IFRS 9 uses expected credit loss (ECL) model that requires entities to recognise a loss on financial instrument based on the expected credit losses over the life of the instrument, rather than waiting for a trigger event.
Under the ECL model, entities are required to estimate the probability of default, the amount that would be lost in the event of default, and the expected timing of the default. This requires the use of historical data, economic forecasts, and other relevant information to estimate the ECLs.
The three-stage model in IFRS 9 is intended to provide a more accurate and timely recognition of impairment losses on financial instruments. By requiring entities to estimate ECL over the entire expected life of the financial instrument, the standard promotes a more forward-looking approach to impairment recognition, which is intended to result in earlier recognition of credit losses. For an example, all loans are classified as Stage 1 at the origination of loan which requires the recognition of an ECL based on probability of default over the next 12 months, even if the loans are very highly to be fully collectible.
Overall, the ECL model in IFRS 9 is considered to be a more principles-based and forward-looking approach to impairment recognition, as it allows for greater judgment and flexibility in estimating credit losses. This model also requires more extensive disclosures regarding the assumptions and methodologies used in estimating ECLs, which promotes greater transparency and comparability in financial reporting.
Rolling-out an IFRS 9 program is an enterprise-wide initiative that cuts across risk, finance, IT and lines of businesses. The initiative will have a number of important individual components that need to be managed and delivered through specific work streams. Some of the key work streams include:
- Standards & policy definitions. This includes determining financial instruments in scope, review credit policy, classification and cross functional governance.
- ECL modelling which includes computation of probability of default (PD) – 12-months and lifetime PD, loss given default (LGD) and exposure at default (EAD). Overlaying the ECLs model with macroeconomic factors, scenario analyses and forecasting and finally the ECL recognition engine.
- Accounting integration & reporting
- Data management across risk, credit control and finance data
- Integration with relevant IT systems
As actuaries, our expertise and our contribution in this IFRS 9 initiative is in relation to IFRS 9 ECL modelling and recognition engine
Subject Matter Expert
Case Study: MARA – Expected Credit Loss Modelling
We worked with Majlis Amanah Rakyat to establish a credit model to compute the expected credit loss of all its educational, entrepreneur and staff loans, according to the methods as prescribed under the MFRS 9 Financial Instruments. Using MARA internal database and our knowledge in predictive modeling, we developed a analytic workflows that include both machine learning and GLM to assist MARA to identify the underlying risk factors (e.g. loan type, loan amount, age of borrowers, geographical location etc) and their correlation with the probability of default for each loan. The model also takes into account the development of macroeconomic conditions which enables MARA to adopt a forward looking approach in managing its loan portfolio. Apart from using this model to compute the expected credit loss for its financial reporting, the predictive model can also be used to further enhance its loan application, credit monitoring and collection process.
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