NEW FINANCIAL REPORTING A GAME CHANGER

Tue, 30 Jan 2018 14:20:03 +0000

THERE is very rarely an International financial reporting standard (IFRS) that has everything; complexity depending on the sort of financial assets you sit with, impactful in terms of its behavioural repercussions on institutional business decisions, a real cost cesspool requiring significant system investments for those with significant financial assets base.

Most often, we say that this standard or that standard will require a re-look at the system and will oblige relearning; the formation of project team etc.

We said that with IFRS 15 Revenue from contracts with customers, and we said that with IFRS 16 Leases. In fact, in my article published in The Accountancy Magazine on IFRS 16 Leases, I alluded to that.

Yet the impact by all of those standards will stand pale in comparison to IFRS 9 Financial Instruments, for those with a significant number of the portfolio of financial assets qualifying for amortised asset classification.

It is a standard that requires more than just a cursory last-minute foray into implementation.

In fact, few standards have captivated the international community the way that IFRS 9 has.

For instance, the way that IFRS 9 was publicised and reported and revered in Kenya over the last few months, one could be forgiven for thinking there was a huge catastrophe on its way to detonate the business community.

In short, it is a game changer, while it is the banks and other financial institutions that are most impacted.  The number of implementation issues that most banks are currently jostling with immense, whether it is the potential for profit and loss volatility, challenges with macroeconomic forecasting or risk modelling skills.

It is perhaps the introduction of the expected credit loss model in the determination of credit losses that is far more revolutionary in this standard.  Whether it is, how to determine the probability of default (PD) that is a point in time (specific), much more than the through the cycle (cyclical averages) probability of default that is relevant with the Basel II. How to determine the Loss Given Default (LGD) for a collateralised credit exposure given the necessity to calculate accurately the estimated cash flow streams from potential recoveries from the collateral.

Yet perhaps among all of this, and of most significance is the issue of how to take account of future macroeconomic scenarios in the determination of the expected credit loss as required by IFRS 9.

Here is why this is the most ambivalent and perhaps one that is keeping banking CFO constantly searching for the next article with just some information to make the problem go away.

 There is never a time when the risk officers, whose advice has often been pertinent, but yet often consigned to the backroom, are most needed than now, unlocked from the shackles of disguise in open abeyance.

Today, they will be assuming their key roles as frontline personnel, called into the centre by default. If a risk officer in the Bank must thank anyone, perhaps it is the gods at G20, at the International Accounting Standards Board (IASB) and the Financial Accounting Standards Board (FASB), the US counterparty. Why is this unchartered territory? Well, every credit risk modelling specialists, every finance specialists, every accountant is in a tunnel of ‘let’s see’. #

And I have yet to chat with the Regulators at Bank of Zambia to understand their gravitas with this standard. But they almost must learn overnight for them to be a valuable source of information.

They will need to be foremost intuitive experts at navigating the uncharted waters.

The complexity is understandable, because in the past and subsequent to the 2008 global market crash, there was a renewed urgency to deal with the causal effects of the financial market crash.  Much of it was blamed on the lack of robust accounting rules that could enable more timely recognition of credit losses, a very apt criticism of the incurred loss model of the International Accounting standard 39; Financial Instruments; Recognition and Measurements (IAS 39).

It was like a whoosh on the Auditors, ‘”the whipping club for blame apportionment, whenever an inconvenient downturn forced the inevitable.”  We escaped the financial markets mob lynching this time and IAS 39 had to be replaced and IFRS 9 was conceived with a caveat that it was going to require the recognition of credit losses using the expected credit loss model rather than an incurred model and forward-looking information was a necessary part of it. What am I saying? IFRS 9 requires entities to adjust the previous historical incurred loss based credit loss estimate into a forward-looking expected credit loss.

So how are we going to get those forward-looking numbers?

Firstly, the standard is sensitivity to the difficulties that can be expected in applying the expected credit loss model and as such, it introduced some operational simplification guidelines with respect to receivables without significant financing element. In other words, those financial assets that fall due within a year of origination.

The simplification guideline introduced is that IFRS 9 requires the information to be used in the calculation of the expected credit loss model, if it can be obtained without undue cost and effort.

Therefore, if forward-looking information cannot be obtained without undue cost and effort, it can likely be overlooked for simple financial assets with non-significant financing element, however, it is a judgemental call.

But this will most likely be a fall-back position for a number of small entities and those entities with short-term financial assets. It is, however, unlikely to be an acceptable position for larger entities, banks and other financial institutions. Therefore, those larger entities (with significant financial asset portfolios) must invest in systems capable of assisting them to generate credit losses that are sensitised with forward-looking information.

 An expected credit loss calculation that is forward-looking must be derived from the estimation of the current and forecast PD, LGD and exposure at default (EAD).

The EAD is the total outstanding amount adjusted by commitments or exposure to credit risk at the time of default.

It can, therefore, be determined from the existing portfolio contractual cash flows with an estimate of the commitments that will be drawn in future using historical observations. The LGD are the credit losses incurred as a consequence of default after deducting the value of collateral and is expressed as a percentage of the full loan (exposure) value.  There will be a question of estimating the expected recoveries from collateral, the timing of those recoveries and present value estimation.   IFRS 9 beyond prescribing an expected credit loss model in calculating credit losses, does not recommend a specific model to use to incorporate forward-looking information, except that the IFRS Transitional Resource Group (ITG) did advise that it is not sufficient to measure expected credit loss, using a single scenario (even the most likely one). An entity is required to consider multiple scenarios.

The ITG further noted that “the probability of default (PD) and credit loss for a range of different forward-looking scenarios is non-linear.  Further, the expected credit losses derived from using a single scenario are not the same as the expected credit losses determined by a range of different, forward-looking scenarios.”

The challenge is that entities will likely have insufficient data to determine statistically robust models and therefore expert judgement and the Central Bank’s macroeconomic econometric statistics will be key.

Parmani (2017) in Forward-looking Perspective on Impairments using Expected Credit Loss, published in Moody’s Analytics noted that “including macroeconomic scenario-based analysis gives a forward-looking view due to its range of possible scenarios.  The purpose of estimating expected credit losses is not to estimate a worst-case or best-case scenario, but to estimate the possibility that a credit loss occurs with the realization of the most likely scenario.

Understanding the risk or probability of a credit loss when incorporating the possibility that a scenario uses weighted probability, even if the possibility of a credit loss occurring is low, can help inform the likelihood of incurring a loss.

The scenario-based analysis incorporates forward-looking information into the impairment estimation using multiple forward-looking macroeconomic scenarios.”

De Vries and De Groot (2016) writing the forward-looking provisions of IFRS 9 article on Zanders noted that “incorporating forward-looking information means modelling business cycle dependency in your PD and LGD.

For homogenous retail exposures, forward-looking elements can be considered on a portfolio level by modelling the dependencies of PD and LGD percentages for realised defaults and losses; in essence, this is a bottom-up approach.

However, statistically significant parameters and models for default relations are difficult to obtain since there is a common time gap in observing and administrating both defaults and business cycle.

Even if there is statistical proof for macroeconomic dependencies in PD and LGD rates, it is advised to be cautious, since it also requires designing credible macroeconomic scenarios. As business cycles are difficult to predict, regular back-testing and continuous monitoring are important for an accurate and robust provision mechanism, especially in the first years after the model is introduced.”

It is important to realise that there may be many possible outcomes that a model will generate, and in that case, the standard would require that a representative sample of the complete distribution for calculating the expected value is utilised instead of modelling all expected possible scenarios.

As well as the various scenarios, that can be derived, incorporating forward-looking information into incurred historical losses will not be straightforward, a lot of judgement will be necessary in deriving and selecting macroeconomic model scenario that are correlated to the portfolio.

 About the author

Kelvin Chungu is an Assurance and Advisory professional and is contactable on +260-976377484.