# CECL – Manage the Change

**Libby Sharman**  
**August 30, 2017**  
**3 min read**

The changes inherent in the shift from FASB’s incurred model to the [current expected credit loss model](/content/whitepapers/fasb-cecl-prep-kit/index.html) (CECL) present many challenges for financial institutions and accounting professionals alike. However, the transition from an incurred model to estimating expected credit losses is not meant to be a cumbersome process, let alone one that is unmanageable.

**Debunking Common Myths**  
_Time to retire – CECL requires making a specific set of decisions, therefore, the effort required is known and not worth seeking a career change_  
_Prophecy is required – FASB is clear that the shift to a forward looking approach requires that the institution apply reasonable and supportable [forecasts](/content/webinars/cecl-methodology-series/index.html), not apply correct forecasts_  
_Methodology first – It is not recommended to start with methodology selection, but rather to start with an analysis of your loan level data and segmentation elections_  
_[DCF](/content/webinars/discounted-cash-flow-modeling/index.html) is for big banks – A discounted cash flow analysis is for banks that have an advanced software partner with built-in capabilities due to the complexity_  
_Benchmark Increases – This is highly dependent on the type of credit and current processes_  
_[Life of loan data required](/content/webinars/cecl-webinar-data-quality/index.html) – this approach is not inherently volatile_  
_Economic cycle data required – this kind of information is not required_  
_Institutions cannot make the necessary decisions yourself – under the right conditions and with proper preparations, institutions can manage this transition_

**Starting Point**  
Estimating expected credit losses is simply executing a series of decisions and making choices from a pool of thousands of possibilities. For example, an institution might have 4 segmentation possibilities, 10 segments within their loan pools, 5 models to apply to these segments, and 10 forecasting factors. That means there are 2,000 possible configurations that are both theoretically reasonable and supportable. In reality, this institution would not be able to support all 2,000 configurations, but it would provide a starting point at which the bank or credit union could then begin to narrow the scope based on their internal/external constraints, data symmetry at the loan level, impact on capital allocation and then the judgement and experience of the individual(s).

With all of that said, there are many ways to estimate expected credit losses, which is hopefully reassuring that this task is manageable. However, it is important to mention that a lot of work must be done in order to manage the process and actually make the series of decisions necessary by the effective date.

### About the Author  
**Libby Sharman**  
Libby Sharman is a Vice President of Marketing at Abrigo.  
[Full Bio](/content/people/libby-sharman/index.html)

## About Abrigo  
Abrigo enables U.S. financial institutions to support their communities through technology that fights financial crime, grows loans and deposits, and optimizes risk. Abrigo's platform centralizes the institution's data, creates a digital user experience, ensures compliance, and delivers efficiency for scale and profitable growth.