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CECL: Importance of Data

Why does it matter?

CECL requires a lifetime loss rate; therefore, the observation of a loan portfolio over a complete economic life cycle is critical for deriving lifetime loss rate. While there are solutions that leverage peer group data or market indices that can compensate for lack of institution-specific data, those standardized data sets may not capture your bank’s unique risk characteristics, portfolio composition, or geographic presence. If possible:

  • Gather as much historical data for your bank as available
  • Capture a full economic life cycle of data 

Case study: The availability of historical data differs for all financial institutions. We’re highlighting two real-life scenarios below:

  • Bank A has data and loss history beginning in 2005 (15 years). Bank A is able to leverage a 15-year look-back window in its model, and therefore quantitatively justifies 75% of their allowance through a discounted cash flow calculation using observed historical loss rates, regression modeling, and a forecasting element. 
  • Bank B has data and loss history beginning in 2015 (5 years), a benign credit period characterized by the economic recovery. The 5-year look-back window does not capture a full economic cycle, so Bank B will have to account for this data gap by either:
    • employing market, peer, or index data in their quantitative calculation
    • Adjusting with qualitative factors

Why we love data? Having a robust data warehouse has benefits beyond supporting a comprehensive CECL model. Analyses such as prepayment speed studies, utilization rate studies for unfunded commitments, and other analytics can be run on historical data sets. Additional sensitivity analyses can be performed to gain insight into how the bank’s loan portfolio performed historically across changing economic scenarios.

What if I don’t have enough data? If your institution does not currently have a robust historical data set, consider how you can gather this data in order to implement bottom-up historical loss data in the future. Financial institutions will likely be strongly encouraged to use their own data in modeling expected credit losses and have a transitional plan in place if you are currently relying on market data or high-level call report data. 

By now most financial institutions, both large and small, have developed a CECL implementation plan, now it’s time to evaluate the data which is defined in Valuant’s CECL Readiness Roadmap

Want to maximize the power of your data with ValuCastContact our Product Solutions Consultant, Gus Alexander, to learn more! 

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As part of an annual study that began in 2020, Valuant conducts analysis on ASC 326, commonly referred to as “CECL”. The study contains key data statistics and insights around the US Regional and Community Banking sectors and the impact CECL has on their Allowance for Credit Loss (ACL) Coverage Ratios.

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