If an entity has a small number of debtors, expected credit losses can be estimated by considering the historical trends and current financial conditions of each debtor. However, in most of the cases, companies have a large portfolio of debtors and specific provisioning for each party is not practically possible. In such cases, estimating the allowance for bad or doubtful debts becomes a challenging task.
A common approach conventionally used to calculate the allowance for bad debts involved preparing a receivables ageing report and applying certain percentages to the receivable balances falling under different age brackets.
Criticism on conventional approach to calculate allowance for bad debts
This approach is easy but not the right one! First of all, age is not the only characteristic to consider while grouping the receivables. For instance, if individual customers and corporate customers have different credit terms and different approach of settling their due amounts, they should be evaluated separately. Similarly, receivables should be grouped according to similar credit loss patterns and then evaluated further. Secondly, the percentages applied are subjective and have no solid basis.
This practice was followed because there was no specific guidance in the International Financial Reporting Standards. However, “IFRS 9 – Financial Instruments” has addressed this matter and has included comprehensive guidance on how to calculate the allowance for bad debts.
Impairment of financial assets under IFRS 9
IFRS 9 requires entities to recognize impairment of financial assets based on the expected credit losses. For doing so, it has introduced the following two approaches.
1. General Approach
It is a three-stage impairment model. At the time of initial recognition of a financial asset, an entity is required to calculate and recognize 12 months expected credit loss. Subsequently at each reporting date, the entity will evaluate the changes in the financial asset’s credit risk. Based on this evaluation, financial asset is placed in one of the three stages of the impairment model. These stages determine the amount of impairment to be recognized. Further details about the general approach are given in our chapter “Impairment of financial assets – ECL approach”.
General approach is relatively complex in terms of determining the relevant stages of the financial assets and evaluating the changes in credit risks. Therefore, IFRS 9 has introduced a relatively easier approach as well.
2. Simplified Approach
In simplified approach, life-time expected credit losses are determined for all the financial assets.
Simplified approach really makes the life simple as entities are not required to evaluate whether credit risk has significantly increased or not. Classification of financial assets into different stages of impairment is also not required.
Let’s see how simplified approach is applied to calculate allowance for bad debts.
Calculation of allowance for bad debts
As a practical expedient, simplified approach allows the use of provision matrix to calculate the allowance for bad debts.
What is a provision matrix? A provision matrix is tabular or other form of organized presentation of receivables for calculation of bad debts allowance.
- ageing of receivables can be used as a provision matrix.
- Age brackets in the ageing are the categories of the provision matrix.
- If an entity has two types of customers, say individual and corporate. Both have different historical trends of credit losses, so the entity has classified its receivables into two groups and separate provision matrices will be used for individual and corporate customers’ receivable balances.
How is the provision matrix used to calculate the allowance for bad debts? Based on the historical credit loss experience and forward-looking estimates, specific provision percentage or default rate is assigned for each category in the provision matrix. For instance, ageing of receivables can be used as a provision matrix and each age bracket is assigned a default rate. Provision or allowance for bad debts is calculated by applying the relevant default rates to respective categories of receivables in the provision matrix.
Depending on the diversity of customer-base, an entity should use appropriate groupings showing similar credit loss patterns. Then the provision matrix should be used for each group to calculate the overall bad debts allowance.
Don’t worry yet! We’ll further elaborate the calculation of allowance for bad debts 😊. In addition, an example is shared in the next chapter “Example – How to calculate allowance for bad debts”, which will clarify this topic.
From the above discussion, four main steps are needed for the calculation of bad debts allowance.
- Grouping of receivables based on similar credit loss patterns.
- Determination of categories in the provision matrix (For e.g., age brackets).
- Determination of default rates.
- Application of default rates to respective categories in the provision matrix to calculate the allowance for bad debts.
Grouping of receivables
Depending on the diversity of customer-base of an entity, receivables can be of different types having different repayment trends. For instance, if an entity operates in different regions, historical trends may suggest difference in the mentalities and default tendencies of customers in different regions. In such a case, the entity should group its receivables according to various regions of operations. Similarly, individual and corporate customers usually have different tendencies and histories of defaults.
In short, the first step of calculation of bad debts allowance is the grouping of receivables in such a way that customers in one group have similar credit loss patterns.
Some examples of basis of grouping are mentioned below:
- Secured and unsecured receivables
- Product type
- Geographical region
- Customer ratings
Determination of categories in the provision matrix
For each group of receivables, different categories of provision matrix are determined. For instance, what age brackets should be used in the ageing report if it is to be used as a provision matrix. There is no hard and fast rule. For instance, If the average credit period is 60 days, age brackets with 60 days intervals can be used.
Determination of default rates
The trickiest part is to determine the default rates. Calculation of default rates is based on the historical credit loss experience of an entity. These default rates are adjusted for current conditions and forward-looking information that is available at the reporting date without undue cost or effort.
Historical credit loss experience
An entity should take a reasonable period of time before the reporting date and analyze the behavior of its receivables. Default history of receivables in that period is extracted and default rates are determined for each age bracket or category of provision matrix accordingly. Example in the next chapter will clarify the determination of historical default rates.
Time period for analyzing the historical credit loss experience should be reasonable. However, it should not go way back as market trends change with time and using old historical data may not be a good option to predict the changed market environment and expected credit losses.
For instance, 1 year before the reporting year is a reasonable time for an entity whose receivables have an average credit period of 30 days. If the average credit period of an entity is greater, say 60 or 90 days, period to evaluate the historical credit losses may be increased to 2 or 3 years before the reporting period.
Adjust for forward-looking information
After determining the historical default rates for each category of the provision matrix, forward looking information that is available without undue cost or effort is incorporated.
What is forward-looking information? It includes information specific to debtors as well as macro-economic factors relevant to the general economic conditions. However, an entity is not required to do extensive research on this. Any reasonable forward-looking information that is available at the reporting date will be used. For instance, unemployment rates and inflation may be relevant as increase in these factors may result in increase in expected credit losses. Similarly, Gross domestic product (GDP) is also an example of macro-economic factor that can be used to adjust the historical default rates.
Adjusting the historical default rates to incorporate the forward-looking information can be tricky. In some cases, historical trends of changes in macro-economic factors and changes in credit loss patterns can be observed to find out their relation. For instance, increase in inflation by 1% resulted in 5% increase in credit losses. Such relation is called linear relation and can be incorporated in the historical default rates easily.
However, relationship of some macro-economic factors and expected credit losses is not readily observable and needs to be determined by modelling techniques such as Monte-Carlo Simulation.
Calculate allowance for bad debts
Last step is to calculate the bad debts allowance by applying the relevant default rates determined in previous step to each category of the provision matrix.
For better understanding of this topic, an example of how to calculate allowance for bad debts is shared in our next post.