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ALLL’s well that ends well: CECL, strategic planning and forecasting ALLL


In articles one and two of this three-part BAI Banking Strategies series, we outlined CECL, the Current Expected Credit Loss standard issued by the Financial Accounting Standards Board. Those pieces discussed a savvy approach to gather the required data, consisting of two crucial elements:

First, assemble the right internal team to pull accurate data, with representatives from these categories:  

  • Risk
  • Accounting
  • Lending
  • Treasury
  • IT
  • Credit risk, and
  • Internal audit

Then, pull the right, required data, including:

  • Current loan data
  • Loan processes/systems
  • Current charge-off and recovery data
  • Systems and processes, and
  • System changes and mergers/acquisitions

Once you collect this data, a third step enters the picture: leverage it to forecast your financial institution’s allowance for loan and lease losses (ALLL). This allows your institution to maintain the minimum amount of capital that CECL requires—and make better strategic business decisions as a result. 

In the final article of this series we’ll discuss the many things you can learn from this data, which you can use calculate the best calls about your institution’s future.

Data compilation: Segments to strategy

Part of data compilation for CECL involves segmenting it out at different levels. You can now take a look at this new information to decide which loan markets to work in.

Start by analyzing which sub-segments of the loan portfolio perform well (limited losses if any at all; segments you’d want to stay in) versus those carry greater losses (sub-prime sectors). When you discover disproportionate losses from certain levels, your institution can evaluate whether to remain in those markets.

Working within the markets you wish to serve, this data can also help determine whether your pricing strategy is profitable. Comb through each sub-segment with this assessment in mind.

As you start to make these decisions, forecast your financial institution’s ALLL for the future based on the economic metrics. Calculate scenarios for your loan portfolios based on how each perform within the economic metrics.

The results from these simulations will support your case for portfolios to further explore–and those where it pays to reduce exposure or alter pricing strategy. In each case, the data provides strategic reasoning to revising your current business plan.

Simulation considerations: Hanging in the balance sheet

Two questions to consider as you play out the simulations:

  • How would you want to shape the institution’s balance sheet due to any losses you’re been experiencing?
  • If things start to change, how do you want to alter strategy because you expect the losses in some markets to grow or shrink?

Keep in mind that the dynamic mix will change as you review scenarios.

From illumination to application

Sub-prime auto lending is a good example of how one size doesn’t fit all:  markets that generate profit for one institution may mean losses in another.

Is the predicted amount of interest from the portfolio of sub-prime auto loans enough to cover the loss rate? Does it remain profitable? Further, if the interest covers the loss rate, does it do so to a point where it actually earns less overall than another potential market portfolio?

Analyzing data to answer these questions can help a financial institution more accurately discern whether sub-prime auto loans yield profits.

Profit implications: Credit cards and risk

One enormous change CECL brings centers on a financial institution’s assessment of credit cards as risk.

For example: When a customer spends just $1,000 on a credit card with a limit of $2,500, an unused portion of the credit line remains—but what happens if things start to go bad? Is this customer likely to tap that unused portion and max out that credit card? And if he does, what is the likelihood that he can’t pay it back?

Adhering to CECL, financial institutions will have to understand the unused portion across its portfolio and the credit risk that comes with it.

By collecting adequate history and segmenting the data into detailed pools, financial institutions will better understand risk sources in their balance sheets. And this data enhances strategic planning as it pinpoints less profitable business lines. With that information, an institution can then make pricing decisions that improve profits and performance.

Start now: Collect and segment the data for better analysis and you’ll create a stronger future. Put another way: Applied to ALLL, the data uncovered by CECL losses can ultimately be your gain.

Tom Caragher is product manager, Enterprise Performance Management, Fiserv. Mr. Caragher has more than 25 years of experience in direct asset liability and balance sheet management. A master’s degree graduate from Arizona State University, he joined Fiserv in 2005 in product development and product consulting.