Loan Participations, CECL, Stress Testing, Loan Modeling for Banks and Credit Unions

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Dear Congress: Don’t toss CECL out, work with FASB to amend it
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11 January 2020

St. Paul, MN (January 11, 2020) – Minnesota Credit Union Network (MnCUN) and Deep Future Analytics (DFA) announced their partnership today. MnCUN will make available DFA’s Prescient Manager™ software to its membership. Prescient Manager™ is an easy-to-use, web-based credit risk forecasting and stress testing solution for credit unions and community banks. The software’s functionality includes:
• Accurate, scenario-based, account-level FAS 5 ALLL and CECL forecasts including discounted cash flow functionality.
• New loan pricing optimization leveraging the same cash flow model as for CECL.
• Scenario-based loan valuations for purchases and sales of loan participations.

Joseph Breeden, founder and CEO of Deep Future Analytics said, “We are excited to be partnering with MnCUN. We share a common vision that our accurate, scenario-based, account level cash flow models can create value across many functions in the FI. These solutions are integrated and coordinated in a way that a collection of independent models cannot be. MnCUN will be a great partner for bringing this capability to Minnesota credit unions.”

“Deep Future Analytics will help best position Minnesota credit unions to manage and anticipate risk. The all-in-one software calculates the necessary lifetime loss forecasts for CECL, but also provides accurate and actionable information for portfolio management, account management, and loan pricing,” said John Ferstl, Chief Operations Officer for MnCUN.

ABOUT DEEP FUTURE ANALYTICS Deep Future Analytics is a joint operational venture of Prescient Models, LLC and Nuvision CUSO Holdings, LLC, a CUSO operated by Nuvision FCU. Dr. Joe Breeden, founder of Prescient Models, brings more than 20 years of experience leading financial institutions through predictive financial modeling, allowing clients to achieve a real understanding of portfolio dynamics for retail lending. Nuvision FCU was founded nearly a century ago as the credit union of Douglas Aircraft, its values were forged in the factories and plants that made the region prosper. Now with assets well-over $2B, Nuvision is a multi-state Credit Union, with branches in Southern California, Arizona, Wyoming, Alaska and Washington.

About the Minnesota Credit Union Network

The Minnesota Credit Union Network is the statewide trade association that works to ensure the success, growth and vitality of Minnesota credit unions. With approximately $25 billion in assets, Minnesota credit unions are local, trusted financial cooperatives that serve more than 1.8 million members at nearly 400 branch locations around the state. As not-for-profit institutions, credit unions give back to the communities they serve. For more information, visit


Media Contact:
Charles Hoy, Director of Business
Deep Future Analytics LLC
(505) 690-7195


15 December 2019

The proposed rule provides helpful comments on how the new Current Expected Credit Loss (CECL) standard should be managed, validated, and monitored. One seemingly simple statement, however, has significant implications.

In the sections “Analyzing and Validating the Overall Measurement of ACLs”, “Responsibilities of Management “, and “Examiner Review of ACLs”, versions of the following statement are used:

“…comparing estimates of expected credit losses to actual write-offs in aggregate, and by portfolio, may enable management to assess whether the institution’s loss estimation process is sufficiently designed…”

Comparing expected credit losses to actual write-offs seems sensible as a general principle, but unfortunately does not work for CECL. CECL is not a loss forecast. First, CECL is a “lifetime” loss forecast where lifetime is not contractually assured. Since a comparison of CECL estimates to actual losses will generally be for a period that is less than the full life of the loan, the timing of the loss expectation will be critical for such a comparison, and yet many CECL-compliant methods make no attempt to correctly time future losses. Therefore, comparing over any time frame less that the full lifetime can be problematic.

Several points in the CECL guidelines will also cause it to deviate dramatically from future loss experience over any chosen fixed horizon, even when considering only existing loans. Some known examples where CECL will not align with actual loss experience are:

• Defaults after a loan renewal or extension where CECL estimates stop at the extension.
• Reduced losses because of term extensions or loan rewrites.
• Increased utilization of lines of credit or credit card, either normal balance growth by the consumer or in response to a credit line increase, leading to increased losses.
• Consumer activation of inactive accounts leading to increased losses.

The greatest difficulty is in measuring a CECL-compliant historic loss experience given the chosen payment allocation rule for lines of credit. This is an accounting election that dramatically affects model performance and can even lead to changing how the models are created, even though actual losses are not being changed.

To tell developers, managers, examiners, and the board that CECL estimates should be compared to actual write-offs is certain to create a false expectation of what CECL is and how it can be monitored. In each section where this is mentioned, it is the first such item suggested, which will further increase the emphasis among those subject to the rule. Particularly alarming is the idea that examiners will judge a CECL model based upon how closely the CECL estimates align with actuals given all of the above reasons that the CECL estimate will, in fact, never align with actual write-offs regardless of the accuracy of the underlying model, i.e. even a perfect CECL model would fail this test.

The rule should be modified to make clear that CECL will NOT agree with actual write-off experience and can only be compared to a retrospective CECL loss in which actual write-offs have been adjusted for all of the accounting rules present within CECL. This means that the institutions must estimate a historic CECL loss against which to compare the CECL estimates, both of which are different from the actual historic write-offs.

Comparable to this is a historic contribution analysis where past losses are split into components of CECL loss and non-CECL loss, perhaps even splitting the non-CECL loss by some of the causes listed above.

Obviously, both of these approaches are additional work, but they are the only way to perform the recommended comparison. If this is viewed as too difficult for some lenders, then the statements about comparing CECL loss estimates to actual write-offs need to be deleted and left to the kind of detailed model performance review conducted during model validation, not as part of high-level oversight.

As CEO of Prescient Models LLC and Deep Future Analytics LLC, I have already seen this confusion in action. We are a CECL provider to nearly 200 regional banks, community banks, credit unions, and finance companies. We also provide validation services for many in-house CECL models. Prior to CECL, I have worked in credit risk modeling for 27 years and written many articles and books on the subject, including the Living with CECL series. During my CECL experiences, clients, validators, and auditors have already demonstrated confusion over the simple point that CECL is an accounting calculation, not a loss forecast.

As a long-time model developer, I sympathize with this confusion, because CECL is the first time in my career where I have needed to create a model to predict a quantity that itself is subject to how I as a developer interpret the CECL guidelines. I hope that in writing rules on model validation and monitoring, the language can be written more carefully to avoid reinforcing this confusion.

Joseph L. Breeden, PhD CEO, Prescient Models LLC and Deep Future Analytics LLC


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Dr. Joe Breeden, COO and Chief Scientist at Deep Future Analytics, has been the nation’s preeminent modeling practitioner for more than 20 years. He has created models through the 1995 Mexican Peso Crisis, the 1997 Asian Economic Crisis, the 2001 Global Recession, the 2003 Hong Kong SARS Recession, and the 2007-2009 US Mortgage Crisis and Global Financial Crisis.

These crises have provided Dr. Breeden with a rare perspective on crisis management and the analytics needs of executives for strategic decision-making. You have the opportunity to learn more about the unique insights offered through Age-Period-Cohort modeling, and many other modeling functions, with whitepapers, books, and videos from Deep Future Analytics.


Comparing CECL to FAS 5 Reserves


✓ Peer Group Comparisons of CECL Loss Reserves
✓ Comparisons of CECL to FAS5 Results
✓ Why and How to Adapt to these changes
✓ Differences in new allowance for Banks vs. Credit Unions


Are we in the Worst Point in the Credit Cycle?How Should You Respond?


  • WHY do the worst loans get booked right before the worst economic conditions?
  • WHERE are we in the credit and economic cycles?
  • WHAT immediate actions can we take to prepare?




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We look forward to seeing you at our upcoming events!

Potential Procyclicality - Panel Speaker

Dr. Joe Breeden will be discussing the current challenges emerging from FASB’s adoption of the Current Expected Credit Loss (CECL) methodology

NACUSO Networking Conference

Disney Yacht & Beach Club. April 20-23, 2020

Thank you for attending our Presentation in 2019, and we're looking forward to seeing you in 2020!

CULytics Credit Union Analytics Summit

Thank you for attending our Presentation in 2019

We're looking forward to seeing you in 2020!


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Recent Publications

All publications below were authored by DFA's own, Dr. Joseph Breeden