What is Current Expected Credit Loss (CECL)?
CECL is a model that estimates credit losses by using historic information, current conditions, and reasonable forecasts to better estimate and measure credit losses for loans and debt securities.
In other words, banks, savings associations, credit unions, and financial institutions will need to use data—historical, current, and enriched data—to paint an accurate picture of what their provisions should look like for each of their lending portfolios. By complying with CECL you will:
- See an accurate representation of what your reserves should look like
- Gain a more holistic view of your lending portfolios
- Make data-driven decisions based on completed and accurate records
- Optimize your risk management plans with an ongoing data quality
- Mitigate risk for future stress scenarios
What are CECL implementation deadlines?
- December 2020 for large, publicly-traded financial institutions
- December 2023 for small, publicly- and privately-owned financial institutions
Why is data quality important to CECL compliance?
Data quality is a foundational first step CECL modeling so you can trust that accurate, relevant, and complete data helps you precisely estimate and measure credit losses for loans and debt securities. Without trusted data, you will not be able to fully comply with CECL and test models that fit both the regulation and your specifications.
How Experian can help you with CECL compliance?
To get started on complying with CECL, you will need to evaluate your current data landscape and determine what is needed to clean it up and build a sustainable data quality and data governance strategy.
As part of our data quality management platform, we have the tools to tackle consolidating, cleaning, and completing your database so that you comply with the CECL standard.
Capabilities include:
Profiling—Identify how your database is structured and where the gaps lie within your data.
Deduplicating—Examine relationships across records to accurately consolidate consumer assets.
Cleansing—Clean up your data by fixing incorrect, incomplete, and improperly formatted records.
Standardizing—Transform your data by creating a consistent format across all data sets.
Monitoring—Control your data to make sure your data quality efforts stay accurate and fit for purpose.
Data quality is the first step in your CECL journey. See how data quality plays an important role in getting started with your CECL implementation.
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