Santa Fe, NM | (714) 794-5195 | firstname.lastname@example.org
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 www.mncun.org.
Charles Hoy, Director of Business Development
Deep Future Analytics LLC
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
Comparing CECL to FAS 5 Results
Credit Unions can expect at least a 22% increase and Community Banks a 59% decrease in reserves as a result of CECL?
Deep Future Analytics (DFA) and Prescient Models (PM) recently conducted a joint study across 103 CECL clients to determine how much their loss reserves could change if CECL were adopted today. The results were presented in a 10/17 webinar which we summarize in this article.
Since loan modeling is such a complex animal, the first question you should always ask is: “Can I trust the numbers?” For this, Dr. Joe Breeden, world renowned credit analyst and author of the “Living with CECL” book series, takes you through how advanced loan modeling works and the data and equations behind it. I provided a “stripped down” version of this below for those less familiar with loan modeling.
For the study we used a MultiHorizon Survival Model. This highly advanced method of computing credit risk is actually a two-step approach which adds to overall accuracy and application of the data. When you take multiple factors like Lifecycle, Environmental, and Credit Quality the information must be handled correctly or the results will be erratic. Therefore, this approach accounts for the changes in variable significance across multiple horizons. For example, delinquency is a very strong indicator of PD in the early months but then becomes less important. All in all, as we look at lifetime losses it is important to have a model that will stand the test as we look multiple years into the future.
Overall CECL vs. FAS5 Results
We took a look at the ratio of the output of the MultiHorizon Survival Model to what is currently being reserved under FAS 5. Among community banks, the most common result (median) was a significant reduction of -59% in reserves. For credit unions the model reveals an increase in reserves of 22%. These estimates are before any Q-Factor adjustments. While the banks may be able to add Q-Factors to bring the new requirements more in line with existing practices, the credit unions will have a much more difficult time “selling” a negative Q-Factor to their auditors.
Next, we took the client data and further stress tested using the FRB Severe scenario for a near-term recession. As a result the CECL loss reserves see a significant increase with the banks going from a -59% up to +69%, and credit unions from +22% up to +107%.
To understand the differences by product, we are taking our model’s 12 month horizon and comparing to a lifetime horizon. This will measure the number of years of coverage you need under CECL, compared to what you would’ve had before.
The median ratio of CECL / 12Mo. loss rate for the individual products is essentially the average life of the loan. For example, we computed the average life of Consumer Loans to be 1.59, thus resulting in a 59% increase in reserves of the current 12 month horizon. For Auto loans the result was an increase of 135%, 55% for Consumer Line, 78% for Credit card, 140% for Residential RE, and 342% for HELOC. The HELOC is sort of an interesting case because it cannot be canceled. This poses many issues in how we manage loss estimates and this actual estimate could actually be understated.
On the commercial side, we show increases for all products as well: Ag Loans (197%), Ag Lines (2%), C&I Loans (93%), C&I Lines (140%), CRE Loans(251%), and CRE Lines(310%)
There are still plenty of institutions today that have yet to prepare for the new CECL standards. Based on these results we feel the time is now to find out your institution’s new reserve requirements so you can make the appropriate course corrections today to ensure a smooth transition tomorrow.
Which CECL model should we use? 23 Jul 2018
If you're a top 20 bank, this will almost certainly be a modified version of your CCAR model. For everyone else, we tested the alternatives.
This is an expansion of our previously released studies. In a sincere effort to assist small lenders in implementing CECL, several "spreadsheet methods" have been proposed.
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?
We look forward to seeing you at our upcoming events!
Dr. Joe Breeden will be discussing the current challenges emerging from FASB’s adoption of the Current Expected Credit Loss (CECL) methodology
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!
Thank you for attending our Presentation in 2019
We're looking forward to seeing you in 2020!
All publications below were authored by DFA's own, Dr. Joseph Breeden
CECL (Current Expected Credit Loss) is the new accounting standard for estimating loss reserves on loan portfolios. The CECL guidance provides a great amount of flexibility in which models are used and a range of other choices that may impact the calculations. This book provides details of a study on how to apply CECL to US mortgage data. It seeks to disclose as many modeling details, results, and validation tests as possible so as to provide a reference for comparison and best practices. Because CECL is so similar to IFRS 9 Stage 2, this can also serve as a benchmark for implementing the new international account standards. The book is organized into three parts. Part I: Study Summary provides an overview of CECL, the design of the mortgage study, and the key comparative results across the models tested. Part II: Model Details provides in-depth discussions of how the models were designed and estimated, the coefficients, and the validation. Part III: Background provides additional conceptual material. Chapters 11 and 12 may be particularly useful to those new to modeling, and Chapter 13 puts CECL modeling in the context of lending analytics overall.
Building on the solid foundation of the previous bestselling first impression, this extended updated impression walks through the various issues of retail lending and develops approaches to address the interaction between economic cycles and retail lending. The complexity of time is extensively explored: vintages, current time and maturity. Reinventing Retail Lending Analytics, Second Impression covers complex issues such as scenario based forecasting, stress testing, volatility analysis, economic capital and portfolio optimisation, credit scoring and last, but not least, model risk.
The book ends by providing examples of the application of nonlinear decomposition. These examples will provide you with rich data sets for exploring portfolio dynamics and improving portfolio management using nonlinear decomposition techniques.
The new loan loss accounting rules for CECL and IFRS 9 require thousands of organizations to learn about modeling. Likewise, accountants and others in finance are now required to learn about statistical modeling concepts. This book is intended to define terms in a manner consistent with decades of academic literature on statistical modeling and hopefully reduce some of the noise and confusion just around definition of terms. It may also serve as a useful guide to analysts new to the field tasked with IFRS 9 compliance, the international loss accounting rules, and credit risk modeling in general.
Each chapter of this book is a term that one might encounter when discussing creating lifetime loss forecasting models for CECL or IFRS 9. Not every term is a model, and some models listed are being mentioned only to explain why they are not likely to be used for loss forecasting. The CECL guidelines and subsequent FAQs have given examples of modeling techniques. Some people new to loss forecasting have assumed that those are all the available or applicable methods. This book is meant in
part to dispel that misconception.
The definitions and descriptions provided here are meant to provide an intuitive understanding across a range of modeling techniques. Mathematical derivations are kept to a minimum. The references listed will provide all the necessary details for an eager analyst.