IFRS 9 and CECL: The challenges of loss accounting standards
By Tom Kimner, Head of Global Product Marketing and Operations, Risk Management
The Financial Accounting Standards Board Accounting Standards Update on Financial Instruments – Credit Losses (Topic 326) was the second phase of a global accounting standards change. This began when the International Accounting Standards Board published their standard, known as IFRS 9 Financial Instruments (a replacement for IAS 39), to the rest of the world in 2014.
The guidance relating to the measurement of credit losses (using a current expected credit loss estimation) represents a fairly significant change from the previous incurred loss model, which was in place for some time. For example, the incurred loss model recognized losses only when they reached a probable threshold of loss. Many analysts have suggested this model had a negative impact during the financial crisis because potential future losses were recognized and provisioned far too late.
While the US standard (known as current expected credit loss, or CECL) deviates in a few significant ways from the international IFRS 9 standard, both revised accounting standards share an important feature: The calculation of the expected loss is now based on the life of the loan. This change increases the credit impairment over the affected assets (with some estimates being as high as a 35 percent), resulting in higher provisioning expenses and a negative impact to capital.
Institutions must ensure that the allowance models used for stress testing keep pace with accounting standards development and that assumptions remain consistent between testing activities.
Working with uncertainty
IFRS 9 and CECL are primarily principle-based. As such, the implementation guidelines will likely continue to evolve as consensus is built around best practices. Given the evolutionary nature of these standards, institutions may need to continue iterating model development cycles even after transitioning to IFRS 9 and CECL reserving.
Because IFRS 9 and CECL development happens concurrently with ongoing stress-testing activities, it is likely that stress tests will need to include preliminary allowance models that are missing a full set of reviews. In a decentralized environment, additional model risk may emerge as methodologies and models used in stress test production become out of sync with the IFRS 9 or CECL models. Institutions must ensure that the allowance models used for stress testing keep pace with accounting standards development and that assumptions remain consistent between testing activities.
Many banks are not prepared for the ever-increasing integration of finance and risk data required for IFRS 9 or CECL and stress testing. Lifetime loss models may rely on data elements not previously required for stress testing. This may create data integrity risk, as analysts obtain the required data through ad hoc channels in order to meet their stress-testing deadlines. Institutions must ensure that the data used for stress testing ties with data used for IFRS 9 and CECL model development.
Meeting the challenges
To meet the changing demands of regulatory compliance and financial standards, organizations must look at solving these issues collectively and comprehensively across several areas. Otherwise, the overall costs of compliance can become burdensome, especially if various parts of the organization find themselves creating redundant or overlapping processes.
Consolidation of finance and risk data provides a number of benefits across the institution. A common data repository for stress testing and allowance estimation greatly reduces audit and reconciliation issues and model risk. It also lessens the overhead of managing multiple platforms.
An organization’s loss modeling and stress testing processes must be robust, yet flexible. They must be able to accommodate dynamic changes in allowance modeling while maintaining sufficient controls to withstand the regulatory scrutiny of capital adequacy assessment under stress. Institutions will find that consolidation of their stress test and allowance estimation platforms provides the best foundation to accomplish this goal.
A centralized model library provides a structure to maintain governance over the stress-test process as allowance model development and testing progress. With proper model versioning, management can maintain oversight and make informed decisions about new model integration into the stress-test process.
In addition, a flexible and modular model library facilitates sensitivity testing. This becomes an increasingly important tool to understand the larger impacts of changing models and assumptions over the IFRS 9 and CECL development and reporting cycles.
By iteratively swapping out model components, comparisons can be performed between model versions. Institutions can also quantify the overall effect of model refinements on their balance sheets while under stress. Sensitivity testing is also an effective tool for trend attribution analysis – a key component of stress testing. Prior stress tests can be rerun with updated model components, or vice versa, to quantify the effects of the ongoing model development on stress test results.
The changes in regulatory stress testing and in accounting standards have necessitated the need for historically independent risk and finance divisions to further integrate and collaborate. The importance of tight cooperation and sharing of data continues to increase under the new accounting standards. Departmental lines are blurring more and more with regard to regulatory and accounting compliance, and the interdependence of the various functions will further intensify.
The transition to IFRS 9 and CECL has presented many financial and operational challenges – compounded by the numerous implementation details that continue to be subject to varying interpretations. Institutions must address these challenges holistically with flexibility to adapt over time. Regulators and investors need to know banks are effectively assessing and managing the risks in their portfolio. Proactively implementing a well-governed approach to managing data and models instills confidence that processes are in place to manage these critical issues now and in the future.
Tom Kimner leads the Risk Marketing and Operations area within the Risk Research and Quantitative Solutions division at SAS. He is responsible for executing the overall marketing plan for risk management solutions as well as coordinating risk priorities and operations on a global basis. Prior to joining SAS, Kimner spent the bulk of his career at Fannie Mae in various senior management roles spearheading corporate initiatives to more effectively manage credit and financial risk. He also worked for a housing and finance regulatory agency and a Washington think tank. Kimner has testified before the Financial Services Committee of the US House of Representatives and regularly speaks at risk conferences and other SAS-hosted events.
- The 5 new rules of retailWhy did the rules of retail need to be rewritten? Because 2020 happened and shopping patterns changed overnight. There were product shortages, store closures and long lines to get in the few stores that were open. Consumers moved to online shopping in droves and expected a seamless experience in-store and online: Curbside pickup; same-day delivery; contactless delivery; buy online, pick up in store. The list goes on.
- Resilience in the face of unpredictabilityUnpredictability can “shatter and reshape” a society. And in these unpredictable times, it is important to remain resilient and be prepared to bounce back. This article explores what it truly means to be resilient, how to build it, and how analytics can help you act when your resilience is tested.
- Saving lives during a global pandemic through medical resource optimizationCleveland Clinic is operationalizing analytics to combat COVID-19, creating innovative models that help forecast patient volume, bed capacity, medical equipment availability and more.
What to read next
Some financial institutions struggle with credit loss allowance estimation. Learn how leading organizations have done things differently – and gotten it right.