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Help Meet CECL and IFRS 9 Accounting Standards

Are you ready for CECL¹ and IFRS 9²? Don’t let an incomplete approach negatively impact your P&L.

The accounting standards represent a major shift in how you estimate Expected Credit Losses (ECL). Both standards focus on current expectations of future credit losses – Current Expected Credit Losses (CECL) through a single, life-time approach and IFRS 9 an International Financial Reporting Standard (IFRS) through a three-stage approach that reflects credit deterioration over time. Despite these differences, both standards require improvements in forward-looking analytics.

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Tap Into a Comprehensive Suite of CECL and IFRS 9 Solutions

Estimate ECL by starting with a through the cycle (TTC) assessment of probability of default (PD) and loss given default (LGD), converting these measures to point in time (PiT), factoring in reasonable and supportable macroeconomic forecasts, and then constructing forward-looking term structures for the remaining life of the exposure.

Calculating CECL

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

Generate reliable, forward-looking assessments with our Credit Assessment Scorecards and Credit Analytics models. Calculate TTC PDs and LGDs based on observed default and recovery rates from over 37 years of history. Scorecard numerical scores outputs are broadly aligned with S&P Global Ratings’ criteria³ and further supported by historical default data dating back to 1981.

Get a market-based view of default risk with PD Model Market Signals⁴. Adjust the TTC PDs to reflect the industry-specific position in the credit cycle. Our sophisticated equity-driven PD Model Market Signals enables you to compare the most recent credit signals of the industry/sector with the long-term industry credit signals. We automatically formulate industry-specific adjustments converting the TTC PDs into PIT PDs using current market conditions.

Incorporate reasonable and supportable macroeconomic forecasts with our Macro Model. Assess changes in credit risk across both user-defined and pre-defined forward-looking scenarios, based on a set of macroeconomic factors. The model is trained on S&P Global Ratings credit ratings and leverages the historical statistical relationship observed between credit rating changes and corresponding macroeconomic conditions.

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Access unrivalled default, transition, and recovery data with CreditPro^®. Evaluate PDs based on S&P Global Ratings default and ratings migration data covering 15,000+ companies, 175,000+ debt instruments, 200,000+ structured finance tranches, and 175+ sovereign entities across the globe. This robust analytics tool can also help you formulate future default and ratings migration scenarios, and validate internal rating systems.

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¹Current Expected Credit Loss; Accounting Standards Update (ASU) No. 2016-13, Topic 326, Financial Instruments – Credit Losses, June 16, 2016.

²IFRS 9 is an International Financial Reporting Standard published by the International Accounting Standards Board.
³S&P Global Ratings does not contribute to or participate in the creation of credit scores generated by S&P Global Market Intelligence. Lowercase nomenclature is used to differentiate S&P Global Market Intelligence PD credit model scores from the credit ratings issued by S&P Global Ratings.
⁴S&P Global Market Intelligence’s PD Model Market Signals combine a structural Merton model approach with country and industry risk factors and provide a daily PD for all public Corporates.

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