Suits The C-Suite

(First of three parts)
IFRS 9 is an International Financial Reporting Standard (IFRS) promulgated by the International Accounting Standards Board on July 24, 2014. It addresses the accounting for financial instruments and features three main topics: classification and measurement of financial instruments; impairment of financial assets; and hedge accounting. It will become effective in 2018 and replaces International Accounting Standards (IAS) 39 Financial Instruments: Recognition and Measurement and all previous versions of IFRS 9. In this article, IFRS 9 is referred to as a “science” because of its systematically organized body of information and measurements on specific topics.
Basel III (or the Third Basel Accord or Basel Standards) is a global, voluntary regulatory capital and liquidity framework agreed upon by the members of the Basel Committee on Banking Supervision (BCBS) in 2010–11. It was scheduled to be introduced from 2013 until 2015; however, the implementation has been extended to March 31, 2019. Another round of changes was agreed upon in 2016 and 2017 (informally referred to as Basel IV) and the BCBS is proposing a nine-year implementation timetable, with a “phase-in” period to commence in 2022 and full implementation expected by 2027. Basel III was developed in response to the deficiencies in financial regulation that came to light after the financial crisis of 2007–08. Basel III is intended to strengthen banks’ capital requirements, liquidity, maturity profile, and leverage. It also introduced macroprudential elements and capital buffers designed to improve the banking sector’s ability to absorb shocks from financial and economic stress; and reduce spillover effects from the financial sector to the real economy. Basel is an “art” form in the context of the need to perform skillful planning and creative visualization in fully comprehending its dynamic processes and uncertainties.
Financial institutions recognize that provisioning and stress testing need to go together, allowing, at any given time, the determination of the credit cost and capital usage of an account, transaction or portfolio. This desired state poses complex and tremendous challenges. It would be helpful to frame at the onset that these exercises can be broadly classified into two types, as an adaptation of Daniel Kahneman’s view on the two selves: Type 1 system for fast, intuitive and unconscious views, and Type 2 system for slow, calculating and conscious thoughts. At the risk of oversimplifying, we do not know yet which exercise will become which system, but what is clear is the emergence of parametric thinking to grapple with the foreseeable function required for calculating expected credit loss (ECL) provisions under IFRS 9 and the related capital usage that will be highlighted with the implementation of the stress testing rules under BSP Circular 989. Here’s a sample illustration on how exposures will be viewed in the coming months (stripped of technical assumptions): Assume a corporate exposure with a moderate quality rating, belonging to an industry that is exhibiting concentration risk, within a benign macroeconomic scenario. If the recovery experience is 65% and the overlay-adjusted probability of default (PD) is 1%, the ECL provisioning cost is .35% and the capital usage is 5%. If recovery experience falls to 55%, the provisioning cost is .45% and capital usage at 7%. If the macroeconomic scenario deteriorates — assume PD at 3%, the provisioning cost is 1.35% and its capital usage is 10%.
The illustration may make computational sense, but note the gap between the ECL and the capital usage. At some point, the ECL will increase to consider the “transmission” from the macro-economic assessment to the credit risk pertaining to the obligor, and this scenario is likely to happen as the IFRS 9 and stress testing exercises become clearly linked in the next 12 to 15 months.
This scenario requires adaptive yet rigorous models and estimation approaches, but the current situation is an irreversible progression from historical, incurred-loss oriented IAS 39 models to Basel-based techniques that are being extended to meet the expected loss criteria and forward-looking view of IFRS 9. As the techniques undergo development or enhancements, it would be helpful to view the provisioning exercise as consisting of parameter drivers — namely the base parameters for Exposure at Default (EAD), Loss-Given Default (LGD) and PD, adjusted for the overlay mechanism and the discounting process (these same parameter drivers can be used as inputs for portfolio management and capital planning, adjusted for horizon, confidence interval and other properties). There is literature available for these parameters so we will skip the introductory discussion and discuss three areas to strengthen the parametric approach to provisioning — clarity on the definition of default, strengthening the staging assessments, and plumbing the overlay mechanism.
Default and staging assessments should be clear, both operationally and in principle. The definition of credit impaired defines what should be Stage 3 for IFRS 9 and is loosely equal to our understanding of non-performing loan exposures. This definition is key for both modeling and estimation approaches, as well as disclosure purposes. The definition of default should be consistent with internal credit risk management practices, and for purposes of assessing significant deterioration, could be different to that used for regulatory models — not all default events are immediately considered credit-impaired. However, in practice, what we are seeing is that the definition of default is shaped mainly by regulatory requirements, and we would not be surprised with an alignment between financial and regulatory reporting for consistency and simplicity, especially for modeling purposes. This means that the 90-day definition looks like a prescription that will be generally observed, although the 30-day backstop does not automatically mean an exposure is considered in default — at most, it would attract a lifetime ECL until the default state is concluded, in which case there is already an outcome (i.e., PD is 100%) and the situation shifts to a recovery strategy issue. This is where financial institutions are advised to regularly perform their stress testing of those jumps or non-linear increases, on top of strengthening the governance around the staging assessments, ranging from the default tagging and classification process to early warning indicators and quantitatively-supported risk assessments to supplement a financial institution’s credit evaluation process.
We previously mentioned that institutions that adopted the now-replaced IAS 39 regime and are immersed in the internal ratings-based approaches of Basel will feel that there is a collective déjà vu, as quantitative and statistical techniques start to dominate the methodology discussions. There are actually three mental models that need to be fused and redesigned, with iteration through time, in coming up with an operationally rigorous IFRS 9 — IAS 39, Basel IRB, and stress testing. We expect a few of the IAS 39 models to be extended as interim measures under IFRS, before eventually being discarded or even mutating if the proxy factors become the norm, especially in micro and retail exposures. But most of the changes — especially for corporate and institutional exposures — will be borrowed from the IRB approaches, which would include adaptation of the capital requirement parameters of Basel, requiring high standards around governance and model development and validation. In its capital adequacy state, the IRB models are generally conservative that use downturn assumptions and scenarios, use a 12 month horizon and use a cost of capital discounting treatment (rather than the effective interest rate).
In the second part of this article, we will continue the discussion on IFSR 9 and Basel, looking at the parameters relevant to the base Expanded Credit Loss model.
This article is for general information only and is not a substitute for professional advice where the facts and circumstances warrant. The views and opinion expressed above are those of the authors and do not necessarily represent the views of SGV & Co.
Christian G. Lauron is a Partner of SGV & Co.