The New Impairment Model Under IFRS 9 and CECL
First published:
ISBN: 978-1-78272-347-9
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The Impacts of CECL: Empirical Assessments and Implications
Michael Fadil
Introduction
The New Era of Expected Credit Loss Provisioning
The Marking of CECL Standard: Comments and Reflections
Sources of Modelling Variation in CECL Allowances
A CRO’s Perspective: Implementing, Operationalising and Governing of IFRS 9
Implementing Both IFRS 9 and CECL
Macroeconomic Forecasting and Scenario Design for IFRS 9 and CECL
Technology Solutions for CECL and IFRS 9
Implementing IFRS 9: Quantifying Expected Credit Losses in Retail and Wholesale Portfolios
From Incurred Loss to CECL: Historical Perspectives and Practical Guidance
Loss Forecasting Retail and Commercial Portfolios for CECL
Implementing CECL at Small and Community Banks
The New Impairment Model: Audit and Disclosure Challenges
The New Impairment Model: Governance and Validation
The Impacts of CECL: Empirical Assessments and Implications
How the New Impairment Model Could Affect Banks’ Business Models
Measuring and Managing the Impact of New Impairment Models on Dynamics in Allowance, Earnings and Bank Capital
Integration into Regulatory Capital Frameworks
Implications for Equity and Debt Investors
INTRODUCTION
Much discussion has occurred in the banking industry with regard to the potential impacts of CECL, for which the accounting standard update was finalised in June 2016. The primary analytical focus has been on how a bank should develop the loss-forecasting models and modelling framework to calculate the lifetime expected credit losses. While developing a modelling framework that will be GAAP-compliant is critical to implementing CECL, understanding how CECL reserves will behave is just as important. Despite the importance, impact analyses to try to fully understand how CECL would have actually performed over the past 10–15 years have been limited, and few have articulated how the total bank CECL reserves might have behaved through the last downturn. For example, Breeden (2017) used a “large dataset from Fannie Mae and Freddie Mac to test a range of models and options”, all allowable by the CECL guidelines, and concluded that for 30-year, fixed-rate, conforming mortgages the lifetime loss rates can “vary by a factor of 2”, depending on which loss-forecasting model methodology a bank chooses. Additionally, Pan, Wang and Wu (2017) of Moody’s Analytics used a historical
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