The science of IFRS 9 and the art of Basel: Use of parametric thinking in provisioning

(Second 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 became effective on Jan. 1, 2018 and replaced 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 to be 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.

The spectrum of methodologies depends on the attributes of the segments and the degree of accuracy expected. These include estimating expected and lifetime loss assumptions from historical loss rates, roll rates (at either the aggregate or account level) and vintage curves to developing models for the Probability of Default (PD) and Loss- Given Default (LGD) parameters. For governance reasons, the technical aspects, features and assumptions of the models and estimation approaches should be thoroughly documented along with the points at which human judgment and intervention will take place. The limitations should also be described along with a discussion on how it will be addressed moving forward, what interim solution is in place (whether through a place holder number or proxy assumption), and if the resulting model risk is within tolerable thresholds.

For instance, loss rates, vintage curves and roll rates (e.g., Markov chain) are generally favored for the retail portfolio as these can be practically aligned with current risk management practices and provide an intuitive portfolio and term structure, especially for banks that are used to monitoring via segmentation and aging-based measures. The obvious drawbacks — such as backward-looking view, assumption of consistency in transition or delinquency movements, no capture of seasoning effects, slow reaction to changes in the portfolio mix and risk characteristics, recovery expectations that are difficult to incorporate, which render a 100% loss assumption when default stage is achieved — can be addressed by requiring multiple overlays and dynamic simulations to address the limitations that improve accuracy but also increase estimation risk.

In cases where models are built to explicitly calculate the PD and LGD parameters at the account, portfolio and facility levels, the more accurate models can be used for risk management purposes and even decision-support activities like pricing. Philippine financial institutions (FIs) that adopted models for certain exposures are aware of the “start-up” and continuing cost and investment required — building models requires significant effort, resources and time. Models also require rigorous maintenance, governance and validation. At this stage, the models that have been built may have produced quantitative results, but the real challenge is to allow these models to stabilize, learn and iterate. We estimate that FIs that have implemented models for IFRS and Basel purposes need another 12 to 15 months before gaining conclusive results.

Ensuring thorough documentation also helps drill institutions towards the full-scale use of machine learning. As the models and estimation approaches “learn” through time, complex computations will consolidate into pockets of decisions and will respond directly from the raw data footprint, which could range from sensor and mobility data used to evaluate logistical and supply chainoriented customers to flow-based financial variables (as opposed to ratios). The implication is significant — the modeling and estimation approaches will bypass the stage of structured data and calculation parameters and enable the codification of decisions. It is just a matter of time before the parametric thinking approach to calculating expected credit loss (ECL) provisions and economic capital will be dislodged by the rise of “coding drivers.” Future-proofing exercises should therefore be applied, and we will come back to this with an illustration for corporate and institutional exposures.

What we have covered so far are the developments at the base ECL model — the composite PD, LGD and Exposure at Default (EAD) parameters — that reflect idiosyncratic or specific risks pertaining to the exposures. The other element that needs scrutiny and improvement in the coming months is the overlay mechanism, which, in IFRS, is intended to capture the forward- looking view and the interdependent relationships within the wider economy. To be specific, the overlay mechanism represents an institution’s own economic reading, which makes the IFRS 9 ECL process a foreseeing exercise of marking- tomodel and marking-to-view.

This is where stress testing will be useful for FIs in plumbing the overlay mechanism. Stress testing also includes macroeconomic forecasting models that have evolved out of the need to support internal stress testing for financial and capital plans, as opposed to the regulatory stress testing that are currently designed to be uniform and which tend to be blunt (think of the real estate stress testing exercise). By design, stress testing is prepared for both immediate and long- term horizons and incorporate forward looking scenarios and interdependent factors. These properties — adjusted for the downturn scenarios — are what would help strengthen the overlay mechanism. The stress testing approaches we are seeing in the industry are first-generation models that have at least served the purpose of informing the IFRS 9 modeling and estimation approaches. The stress testing approaches are currently aggregations of calculations and processes that require a lot of manual intervention and judgment, ranging from the work-in-progress integrated stress testing used for strategic and corporate planning, financial and capital planning, and enterprise and business risk assessments to the resilience planning that underlies the capital adequacy and recovery planning. This naturally leads to confusion on the application of the forward-looking economic view and the probability-weighting of scenarios. The stress testing models we have seen in the industry need to be repurposed as dynamic and agile, and we expect another 12 to 15 months for development and strengthening. This improvement is timely given the full implementation required for the stress testing and macro-prudential regulations by 2019 at the latest.

In the third part of this article, we will continue with what FIs can expect in the next 12 to 15 months.

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.