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In This List

Part Two IFRS 9 Blog Series: The Need to Upgrade Analytical Tools

IFRS 9 And The COVID 19 Pandemic Important Considerations

Part Three IFRS 9 Blog Series: The Importance of Efficiency and Transparency

IFRS 9 Blog Series: Tackling the Challenge of Calculating Impairment

2018 US Property Casualty Insurance Market Report


Part Two IFRS 9 Blog Series: The Need to Upgrade Analytical Tools

As we discussed in our first IFRS 9 blog, the financial crisis of 2007-2008 underscored the need to better understand an organization’s expected credit losses (ECLs). International Financial Reporting Standard 9 (IFRS 9) was put in place as a result to address perceived deficiencies in the accounting for financial instruments. Have you taken the steps needed to comply with the required changes? If not, the clock is ticking.

The Need to Upgrade Analytical Tools

Insurance companies should consider analytical tools for IFRS 9 that are robust, efficient, and transparent. In this blog, we will focus on the need for robustness. ECLs are typically generated by the estimation of three elements: (1) Probabilities of Default (PD) – a view on the probability that a bond issuer will not pay when due, (2) Loss Given Default (LGD) – a view on post-default recovery prospects, and (3) Exposure at Default (EAD) – the total value claimed by the holder of the bond against the issuer. Our approach aims to generate robust estimates in all three cases.

  1. 1. PD Estimation

PD estimation is particularly challenging given the need to have a point-in-time (PIT) view of default risk, which is forward looking until a bond matures. Matters are further complicated by data scarcity. Asset classes to which insurers are typically exposed are considered low-default portfolios, where the number of historical defaults are very low or non-existent. The latter is the case for bonds issued by sovereigns, regional governments, banks, insurers, and large corporates. For example, no sovereign rated higher than “BBB-” has ever defaulted over a one-year period, with almost all defaults occurring in emerging markets. This renders PD estimation exceptionally difficult.

S&P Global Market Intelligence has addressed these difficulties via the following steps:

  1. 1. Determine the public rating assigned by S&P Global Ratings as the starting point.
  2. 2. Assign a long-term or through-the-cycle (TTC) term structure to the PD based on over 40 years of historical S&P Global Ratings default data.
  3. 3. Transform TTC PD term structure to a PIT PD term structure using the S&P Global Market Intelligence proprietary Credit Cycle Projection Overlay (see figure below). This utilises six macroeconomic and market factors to estimate the current and projected future movements of country- and sector-specific economic and credit cycles.

Source: S&P Global Market Intelligence. For illustrative purposes only.

The above process is grounded in rich historical data, whilst reflecting our deep and broad experience in credit analysis, leading to robust bond-specific estimates of PDs.

  1. 2. LGD Estimation

LGD estimation is considered more difficult than the estimation of PD. Many institutions struggle with developing sound methodologies, with most settling on the use of average historical losses as indicators of future expected losses. This simplified approach is not well suited for IFRS 9 purposes as averages, by definition, are not PIT nor supported by empirical evidence.

Source: LossStats™ Model, S&P Global Market Intelligence. Data as of September 11, 2019.

S&P Global Market Intelligence introduced sector-specific LGD Scorecards over 15 years ago. These Scorecards are driven by a mix of S&P Global LGD experience and market data. We have also undertaken robust testing of our LGD solutions by backtesting on over 2,000 defaulted bonds issued by sovereigns, banks, insurers and corporates, with our approach reliably predicating the loss severity for approximately 1,400 bonds. Furthermore, the average difference between the point estimate predicated by our approach and the actual loss incurred is less than 1.5%.

Source: S&P Global Market Intelligence LGD Scorecard Tech document, December 2017

 

Given LGD does vary continuously from 0% to over 100% (including allowable recovery costs), these levels of reliability provide confidence for those looking to obtain robust LGD estimates to satisfy regulators, auditors, and stakeholders.

  1. 3. EAD Estimation

The EAD approach is dependent on the type of investments under analysis and is often rules-based, driven by the insurer. S&P Global Market Intelligence provides guidance on this topic to help insurers implement intuitive and data-driven rules.

Maintaining High Standards – Ongoing Update and Maintenance

Our team is focused on building robust credit solutions, as well as nurturing and enhancing these solutions so they continue to meet the  ever-increasing demands of our clients. This is achieved by way of our update and maintenance service, where a dedicated team delivers up-to-date data and analytics (e.g., macroeconomic forecasts) throughout the year.

To learn more about our robust, efficient, and transparent IFRS 9 offering, contact us here.

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