International GAAP® 2019: Generally Accepted Accounting Practice under International Financial Reporting Standards
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that guidance.
6.5.1
Example of individual assessment of changes in credit risk
First, as a benchmark, Scenario 1 (an individual assessment) illustrates a situation where
a bank has sufficient information at individual exposure level to identify a significant
deterioration of credit quality.
Financial instruments: Impairment 3811
Example 47.15: Individual assessment in relation to responsiveness to changes in
credit risk
The bank assesses each of its mortgage loans on a monthly basis by means of an automated behavioural
scoring process based on current and historical past due statuses, levels of customer indebtedness, loan-to-
value (LTV) measures, customer behaviour on other financial instruments with the bank, the loan size and
the time since the origination of the loan. It is said that historical data indicates a strong correlation between
the value of residential property and the default rates for mortgages.
The bank updates the LTV measures on a regular basis through an automated process that re-estimates
property values using recent sales in each post code area and reasonable and supportable forward-looking
information that is available without undue cost or effort. Therefore, an increased risk of a default occurring
due to an expected decline in residential property value adjusts the behavioural scores and the Bank is
therefore able to identify significant increases in credit risk on individual customers before a mortgage
becomes past due if there has been a deterioration in the behavioural score.
The example concludes that if the bank is unable to update behavioural scores to reflect the expected declines
in property prices, it would use reasonable and supportable information that is available without undue cost
or effort to undertake a collective assessment to determine the loans on which there has been a significant
increase in credit risk since initial recognition and recognize lifetime ECLs for those loans.
It should be noted that, in this example, the main source of forward-looking information
is expected future property prices. No account would appear to be taken of other
economic data such as future levels of employment or interest rates. We assume that
the Board took this approach to make the example simple, but it implies that future
property prices are considered to provide a sufficiently good guide to future defaults
that it is not necessary to take account of other data as well.
6.5.2
Basis of aggregation for collective assessment
Next, the standard sets out how financial instruments may be grouped together in order
to determine whether there has been a significant increase in credit risk. Any
instruments assessed collectively must possess shared credit risk characteristics. It is not
permitted to aggregate exposures that have different risks and, in so doing, obscure
significant increases in risk that may arise on a sub-set of the portfolio. Examples of
shared credit risk characteristics given in the standard include, but are not limited to:
[IFRS 9.B5.5.5]
• instrument type;
• credit risk ratings;
• collateral type;
• date of initial recognition;
• remaining term to maturity;
• industry;
• geographical location of the borrower; and
• the value of collateral relative to the asset (the loan-to-value or LTV ratio), if this
would have an impact on the risk of a default occurring.
The standard also states that the basis of aggregation of financial instruments to assess
whether there have been changes in credit risk on a collective basis may have to change
over time, as new information on groups of, or individual, financial instruments becomes
available. [IFRS 9.B5.5.6].
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We make the following observations:
• As has been stressed earlier, the assessment of significant deterioration is intended
to reflect the risk of default, not the risk of loss, hence collateral should normally be
ignored for the assessment. The standard nonetheless explains that the value of
collateral relative to the financial asset would be relevant to the collective
assessment if it has an impact on the risk of a default occurring. It cites, as an
example, non-recourse loans in certain jurisdictions. The question of when such an
arrangement would always meet the IFRS 9 classification and measurement
characteristics of the asset test is beyond the scope of this chapter. LTV or a house
price index may be a useful indicator of significant collective deterioration in a wider
range of circumstances than just where the loans are non-recourse. First, house
prices are themselves a useful barometer of the economy and so higher LTVs and
lower indices correlate with declining economic conditions. Second, loans that were
originally advanced at higher LTVs may reflect more aggressive lending practices,
with the consequence that such loans may exhibit a higher PD if economic
conditions decline. Third, a borrower in trouble with a lower LTV will likely sell his
house to redeem the mortgage rather than defaulting on the mortgage (and,
conversely, a borrower with a high LTV will have less incentive not to default).
• By date of original recognition, we assume that the Board did not intend that loans
should be assessed in separate groups for each year of origination, but that vintages
may be aggregated into groups that share similar credit risk characteristics. Loan
products and lending practices, including the extent of due diligence, and key ratios,
such as the LTV and loan to income, change over time, often reflecting the economic
conditions at the time of origination. The consequence is that loans from particular
years are inherently more risky than others. For some banks, this might mean
isolating those loans advanced just prior to the financial crisis from those originated
earlier or in the subsequent, more careful lending environment. Also, there is a
phenomenon termed seasoning, which describes how loans that been serviced
adequately for a number of years, over a business cycle, are statistically less likely to
default in future, suggesting that older loans should be assessed separately.
• Although the examples in the standard refer to regions, as the geographical location
of borrowers, the groupings could be much larger, such as by country, or much
smaller, if there are particular issues associated with particular towns. Hence the
choice of geographical groupings will depend very much on the environment in
which a bank operates.
• Other ways that loans might be grouped according to shared credit risk
characteristics could include by credit score, by payment history, whether
previously restructured or subject to forbearance but subsequently restored to a
12-month ECL allowance, and manner of employment (as featured in Illustrative
Example 5 in the Implementation Guidance for the standard under the bottom up
assessment discussed in Example 47.16 below).
• The requirement that financial instruments that are assessed together must share
similar credit risk characteristics means that a bank may have a substantial number
of portfolios. Even a relatively small bank might have six different products (taking
into account terms to maturity and types of collateral), three regions and three
Financial instruments: Impairment 3813
different vintage groups which, multiplied out, would give fifty four different
assessment groups. A larger global bank might need to monitor many more
different portfolios. However, a balance will need to be struck between ensuring
that portfolios are small enough to have sufficient homogeneity and yet not so
small that there is too little historical data for losses to be reliably estimated.
• Also, the requirement that groupings may have to be amended over time means
that there must be processes put in place to reassess whether loans continue to
share similar credit risk characteristics. Yet, in practice, there will need to be a
sufficient level of stability in the construction of portfolios to allow enough
historical data to be gathered for reliable estimation of losses.
Finally, paragraph B5.5.6 in IFRS 9 adds that, ‘if an entity is not able to group financial
instruments for which the credit risk is considered to have increased significantly since
initial recognition based on shared credit risk characteristics, the entity should recognise
lifetime ECLs on a portion of the financial assets for which credit risk is deemed to have
increased significantly’.
As clarified by the IASB in its webcast on forward-looking information in July 2106, it is
possible that a bank is aware of differences in sensitivities of credit risk to a change in a
particular parameter but is unable to group the assets on the basis of such sensitivity. In
such instances, the bank may determine that the expected forward-looking scenario
would result in significant increases in credit risk for a certain proportion of its portfolio.
6.5.3
Example of collective assessment (‘bottom up’ and ‘top down’ approach)
The main standard does not amplify how a collective assessment would be made but
Illustrative Example 5 in the Implementation Guidance of IFRS 9 provides two
scenarios that explore the approach. [IFRS 9 IG Example 5 IE29-IE39].
Example 47.16: Collective assessment in relation to responsiveness to changes in
credit risk (‘bottom up’ approach)
Region Two of Illustrative Example 5 in the Implementation Guidance for the standard introduces the so-
called bottom up method. It deals with a mining community within a region that faces unemployment risk
due to a decline in coal exports and, consequently, anticipated future mine closures. Although most of the
loans are not yet 30 days past due and, further, the borrowers are not yet unemployed, the bank re-segments
its mortgage portfolio so as to separate loans to customers employed in the mining industry (based on
information in the original mortgage application form).
For these loans (plus any others that are more than 30 days past due), Bank ABC recognises lifetime ECLs,
while it continues to recognise 12-month ECLs for the other mortgage loans in the region. Any new loans to
borrowers who rely on the coal industry would also attract only a 12-month allowance, until they also
demonstrate a significant increase in credit risk.
The bottom up method is described as an example of how to assess credit deterioration
by using information that is more forward-looking than past due status. But this example
also illustrates that collectively assessed groups may need to change over time, to ensure
that they share similar credit risk characteristics. Once the coal mining industry begins
to decline, those loans connected with it would no longer share the same risk
characteristics as other loans to borrowers in the region, and so would need to be
assessed separately. We also note that this example assumes that macroeconomic
factors can be linked to the ECLs of a very specific portfolio. Further, in practice, most
banks may not have the data to achieve this level of segmentation.
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As already described above (possible criteria for grouping of financial assets with similar
credit risk characteristics), the bottom up approach could be applied to sub-portfolios
differentiated by type of instrument, risk rating, type of collateral, date of initial
recognition, remaining term to maturity, industry, geographical location of the
borrower, or the LTV ratio. A good example of this approach might be for exposures to
borrowers that are expected to suffer major economic difficulties due to war or political
upheaval, or borrowers with the weakest credit scores, who are expected to be more
sensitive to a change in a relevant macroeconomic factor. In addition, as underwriting
standards may vary or change, the portfolio might be sub-divided so as to reflect this.
Note that the coal mines closures are, as yet, only anticipated, hence this example helps
show how the standard is intended to look much further forward than the consequent
unemployment that would probably trigger an IAS 39 impairment provision. The need
to look forward is also illustrated in the next example.
Example 47.17: Collective assessment in relation to responsiveness to changes in
credit risk (‘top down’ approach)
For Region Three of Illustrative Example 5 in the Implementation Guidance for the standard, Bank ABC
anticipates an increase in defaults following an expected rise in interest rates. Historically, an increase in
interest rates has been a lead indicator of future defaults on floating-rate mortgages in the region. The bank
regards the portfolio of variable rate mortgage loans in that region to be homogenous and it is incapable of
identifying particular sub portfolios on the basis of shared credit risk characteristics. Hence, it uses what is
described as a top down method.
Based on historical data, the bank estimates that a 200 basis points rise in interest rates will cause a significant
increase in credit risk on 20 per cent of the mortgages. As a result, presumably because the bank expects a
200 basis points rise in rates, it recognises lifetime ECLs on 20 per cent of the portfolio (along with those
loans that are more than 30 days past due) and 12-month ECLs on the remainder of mortgages in the region.
The challenge posed by the top down method is how to calculate the percentage of
loans that have significantly deteriorated. That a rise in interest rates will likely lead to
a significant deterioration in credit risk for some floating-rate borrowers, is not
controversial. But working out whether they make up 5 per cent, 20 per cent or 35 per
cent of the portfolio would appear to be more of an art than science, and no two banks
are likely to arrive at the same figure.
The IASB brought some useful clarification on this example in its July 2016 webcast on
forward-looking information:
• First, they clarified that one financial instrument cannot exist in stage 1 and in stage 2
at the same time. Therefore, the Board in the above example did not mean that each
asset in the portfolio is to be regarded as 20% in stage 2 and 80% in stage 1. Instead,
20% of the assets are in stage 2, even if the bank does not yet know which.
• This allocation is intended to reflect that some assets in the portfolio will respond
more adversely to a given change to the macroeconomic factor (e.g.
unemployment rate) than others. Therefore, some assets in the portfolio may be
Judgement is required to determine how much of the portfolio should move to
stage 2. An entity may, for example, determine that given the range of possible
scenarios, 20% of the portfolio moves to stage 2 considering the different level of
sensitivity of the assets in the portfolio to the different relevant credit risk drivers.
Financial instruments: Impairment 3815
• As further explained in the next section on using multiple scenarios for the staging
assessment (see 6.7 below), it is important to note that the 20% is not the probability
of occurrence of the more adverse scenario. Rather, it reflects the proportion of
the portfolio deemed to have already significantly deteriorated based on the most
recent probability-weighted average PD. This is due to the heightened sensitivity
of this proportion of the portfolio to certain macroeconomic factors.
A further issue with the top down approach is the question of what the lender should do
if it subsequently finds that differences in risk characteristics emerge within the portfolio,
such that certain assets need to be measured using lifetime ECLs using the bottom up
approach. A similar question arises if individual assets subsequently need to be measured
using lifetime ECLs, for instance, because they become 30 days past due. In practice, it is
likely that banks, at each reporting date, will first allocate exposures to stage 2 based on
an individual assessment and then apply a collective approach to the remaining stage 1
exposures. They are unlikely to ‘roll-forward’ the collective allowance.
Presumably the proportion of the portfolio ECLs in stage 2 can be measured once again
using 12-month ECLs if economic conditions are expected to improve. However, any
assets that are 30 days past due will continue to be treated as stage 2. [IFRS 9.B5.5.19].
Because of these and similar difficulties, we are not currently aware of any banks who intend
to use the top down approach in the manner set out in the Illustrative Example. Banks prefer
to know which loans are measured using lifetime ECLs, rather than a notional percentage
of the population. In practice, the methods that are being explored by banks are closer to a