Measurement of Expected Credit Losses

Measurement of Expected Credit Losses does not have to be difficult, certainly in a normal business operation. For financial institutions (insurance and banks) such measurement will be more complicated, but in these industries big data technologies provide a very useful tool.

The basis for an Estimate of Expected Credit Losses is an estimate, that shall reflect: Measurement of Expected Credit Losses

  1. an unbiased and probability-weighted amount that is determined by evaluating a range of possible outcomes;
  2. the time value of money; and Measurement of Expected Credit Losses
  3. reasonable and supportable information that is available without undue cost or effort at the reporting date about past events, current conditions and forecasts of future economic conditions [IFRS 9 5.5.17].
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The use of an outcome based on a best or worst-case scenario is not permitted. IFRS 9 does not prescribe particular measurement methods. An entity would need to consider a broader range of information when assessing and measuring expected credit losses. The measurement should be based on the relevant information that is available without undue cost or effort, including information about: Measurement of Expected Credit Losses

  1. past events, such as the historical loss experience for similar financial instruments;
  2. current conditions; and Measurement of Expected Credit Losses
  3. reasonable and supportable forecasts that affect the expected collectability of future cash flows on the instrument.

For this requirement, an entity would need to consider both quantitative and qualitative factors that are specific to the borrower, including the entity’s current evaluation of the borrower’s creditworthiness, general economic conditions and an evaluation of both the current point in, and the forecast direction of, the economic cycle. Although the model is forward-looking, historical information is always considered to be an important anchor or base from which to measure expected credit losses. However, historical data should be adjusted on the basis of current observable data to reflect the effects of current conditions and forecasts of future conditions. Measurement of Expected Credit Losses

An estimate of expected credit losses would always reflect the probability that a credit loss might occur and, implicitly, that it might not occur. Thus, an entity is not permitted to estimate expected credit losses solely on the basis of the most likely outcome (i.e. use of the statistical mode is prohibited).

Something else -   Interest-free term loan No bank debt

Example 12-month expected credit loss measurement using an explicit probability of default occurring (PD) approach

Entity L originates a single loan for CU1,000,000. Using the most recent information available, such as holder-specific data, industry data, the credit quality of the borrower and the economic outlook for the next 12 months, Entity L estimates that the instrument has a 1% probability of a default occurring in the next 12 months.

It further estimates that 25% of the gross carrying amount will be lost if the loan defaults. Entity L recognises a loss allowance at an amount equal to 12-month expected credit losses using the 1% 12-month probability of default. Implicit in the calculation is the 99% probability that there is no default. The loss allowance for the 12-month expected credit losses is computed as follows: Measurement of Expected Credit Losses

= 1% x 25% x CU1,000,000 = CU2,500. Measurement of Expected Credit Losses

Example: 12-month expected credit loss measurement based on loss rate (LR) approach.

Application of credit loss approach [Application guide IFRS 9, B5.5.12]

Bank X segments its housing loan portfolio into borrower groups P and Q on the basis of common risk characteristics that are indicative of the borrower’s ability to pay all amounts that are contractually due. Groups P and Q make up CU200 million and CU300 million of the carrying amount respectively. The principal per client is CU200,000 for Group P and CU600,000 for Group Q.

Historically, for a sample of 50 loans in each group, Group P’s per annum average was four defaults in the first year, and Group Q’s per annum average was two defaults in the first year. Over the entire contractual term of those loans that defaulted in the first year after origination, the present value of the observed credit loss was CU750,000 for Group P and CU1,130,000 for Group Q [reference B5.5.53 Application to consistent groups]].

Something else -   IFRS 9 Financial Instruments Measurement

The historical loss rates for the first year are determined as follows:

Measurement of Expected Credit Losses

At the end of the current year Bank X expects an increase in defaults over the next 12 months compared to the historical rate. As a result, Bank X estimates five defaults in the next 12 months for 50 loans in Group P and three for 50 loans in Group Q. It estimates that the present value of observed credit loss per client will remain consistent with the historical loss per client. Bank X revises the historical loss rates as follows:

Measurement of Expected Credit Losses

Bank X uses the revised expected loss rates of 9.38% and 5.65% to estimate 12-month expected credit losses on other loans in Group P and Group Q respectively, which the Bank originated during the year.

Measurement of Expected Credit Losses

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