IFRS 9 Implementation Challenges - Is Malaysia ready? Talk to us now!
As we all know by now, the major reboot from IAS 39 to IFRS 9 is the move from an “Incurred Loss Approach” to a forward-looking “Expected Loss Approach” for impairment assessment. IFRS 9 has significantly transformed many banks’ existing impairment assessment to address concerns about “too little, too late” provisioning for loan losses. While many banks have started to engage with advisors and consulting firms on this major change, it should be noted that a successful implementation of IFRS 9 is premised upon the successful and the close coordination between the Risk and Finance teams within the bank. Banks that approach an implementation in silos is likely to run into considerable problems at a later stage.
Many banks in emerging markets have asset allocations that are relatively non-complex and not subject to dynamic changes. This simplifies identification of business models for asset classification, but the same reasons call for extensive credit risk modelling efforts for impairment calculations. Hence, impairment modeling is one of the core areas where banks need to focus.
IFRS 9 uses a ‘3-stage model’ for measurement of Expected Credit Losses (“ECLs”), and one of the major challenges of implementing this model was tracking and determining whether there has been a significant increase in credit risk of an exposure since origination. Entities are required to recognise an allowance for either 12-month or lifetime ECLs, depending on whether there has been a significant increase in credit risk since initial recognition. This significant increase in credit risk needs to be measured ideally at the instrument level.
Currently under IAS 39, most banks determine their allowance for loan losses for their retail assets on a collective basis while for commercial assets, the allowance is determined on an individual basis. For retail assets, an assessment at the instrument level might be difficult to achieve and therefore, the collective approach is still clearly the preferred way to go, where the deterioration in asset quality would be checked for on a portfolio or a sub-portfolio basis. It would be pertinent to note that this is relevant for Stage 2. For Stage 3 classification, since it is nearly the same as the existing incurred loss model, this determination would continue to be on an individual basis. Consequently, for collective assessment, where it has been determined that there has been a deterioration in asset quality (on a forward looking basis), the lifetime ECLs need to be computed for that section of the portfolio. Banks may leverage existing segmentation schemes to identify the various segments of a portfolio that are deemed to have deteriorated, such as instrument type, collateral types and values, risk scoring etc.
The measurement of lifetime expected loss can be done by either a Loss Rate method or an approach that uses Probability of Default (PD) and Loss Given Default (LGD) estimates. The Loss Rate method looks at historical losses suffered and uses the same to estimate forward looking losses. While the Loss Rate method appears easy to implement, and at first glance it seems as if lifetime PDs and LGDs may not be needed for this method; the truth is that while for ECL computation PDs may not be needed, to determine significant deterioration in credit quality, lifetime PDs are still very useful. The article walks through a few numerical examples of methodologies using the Loss rate approaches as well as the PD-LGD approach.
Before we talk about different modeling methodologies in loss forecasting, it is important to understand how the loss ratio is calculated. The loss rate can either be calculated at an instrument level or at a cohort level.
