 7,500 paid within due date) and so on and so forth.
Also, it is important to note that the loss of INR 500 is applied to each bucket and not only in the above 365 days bucket. This is because this receivable has passed through all the stages over its life.
Step 3: Incorporate forward looking information
This can be the most difficult one to incorporate. But let us try to understand this with a simple approach. Let us for simplicity assume that XYZ Ltd. trade receivables has almost linear relationship with the rate of unemployment. And let us say that historically every 1% increase in unemployment rate increases the default rate by 10% (this needs to be proved with proper supporting and evidences of historical data by XYZ Ltd.). Also based on the statistical information available the unemployment rate is likely to increase from 3% to 4% in the next year.
Based on its historical experience XYZ Ltd. knows that because of increase of 1% in the unemployment rate, the default rate will increase by 10% i.e. default of INR 500 will further increase by INR 50 and the total default is likely to be INR 550.
Thus, the calculation of default rates after incorporating forward looking information will be as below:
|Age of trade receivables||Unpaid amount (A)||Loss (B)||Default rate % (B/A)|
|Within due date (0 – 30 days)||20,000||550||2.75%|
|31 – 60 days||12,500||550||4.4%|
|61 – 180 days||5,700||550||9.60%|
|181 – 365 days||2,700||550||20.40%|
Step 4: Apply the default rate arrived in step 3 to the current trade receivables to arrive at the ECL amount
|Ageing from invoice date||Amount outstanding (in lakhs)||Default rate||Amount of ECL|
|Within due date (0 – 30 days)||1000||2.75%||27.5|
|31 – 60 days||500||4.4%||22|
|61 – 180 days||380||9.60%||36.48|
|181 – 365 days||200||20.40%||40.8|
|Above 365 days||120||100%||120|
XYZ Ltd. needs to recognise the ECL of INR 246.78 for 31 March 20X2 by passing the following entry:
Impairment loss on trade receivables – Debit INR 246.78
Provision for Impairment on trade receivables – Credit INR 246.78
The last word
Ind AS has combined the past historical trend with forward looking assumptions to make the ECL on trade receivables more scientific and reliable for users of the financial statements. Though it does not completely eliminate the management estimates involved in bad debts provisioning, it reduces subjectivity to a great extent in terms of documented provision matrix. A word of caution here is that the provisioning matrix is not a one time exercise to be performed but needs evaluation at periodic interval to ensure sufficient provision for ECL is created.