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Applying the CECL model to financial asset credit losses · PwC's accounting podcast

• 0:00
  SPEAKER_02: Thought leadership from PWC's national office.
• 0:06
  SPEAKER_01: Hello, and welcome to PWC's Accounting Podcast.
• 0:10
  SPEAKER_01: I'm Heather Horn.
• 0:12
  SPEAKER_01: In today's episode, we continue our series on trending topics from our loans and investment guide.
• 0:17
  SPEAKER_03: Part of the reason we're talking here is that because of the whole nature of Cecil, it's not static.
• 0:24
  SPEAKER_03: You're always refreshing different parts of your estimation process.
• 0:29
  SPEAKER_03: That naturally happens quarter to quarter.
• 0:31
  SPEAKER_03: But also when you step back from things, the economic environment has changed a lot.
• 0:36
  SPEAKER_03: Like what we were talking about upon adoption of Cecil when we were in the middle of COVID disruption is very different than what we're talking about today in the in the credit environment.
• 0:53
  SPEAKER_01: That was Brett Dougley, Deputy Chief Accountant in PWC's National Office, who's joined by Catherine Espino, also a partner in the National Office.
• 1:01
  SPEAKER_01: They've both seen it all on this topic and have lots of great insights to share.
• 1:06
  SPEAKER_01: So let's go ahead and start the episode.
• 1:09
  SPEAKER_01: Brett, Catherine, welcome back to the podcast.
• 1:12
  SPEAKER_01: And it's so nice to have both of you on for our third episode in our series on topics from the Loans and Investments Guide.
• 1:19
  SPEAKER_01: And today's topic is going to focus on current expected credit losses or Cecil.
• 1:24
  SPEAKER_01: And I think in particular, in this episode, we're going to discuss some current areas of focus and what's on the horizon.
• 1:31
  SPEAKER_01: Catherine, obviously, we introduced Cecil in our last episode and even got into a bit about the philosophy that maybe I should ask Brett the same question that we'll get into at the end.
• 1:40
  SPEAKER_01: And in addition to that brief mention, our last podcast, this has obviously been something we've talked about in many prior podcasts, but I think it's still great to go through some of the new questions and to refresh sort of on what we're seeing from that point of view.
• 1:55
  SPEAKER_01: But Brett, maybe just to kick things off, to some extent, because it has been four years, um, for public companies at least that they've adopted Cecil, you know, why do you think we're still getting questions in this area?
• 2:08
  SPEAKER_03: Yeah, four years, and I think of four long and very active years, right?
• 2:13
  SPEAKER_03: You know, I think there's a few reasons it's still top of mind.
• 2:17
  SPEAKER_03: Um one is because it has been a few years after adoption.
• 2:20
  SPEAKER_03: You know, Cecil had a long implementation period, um, a lot of effort going into it.
• 2:26
  SPEAKER_03: We've been um we've had now a couple of few years of letting it synthesize with people.
• 2:33
  SPEAKER_03: Um stakeholders are seeing the disclosures.
• 2:36
  SPEAKER_03: We've seen some economic cycles.
• 2:38
  SPEAKER_03: Um, we we adopted Cecil right at the beginning of uh COVID economic disruption.
• 2:44
  SPEAKER_03: We lived through um high inflation rates, high interest rates, and so we've had some time now to have it settle in.
• 2:53
  SPEAKER_03: Um I think a second reason is focused by the regulators.
• 2:57
  SPEAKER_03: Both the SEC and the PCAOB have had some time now to see practice develop and are starting to show uh an interest in um understanding uh practices and judgments uh being made.
• 3:10
  SPEAKER_03: And then I think the third reason is it is such an area of such significant judgment from the economic projections required for um all institutions for the economic modeling that is being done by um institutions that have more sophisticated and complex loan portfolios, uh, all of that requires a lot of data and a lot of management judgment.
• 3:34
  SPEAKER_03: And I think um, you know, whenever you have that uh wide degree of management judgment, it always is an area that that raises a lot of questions.
• 3:42
  SPEAKER_01: All right, this helpful background.
• 3:43
  SPEAKER_01: And I think it's also important to remind our listeners, and hopefully if they're listening, they know this.
• 3:48
  SPEAKER_01: But this, although it's particularly complex for companies or financial institutions to apply this, it applies to all types of companies, a wide range of them.
• 3:58
  SPEAKER_01: So maybe just to level set before we get into some of the detail, uh can you just give us a quick refresher, Brett, on the Cecil model?
• 4:06
  SPEAKER_03: Sure.
• 4:07
  SPEAKER_03: I think the model itself is fairly straightforward.
• 4:10
  SPEAKER_03: Uh you're it it the current expected credit loss model.
• 4:14
  SPEAKER_03: So it's looking ahead to estimate all future credit losses uh over the entire life of an asset.
• 4:21
  SPEAKER_03: We often use a reasonable and supportable forecast period because people ask questions, you know, I've got a 30-year mortgages, 30-year mortgage, how am I going to predict the and estimate the economic conditions over the next 30 years?
• 4:37
  SPEAKER_03: Um you do have this reasonable and supportable period concept.
• 4:40
  SPEAKER_03: Remember, prior to Cecil, we had what we called the incurred loss model.
• 4:45
  SPEAKER_03: Uh and it had been in place for so long, I think people understood it.
• 4:49
  SPEAKER_03: Um, you know, you were booking losses when they were probable and estimable, but also after a defined loss had been incurred.
• 4:58
  SPEAKER_03: We we all know that through the 2008 financial crisis, that model was criticized as delaying credit losses uh from being recognized.
• 5:07
  SPEAKER_03: And so this model was put in place in order to accelerate the recognition of all those models and or all those estimates, really accelerated them to day one when you first originate or purchase uh a financial asset that's subject to Cecil, you're booking all of those expected uh credit losses.
• 5:28
  SPEAKER_03: Remember, it's applicable, as you said, to all financial assets measured at amortized cost, both purchased and originated.
• 5:36
  SPEAKER_03: Um, you know, for banks, that may include all of the loan portfolios, but even for corporate entities, it includes their accounts receivable and other loans that they have.
• 5:45
  SPEAKER_03: So there's something in there for everyone.
• 5:47
  SPEAKER_03: It also includes certain off-balance sheet exposures and commitments too.
• 5:51
  SPEAKER_03: Um you had mentioned it was effective in 2020 and in 2023, so we've had either two or four years um to uh to live with this a little bit.
• 6:01
  SPEAKER_03: And it's already been affected by a few changes.
• 6:04
  SPEAKER_03: Um of the changes I don't think uh affect the fundamental model of Cecil.
• 6:10
  SPEAKER_03: They're more about transition and and some uh more isolated issues.
• 6:14
  SPEAKER_03: And so we do have a relatively consistent model that we've been trying to follow uh since adoption.
• 6:20
  SPEAKER_01: Aaron Powell And I think, Brett, just to clarify, just to make sure the listeners know, when you're talking about affected by changes, you're not talking about broad changes in the environment or otherwise.
• 6:29
  SPEAKER_01: You mean the model itself has been changed by ASUs adopted by the FASB.
• 6:34
  SPEAKER_03: Aaron Powell Exactly.
• 6:35
  SPEAKER_03: And I have to be careful when I talk because we talk about credit loss models.
• 6:38
  SPEAKER_03: Yeah.
• 6:38
  SPEAKER_03: And then we talk about the model of this.
• 6:40
  SPEAKER_03: So framework the framework.
• 6:42
  SPEAKER_03: Cecil itself hasn't changed much.
• 6:45
  SPEAKER_03: But part of the reason we're talking here is that because of the whole nature of Cecil, it's not static.
• 6:51
  SPEAKER_03: Uh you're always refreshing different parts of your estimation process.
• 6:56
  SPEAKER_03: Uh, that naturally happens quarter to quarter.
• 6:58
  SPEAKER_03: Uh, but also when you step back from things, the economic environment has changed a lot.
• 7:04
  SPEAKER_03: Like what we were talking about upon adoption of Cecil when we were in the middle of COVID disruption is very different than what we're talking about today in the in the credit environment.
• 7:15
  SPEAKER_03: And so as companies, as as the economic environment changes, companies need to adapt their processes, their models, and and their estimate um governance framework in some cases, in order to adapt to the current environment.
• 7:32
  SPEAKER_03: I would also say that that um just as you get experience with the model with the with CSL in general, models are consistently updated.
• 7:42
  SPEAKER_03: Um, and as regulators are going through and doing reviews and looking for consistency among the industry, you see more challenges to individual models.
• 7:52
  SPEAKER_03: You know, models are always being updated.
• 7:54
  SPEAKER_03: And so um there's they're just part of a natural cycle there um that companies are adapting to as we go along.
• 8:02
  SPEAKER_01: All right.
• 8:02
  SPEAKER_01: I think that's some helpful background, Brett.
• 8:04
  SPEAKER_01: So thank you.
• 8:05
  SPEAKER_01: As people kind of think about the the questions that we're having now.
• 8:09
  SPEAKER_01: And I guess, Catherine, from your point of view, what are some of the the focus areas for companies in this current economic environment?
• 8:18
  SPEAKER_00: Yeah, so I would I would say um a couple of things I would point out.
• 8:21
  SPEAKER_00: First is related to credit card delinquencies.
• 8:24
  SPEAKER_00: So as we've been talking about, right, Cecil became effective right at the beginning of COVID.
• 8:28
  SPEAKER_00: And what we ended up seeing is a period of time where we had historically low credit card delinquency rates.
• 8:34
  SPEAKER_00: And I think most of us attribute that to the government stimulus that was in place at the time and then consumers having that extra assistance that was provided.
• 8:43
  SPEAKER_00: But what we're seeing now over the last year or so is really an increase in consumer spending.
• 8:48
  SPEAKER_00: And we're seeing that come through from a credit card perspective in terms of higher balances and particularly higher revolving balances.
• 8:54
  SPEAKER_00: So those are the balances that borrowers are not paying off every month, and then they'll go on to the next month.
• 9:00
  SPEAKER_00: So I think this was largely expected, and we're seeing it come true now, right?
• 9:03
  SPEAKER_00: That delinquency rates would eventually return to some more normalized levels, the ones that we saw pre-pandemic.
• 9:09
  SPEAKER_00: And so we are seeing that happen now over the last year.
• 9:13
  SPEAKER_00: And maybe the second kind of current event that I would point out is in the commercial real estate area.
• 9:17
  SPEAKER_00: So, and this is specifically in the office sector, um, which we've really been focusing on for a good bit of time now.
• 9:23
  SPEAKER_00: Um, but the reason that we're talking about it is because in this post-pandemic world, we've all adopted some different working methods, which is the hybrid and remote working models.
• 9:34
  SPEAKER_00: And that's really reduced the demand for office space and has reduced the occupancy rates at these office buildings.
• 9:40
  SPEAKER_00: And that's kind of coupled with also the economic environment in which the owners of the office buildings are facing an economic environment with very high interest rates and high inflation, and that's really impacted their ability to extend or refinance their loans.
• 9:54
  SPEAKER_00: So the combination of those two things has led to an increase in charge off and delinquencies in the office sector.
• 10:00
  SPEAKER_00: So I think what we've seen now kind of through 2024 is that most lenders have gone through their office portfolios in a very detailed way.
• 10:08
  SPEAKER_00: They've gone loan by loan and tried to estimate expected credit losses.
• 10:13
  SPEAKER_00: And maybe one thing to point out too is that, and we'll talk about this a bit later, but this is one area where managements really had to use their qualitative framework to appropriately capture expected credit losses.
• 10:24
  SPEAKER_00: So an area where the models are not able to effectively estimate credit losses, because this is a quite a unique environment for this sector.
• 10:31
  SPEAKER_00: And so a lot of you know qualitative adjustments had to be made to that model output.
• 10:35
  SPEAKER_00: So I think while there still remains a lot of uncertainty and we'll see kind of how it plays out, I think absent any significant changes in the economic environment, what we'll see is that charge offs will continue to come through.
• 10:46
  SPEAKER_00: And as they do, they'll utilize the reserves that have already been built over the last year or so.
• 10:51
  SPEAKER_03: I think one of the dynamics people are looking for in this area are what will happen with this very large um portfolio of loans that are maturing just naturally in their cycle in the next few years.
• 11:05
  SPEAKER_03: Um, as Catherine said, like there's a couple of economic things going on that put that put pressure on the office space and the office portfolios, but it's also influenced by the vast number of facilities that are going to need to be renewed in a certain time period.
• 11:26
  SPEAKER_03: So I think you know, when we talk about what remains to be seen, I think you know how that plays into the dynamic uh will be interesting.
• 11:34
  SPEAKER_03: I also think that something you pointed out, Catherine, is really interesting.
• 11:38
  SPEAKER_03: And I'm not sure people are fully cognizant of it yet or or are thinking about it.
• 11:44
  SPEAKER_03: In prior economic cycles, when you have uh periods of large charge-offs, you'd it was often accompanied with large provision builds, and so you'd have decreased earnings.
• 11:55
  SPEAKER_03: And remember that at least the theory of Cecil is that if your models and your estimates were perfectly accurate, which we know they never are, but if they were, what should happen is when you have a charge-off, it it just reduces the reserve.
• 12:09
  SPEAKER_03: And so when you're in this peak period of stress, all you should be seeing is reserves decrease and no impact to the PL.
• 12:19
  SPEAKER_03: Now, in practice, we know that's never going to be true in part because a lot of dynamics, but in part because of um, you know, economic estimates and models are never perfect.
• 12:30
  SPEAKER_03: But um, how we see um this type this period of economic stress and charge-off peaks, how we see it flow through to the bottom line to the PL will be interesting to see how it continues to play out.
• 12:43
  SPEAKER_01: Yeah, I mean, definitely a lot to still be seen.
• 12:46
  SPEAKER_01: I do think one thing that's interesting about what you were both just discussing is I think in some people's minds, there's this idea that you have this model and you put some numbers in, it spits some numbers out, and then that's what you record.
• 12:59
  SPEAKER_01: And so that's fully quantitative, and all you need is this quote, perfect model.
• 13:03
  SPEAKER_01: But Catherine, you mentioned qualitative factors as well.
• 13:07
  SPEAKER_01: And I know, especially in you know, these periods where there's a lot of changes, that be can become particularly important.
• 13:14
  SPEAKER_01: And so can you explain a little bit more about how qualitative factors may be considered in this evaluation?
• 13:22
  SPEAKER_00: Yeah, definitely.
• 13:23
  SPEAKER_00: And so, I mean, when you think about qualitative reserves, it's it's a concept that's existed for a long period of time.
• 13:29
  SPEAKER_00: Pre-CESL, we used qualitative reserves in the incurred loss model as well.
• 13:32
  SPEAKER_00: But when we think about the allowance, there really are the two different components, quantitative and qualitative.
• 13:37
  SPEAKER_00: And what the quantitative component represents is the output of your model.
• 13:42
  SPEAKER_00: So it's it's what's been able to quantitatively be determined using your model and using the inputs on your macroeconomic variables like unemployment and GDP.
• 13:51
  SPEAKER_00: So it's what comes out of your model.
• 13:53
  SPEAKER_00: But as Brett said, we all know that the model output isn't always, you know, it is never and is never uh completely accurate.
• 13:59
  SPEAKER_00: So what we have to do is consider what did our model not capture in terms of the risks that are out there?
• 14:04
  SPEAKER_00: And that is captured through what we call the qualitative component.
• 14:07
  SPEAKER_00: And so oftentimes that's uh a lot of judgment that goes into the qualitative reserve.
• 14:12
  SPEAKER_00: Um, you know, maybe not as quantitative.
• 14:14
  SPEAKER_00: There may be, again, judgments and estimates that go into it, but typically what you'll see is that qualitative reserve is to capture the potential regulatory changes, the impact of like new geopolitical events, um, any unprecedented situations, which we talked about in the office sector or COVID, all of that is likely to be captured through a qualitative reserve because your models just haven't been built to deal with certain of those situations.
• 14:36
  SPEAKER_00: And so I think now, just as we enter into kind of a more normalized economic environment, you know, we may see, start to see kind of that shift between qualitative and quantitative, where we see our models being better able to capture the risks that are out there if we are in a more normalized environment.
• 14:53
  SPEAKER_00: And we'll see some of those qualitative reserves come down and shift into your quantitative component.
• 14:58
  SPEAKER_00: So again, those are the two components.
• 14:59
  SPEAKER_00: I think at the end of the day, right?
• 15:01
  SPEAKER_00: Obviously, what we're trying to do is estimate expected credit losses over the life of your asset.
• 15:05
  SPEAKER_00: And that can be captured through the quantitative or qualitative components.
• 15:08
  SPEAKER_01: All right, that's helpful.
• 15:09
  SPEAKER_01: And I think it's interesting, you know, we think about this in, and I think we reference this normalized economic environment, but I'm not sure the last time we had a quote normal economic environment.
• 15:20
  SPEAKER_01: And in particular right now, I think there's a lot of uncertainty.
• 15:22
  SPEAKER_01: There's changes in consumer spending.
• 15:24
  SPEAKER_01: We saw the rate cut, like what the impact of that is going to be.
• 15:28
  SPEAKER_01: And and so it there's a lot for companies to think about and react to.
• 15:32
  SPEAKER_01: And Brett, from a practical point of view, what are we seeing in in this time of great uncertainty?
• 15:38
  SPEAKER_03: Yeah, I I think we're seeing a range of act of reactions to it, uh, frankly.
• 15:43
  SPEAKER_03: Remember that for many companies, this modeling we're talking about may not be terribly significant.
• 15:49
  SPEAKER_03: Think if you're a company with short-term receivables that are going to be collected in over the next 60 days.
• 15:55
  SPEAKER_03: You've got creditworthy customers that you have a good relationship with and that are known well to you.
• 16:02
  SPEAKER_03: There may not be much volatility in those estimates.
• 16:05
  SPEAKER_03: And some of these qualitative aspects, and frankly, some of the economic modeling that's done may not be that significant.
• 16:12
  SPEAKER_03: But that's very different than what we see when you're dealing with long-term receivables, where you've got either more challenged borrowers from a creditworthiness perspective, or a wider range of types of borrowers.
• 16:28
  SPEAKER_03: Um and certainly when you're dealing with uncertainty in the context of financial institutions that are expected to have a high degree of rigor around and I'd say a high degree of rigor and robust modeling capabilities, I think I think that's where we're seeing probably more action and reaction.
• 16:47
  SPEAKER_03: And I think like so much in accounting, a lot of this estimation just goes back to start with data.
• 16:52
  SPEAKER_03: There's a huge focus on data, and companies and particularly financial institutions are constantly revisiting the historical data that they're using to uh as kind of inputs to build their models upon.
• 17:07
  SPEAKER_03: And so think of let reference data as just being the data that the model is trained on.
• 17:14
  SPEAKER_03: It uses that data to predict what's going to happen in the future.
• 17:18
  SPEAKER_03: And so one of the key decisions that a company needs to make is where am I going to get this data and over what period?
• 17:25
  SPEAKER_03: And generally, this data set covers at least an economic cycle.
• 17:29
  SPEAKER_03: And so as we're sitting here now four years after the initial adoption of Cecil, many companies are discussing what this backwards looking at data should be used.
• 17:40
  SPEAKER_03: Like, what should I do with all of this lost data that comes from the COVID era, for example?
• 17:47
  SPEAKER_03: It was a fairly unusual economic environment.
• 17:50
  SPEAKER_03: Uh Catherine pointed out the large degree of government stimulus.
• 17:54
  SPEAKER_03: Should I continue to assume that that type of government stimulus?
• 17:58
  SPEAKER_03: If I want to strip it out, how do I strip it out?
• 18:01
  SPEAKER_03: And so companies are thinking about the best way to use that data, which is now a decent chunk of the most at least the recent history, and how how that should be included in their historical data and their this reference set of data that we're talking about.
• 18:18
  SPEAKER_03: And I think there's no one clear and right answer to it.
• 18:21
  SPEAKER_03: It's often going to be dependent on what your model looks like, what the product types are, and also what that economic environment looked like and what the your loss data shows compared to pre-pandemic uh information, et cetera.
• 18:36
  SPEAKER_01: Yeah.
• 18:37
  SPEAKER_01: And Brett, before I go on, I guess one of the things I was just reflecting is this also there is some, I'll call it industry component to it, because as you were talking about short-term receivables and 60 days, you know, your customers in my head, and frequent listeners know that I was in the power and utilities industry.
• 18:54
  SPEAKER_01: That is not necessarily the case because they're selling to pretty much everyone.
• 18:59
  SPEAKER_01: And so, you know, maybe some of these economic factors would have a much bigger impact on them versus someone who maybe only has one customer.
• 19:07
  SPEAKER_01: Or and so, or, you know, different types of industries may have different impact and different ways they think about this data.
• 19:13
  SPEAKER_01: So it's just another factor to think about, where maybe some information from your industry groups or otherwise actually could be helpful in understanding how others are thinking about things.
• 19:24
  SPEAKER_03: I think that's right.
• 19:25
  SPEAKER_03: I think that's right.
• 19:26
  SPEAKER_03: This may be a good segue into another factor that people, I think companies are looking at, which is portfolio segmentation.
• 19:34
  SPEAKER_03: Based on backtesting over the last several years, you may find that you have portfolios or groups of customers that you thought would behave in a homogeneous way, but are not.
• 19:47
  SPEAKER_03: And so segmenting the portfolio may help management come to a more precise or relevant estimate of credit losses.
• 19:55
  SPEAKER_03: This may be based on loan product, it may be based on geography, customer credit scores.
• 20:02
  SPEAKER_03: And the obviously the purpose of all that segmentation, both in the portfolio as well as macroeconomic variables, is to think more precisely around what is driving credit losses and how do I do that.
• 20:18
  SPEAKER_03: So as management is able to gather more data and evaluate that data, we are seeing companies make changes to how they thought of their portfolio segmentation in that modeling.
• 20:30
  SPEAKER_03: The third and last area I would point out is the macroeconomic variables themselves.
• 20:35
  SPEAKER_03: So one of the most significant changes we had with Cecil was this use of a reasonable and supportable economic forecast to predict lifetime losses.
• 20:44
  SPEAKER_03: You know, companies were tasked with coming up with a view of what the economy and what the economic factors were going to look like.
• 20:52
  SPEAKER_03: And so during implementation, companies made all sorts of decisions regarding what macroeconomic variables they were going to focus on for their models, you know, what variables were most impactful or correlated to credit losses, whether that is unemployment rates or interest rates or a host of number of factors.
• 21:14
  SPEAKER_03: And then once you have the forecast, uh many companies use different scenarios to and then probability weight those scenarios so they're not focused on just one scenario.
• 21:26
  SPEAKER_03: So how do I think of these different forecasts I've come up with?
• 21:30
  SPEAKER_03: Maybe it's three scenarios, maybe it's five or more.
• 21:33
  SPEAKER_03: And how do I weight those to come up with my final number?
• 21:38
  SPEAKER_03: And so what we're seeing is some refinement of these MEVs and the weighting uh processes that companies are going through, uh, largely based on experience and backtesting.
• 21:50
  SPEAKER_03: So one simple example maybe models in the past were trained on using national unemployment rates.
• 21:57
  SPEAKER_03: And uh a company may find that use State level unemployment gives a better indicator of credit losses.
• 22:06
  SPEAKER_03: And so implementing that into their models on a prospective basis may give them a better estimate of credit losses.
• 22:14
  SPEAKER_03: That may not be a significant change when they first make it, but it's more of a refinement to their process.
• 22:20
  SPEAKER_01: Yeah.
• 22:20
  SPEAKER_01: And I guess before we talk about the model itself in terms of the actual quantitative model being used to create the numbers, I am curious, Brett, because your point about using US-wide versus state level or otherwise refining, those pools all use that word.
• 22:38
  SPEAKER_01: Are you seeing a lot of companies continuing to refine, or is it more based on how their experience is comparing to their model outputs, or some combination of both of those?
• 22:49
  SPEAKER_03: I think some combination of them.
• 22:52
  SPEAKER_03: I mean, I don't think a lot of companies are just eager to like be in a constant state of change, right?
• 22:57
  SPEAKER_03: Right.
• 22:58
  SPEAKER_03: But um especially more sophisticated financial institutions are constantly doing backtesting of their models and realizing finding ways that their models could be more accurate.
• 23:07
  SPEAKER_03: They're doing that not just for accounting purposes, right?
• 23:10
  SPEAKER_03: They're doing it for actual credit decisions.
• 23:12
  SPEAKER_03: And so this whole model is trying to align what the data and everything you're doing from an actual credit and business perspective to what the accounting is.
• 23:23
  SPEAKER_03: And so I think that that ongoing backtesting is a constant process for many companies.
• 23:30
  SPEAKER_01: And I guess, Catherine, you know, if we think about um Brett mentioned backtesting, which is a form of monitoring, but obviously actually understanding the models, monitoring the models, making sure you have appropriate governance over the models is critical to this whole process.
• 23:46
  SPEAKER_01: So what are some of the things that we're doing or what that we're seeing companies do to do this monitoring?
• 23:52
  SPEAKER_00: Yeah.
• 23:52
  SPEAKER_00: So I think it's largely a continuation, probably, of what they've been doing, even pre-Cecal and into Cecil.
• 23:59
  SPEAKER_00: And as Brett mentioned, this is not only for purposes of the Cecil estimate, but as they look across any models that are used, companies need to make sure that they have policies and procedures in place to monitor the performance of the models, whether that's through kind of continuous back testing or through and or through periodic revalidations of the model where it would be kind of reviewed in more in a more granular way.
• 24:22
  SPEAKER_00: That definitely needs to be in place and it needs to be a periodic assessment.
• 24:26
  SPEAKER_00: And I think then coming out of those assessments, you have you have data that you need to analyze and evaluate and see to really see how your model's been performing.
• 24:34
  SPEAKER_00: Um and then are there any adjustments that need to be made based on what you're seeing come through?
• 24:38
  SPEAKER_00: So Brett mentioned some certain, there may be certain enhancements.
• 24:41
  SPEAKER_00: They may be, you know, not very significant, but a change to the level of MEV that you're looking at.
• 24:46
  SPEAKER_00: Um or what you may see is that there's a risk that's not being captured in your model, like we talked about, that may need to be accounted for through those uh qualitative reserves.
• 24:55
  SPEAKER_00: So I think it's an, you mean one, important that this is a periodic assessment.
• 24:58
  SPEAKER_00: I think two that the evaluation is done, and then any actions that are taken coming out of evaluating those results should be documented.
• 25:07
  SPEAKER_00: And so that's what did you consider and what decision did you make?
• 25:10
  SPEAKER_00: If you made a decision to enhance the model, make sure that's well documented and supported by the data.
• 25:15
  SPEAKER_00: And equally important, if you've reviewed it and decided that no changes need to be made, document that decision as well and the supporting information there.
• 25:23
  SPEAKER_00: And then I would say, kind of in addition to just the model governance and performance monitoring, you know, the regulators have continued to enforce the importance of documentation over the whole allowance for credit losses methodology, right?
• 25:36
  SPEAKER_00: And that was initially established in SAB 102, and this is back in the occurred loss model, and was uh updated with SAB 119 when the Cecil model came out.
• 25:45
  SPEAKER_00: Um, and that guidance should be followed by all public companies.
• 25:49
  SPEAKER_00: Um, and and it does go through all of the documentation requirements in terms of policies and methodologies and key judgments that management is making.
• 25:57
  SPEAKER_00: And then maybe finally, I just I guess reiterate and continue to focus on that the right key controls need to be implemented in your process, right?
• 26:04
  SPEAKER_00: There's a lot that goes into the modeling of credit losses.
• 26:08
  SPEAKER_00: Um, and maybe one point that that we've heard a lot about recently is just key controls around the completeness and accuracy of data.
• 26:13
  SPEAKER_00: And Brett talked about the data that's used in the model.
• 26:16
  SPEAKER_00: There's data underlying a lot of your qualitative reserves, there's data underlying the governance materials that management is using to make key decisions off of.
• 26:24
  SPEAKER_00: There really should be key controls in place to ensure the completeness and accuracy of all of that data.
• 26:29
  SPEAKER_01: So, Catherine, you mentioned uh regulator oversight.
• 26:32
  SPEAKER_01: And I think one of our most popular topics always on this podcast is what's going on with the SEC and their views.
• 26:39
  SPEAKER_01: And so just curious, Brett, what we are seeing coming from the SEC in terms of comments or otherwise.
• 26:45
  SPEAKER_03: Yeah.
• 26:45
  SPEAKER_03: You know, I think as expected for a while, given all of the activity around the adoption of CSOL and the new disclosures that were being made, and uh analysts were getting used to the new disclosures, companies were doing peer comparisons and finding out what was useful to stakeholders.
• 27:04
  SPEAKER_03: Um, I think the SEC for a bit kind of stood back a little bit and to see how practice and disclosures were developed.
• 27:13
  SPEAKER_03: And more recently, we're seeing activity, more activity, I think, from the regulators, including the SEC.
• 27:19
  SPEAKER_03: Uh we recently published an article that's available uh to our listeners describing these SEC comment letter trends, where we focused on what was going on with Cecil and loan disclosures as well as allowance disclosures.
• 27:34
  SPEAKER_03: Um I think they we pointed out four maybe general trends that we're seeing in comments.
• 27:39
  SPEAKER_03: One, the first was around disclosures regarding the qualitative factors that Catherine was talking about and how they affect the loan portfolio as and how they those qualitative factors influence the amount of the allowance for loan losses.
• 27:54
  SPEAKER_03: I think the SEC is very interested, and stakeholders, you know, analysts are very interested in the degree of adjustments that are that these qualitative reserves mean and how management is thinking about them and how they evolve over time.
• 28:08
  SPEAKER_03: I think uh the second area of disclosures or of comments that we've seen relate to specific loan portfolios and the characteristics and risks of those portfolios.
• 28:19
  SPEAKER_03: You know, think commercial real estate like Catherine was talking about, um, understanding how how what what the dynamics are and what the what the loan characteristics are, and then how those are translating into reserve changes.
• 28:34
  SPEAKER_03: I think the SEC is particularly interested in seeing that connection between these portfolios that they're hearing a lot about in the press and that that financial institutions are talking about, and what that actually means to a quantified reserve.
• 28:49
  SPEAKER_03: Um I think the third area is where there is a significant change in allowance.
• 28:56
  SPEAKER_03: Uh I think the SEC staff is asking for more information around what's driving that change, um, especially where that change may be inconsistent with some key credit metric that is being shown, whether it's charge-off rates or delinquency rates, understanding and making sure that companies are being transparent and complete in their disclosures about what's driving those changes in the allowance.
• 29:18
  SPEAKER_03: And then the last area that we've seen some interest in is just disclosure of charge-off policies.
• 29:24
  SPEAKER_03: Remember, this is an area in the standard itself doesn't prescribe when a loan balance should be charged off and use up that reserve that you had previously established.
• 29:35
  SPEAKER_03: In the banking industry, there's kind of well-regarded or well-understood regulatory guidance, and most companies just follow that, those same regulatory charge-off standards, but those don't exist in every industry.
• 29:47
  SPEAKER_03: And so in some cases, um, the SEC is just looking for information around what companies are doing uh with with respect to establishing policies for the timing of those charge-offs and then how that would influence the reserve.
• 30:01
  SPEAKER_01: Yeah, so Brett, it's interesting because it's particularly about requesting additional disclosure for significant quantitative or I guess qualitative changes, because we were just talking through this whole podcast about the number of potential quantitative changes or qualitative changes companies could have made to their reserves over the past four years.
• 30:21
  SPEAKER_01: So it sounds like the key takeaway from these SEC comments is transparent disclosure of what you're doing is critically important.
• 30:29
  SPEAKER_03: I think that's right.
• 30:30
  SPEAKER_01: Yeah, very interesting.
• 30:31
  SPEAKER_01: So then with all of that said, the sort of current state, but I know the FASBE is monitoring and reacting to comments on this and um comments from stakeholders.
• 30:41
  SPEAKER_01: So, Catherine, what are we seeing coming from the FASBE?
• 30:44
  SPEAKER_00: Yeah, so uh the FASBE, as they do with any kind of significant standard that's been issued, uh, did a post-implementation review for Cecil uh just to really understand what's working well or where if there's areas that need attention or that you know may need some related standard setting.
• 30:59
  SPEAKER_00: Um, and as they did that for Cecil, there's one issue that analysts continue to raise, and that is that within the Cecil model, there's two accounting models for purchased assets.
• 31:08
  SPEAKER_00: And that's you either have a purchased credit deteriorated asset or you have a non-purchased credit deteriorated asset.
• 31:15
  SPEAKER_00: And with the two models, the analysts pointed out that you know it's it reduces the comparability between the um the companies, and it also you know creates unnecessary complexity.
• 31:25
  SPEAKER_00: And maybe just as what, you know, going back to what Brett said, just in terms of the overview of the Cecil model, but the Cecil model creates a day one loss, right?
• 31:33
  SPEAKER_00: That's the intention for loans that are originated or purchased, and that day one loss goes through your PL.
• 31:39
  SPEAKER_00: And that's intended to capture all future expected credit losses.
• 31:42
  SPEAKER_00: But the exception to that is for purchased credit deteriorated assets, the allowance uh is recognized on day one with an offsetting gross-up to the asset.
• 31:50
  SPEAKER_00: So there's no impact to your earnings or PL on day one.
• 31:54
  SPEAKER_00: So obviously that can get fairly confusing under the two models.
• 31:57
  SPEAKER_00: So the FASB has taken on a project to try to fix that problem.
• 32:02
  SPEAKER_00: And what they where they currently are is the uh looking at an expansion of that gross-up model that we see for PCD assets currently.
• 32:09
  SPEAKER_01: So, Brett, I'm I'm curious to the status of the project and you know when we can expect to see a final answer.
• 32:15
  SPEAKER_01: But before you get to that, I'm actually very curious why the FASB went the direction of saying that they were going to use this, I'll use the word gross-up model for all purchased um financial assets rather than I'll use say the normal Cecil model with this day one loss.
• 32:32
  SPEAKER_03: Yeah.
• 32:33
  SPEAKER_03: You're right.
• 32:34
  SPEAKER_03: There's a there's always a couple of ways to to fix a problem that that exists in practice.
• 32:40
  SPEAKER_03: Remember that I I think in many regards, this day one loss concept, some stakeholders criticized as not very conceptual.
• 32:49
  SPEAKER_03: Yes, right.
• 32:50
  SPEAKER_03: And so uh in in general.
• 32:52
  SPEAKER_03: And the reason they came up with the model they did to eliminate the day one losses for what we call PCD assets was think if you were buying a portfolio of loans and you paid 50 cents on the dollar because they they were they were very credit deteriorated.
• 33:10
  SPEAKER_03: You know, under under the Cecil model, like you built in, well, economically, you built in those credit losses into the price you were willing to pay.
• 33:20
  SPEAKER_03: And so if you had a day one loss for that portfolio, you'd record a 50 cent day one loss on the portfolio.
• 33:28
  SPEAKER_03: Like you didn't pay for it.
• 33:30
  SPEAKER_03: So that now that same concept, admittedly, is true in every originated asset.
• 33:36
  SPEAKER_03: When I originate a loan at par, the reason there's a credit spread in the interest rate is because I expect there to be some credit risk there, and I'm not always going to get it.
• 33:46
  SPEAKER_03: But it's a matter of degree.
• 33:47
  SPEAKER_03: And I think originally when when Cecil was was um was conceived, they had this vision that at some point for for these purchased, there's some class of purchased assets where taking the day one loss just doesn't make sense and we can come up with a different model, this gross-up model to to address that.
• 34:09
  SPEAKER_01: And I guess Brett, maybe I have a clarifying question that I maybe should have picked up.
• 34:13
  SPEAKER_01: When you're talking about this gross up, I guess in my mind, with your example of the you paid 50 cents on the dollar, I was thinking the asset you're recording is the 50 cents on the dollar.
• 34:23
  SPEAKER_01: But what you're saying is actually you would be recording it at 100 and then reducing it to 50 cents.
• 34:30
  SPEAKER_03: My 50 cent example is probably too extreme to really illustrate.
• 34:35
  SPEAKER_03: But let's say, let's say I bought it at 90 cents on the dollar.
• 34:38
  SPEAKER_03: What the current model would do, you book that asset for 90 cents, but you still need to put a Cecil reserve.
• 34:45
  SPEAKER_01: And so then you'd write it down even further.
• 34:46
  SPEAKER_03: You'd write it down even further.
• 34:48
  SPEAKER_03: So the FASBY is working through um this project, and they issued an exposure draft last June and June of 2023 that would expand the use of this gross-up model to all purchased financial assets.
• 35:04
  SPEAKER_03: All is with an asterisk.
• 35:05
  SPEAKER_03: There are some that are excluded, I'll get into that in a minute.
• 35:08
  SPEAKER_03: But they call it the PFA purchased financial asset model.
• 35:12
  SPEAKER_03: And so what you would do under this model is all acquired financial assets, like if you acquire them in a business combination and even assets that are seasoned that are acquired in an asset purchase, would be subject to this gross-up model.
• 35:28
  SPEAKER_03: You wouldn't have a day one loss associated with it.
• 35:30
  SPEAKER_03: Keep in mind that AFS securities wouldn't be subject to this PFA model.
• 35:35
  SPEAKER_03: And what this would do was eliminate this distinction that Catherine talked about about a purchased financial asset from a credit-deteor asset.
• 35:45
  SPEAKER_03: It it moves that line and just says all purchased assets except for stuff that what hasn't been seasoned.
• 35:52
  SPEAKER_03: So if you originate a loan and sell it to me a day later, like that doesn't count.
• 35:58
  SPEAKER_03: It moves the line from making this decision, this very judgmental decision about what does it mean to have significant credit deterioration and just says, I'm gonna apply this to all purchased financial assets, which eliminates this day one, you know, the day one credit loss that many stakeholders had a problem with.
• 36:15
  SPEAKER_01: And I guess Brett, the reasoning for that would be the fact that this credit loss should already be built into the purchase price.
• 36:23
  SPEAKER_01: You wouldn't have paid what you did if you thought there was going to be even more losses.
• 36:27
  SPEAKER_03: Exactly.
• 36:28
  SPEAKER_03: All right.
• 36:28
  SPEAKER_03: Exactly.
• 36:29
  SPEAKER_01: That's very helpful.
• 36:30
  SPEAKER_01: So what are we thinking then from a timeline point of view?
• 36:33
  SPEAKER_01: We don't know.
• 36:34
  SPEAKER_03: So um they're still evaluating feedback.
• 36:36
  SPEAKER_03: I I'd characterize the feedback they got as fairly mixed.
• 36:39
  SPEAKER_03: Um as you can imagine, many stakeholders are in favor of the proposal because it eliminates, it doesn't eliminate, it reduces the extent of this day one loss.
• 36:52
  SPEAKER_03: Other other uh stakeholders were not in favor of it because they think that it's too soon after the adoption of a major standard to make such a fundamental change.
• 37:03
  SPEAKER_03: And they would also observe you didn't they didn't uh completely eliminate this issue around day one credit losses.
• 37:11
  SPEAKER_03: They just moved the line from a judgmental line to a originated versus purchased line.
• 37:16
  SPEAKER_03: Many institutions would claim, would note that when they originate a financial asset, they also bake in those credit losses there.
• 37:24
  SPEAKER_03: So conceptually, the difference between purchased and an originated asset is is, you know, we're all struggling with that a little bit.
• 37:33
  SPEAKER_03: Um so I think it remains to be seen how the FASBE is going to proceed with this project.
• 37:38
  SPEAKER_01: Very interesting.
• 37:38
  SPEAKER_01: So, Catherine, anything else that we're seeing from the FASBE?
• 37:41
  SPEAKER_00: Yeah, the only other thing I would mention is that the FASBE's private company council or PCC, um, they're taking on a project relating to receivables that are originated under ASC 606.
• 37:53
  SPEAKER_00: So that's your normal kind of trade receivables that are recorded when you sell, make a sale to a customer and that are accounted for under the revenue guidance.
• 38:02
  SPEAKER_00: So the reason the PCC is considering this is that what they've seen is that for short-term trade receivables, the the Cecil model may be kind of overly complex or doesn't really provide any decision useful information to financial statement users.
• 38:16
  SPEAKER_00: So the allowance you would come up under the Cecil model, because these are short-term and not very reactive to macroeconomic variables, may be very similar to the loss estimate that you would come up with just using your historical data.
• 38:30
  SPEAKER_00: So the PCC is considering how they can kind of lessen the burden of determining the allowance for certain short-term trade receivables.
• 38:38
  SPEAKER_00: Um, but I think that's very early stages.
• 38:40
  SPEAKER_00: I think there's a lot more to come and to see where they go with that.
• 38:43
  SPEAKER_01: All right.
• 38:43
  SPEAKER_01: It's very helpful.
• 38:44
  SPEAKER_01: So we're getting close to the end, but Brett, before we wrap things up, I'm gonna ask you the philosophical question I asked, or maybe not philosophical, but broad question I asked Chip and Catherine when we talked about Cecil in our last podcast.
• 38:57
  SPEAKER_01: And Chip had run through all the things that have happened since we adopted Cecil.
• 39:03
  SPEAKER_01: So you had the pandemic, you had then all the government stimulus, you had high inflation, now we have rate cuts.
• 39:10
  SPEAKER_01: Like there's all these different things that have happened.
• 39:12
  SPEAKER_01: And, you know, at the time Cecil was adopted, everyone kept saying, well, a pandemic wasn't considered when you know these models were developed.
• 39:20
  SPEAKER_01: And a pandemic wasn't really in the FASB's mind when, you know, Cecil, the framework.
• 39:25
  SPEAKER_01: And so I'm just curious, and you can give your own point of view, it doesn't have to be PWCs, if you think that the overall Cecil framework has shown the flexibility to sort of absorb everything that's going on and um you know to be to be providing decision useful information.
• 39:43
  SPEAKER_03: Yeah.
• 39:44
  SPEAKER_03: I think the debates we've seen post-adoption have um have just highlighted the fact that there are a lot of different stakeholders with different points of view about what is decision-useful information, right?
• 39:58
  SPEAKER_03: Going back to all the fact that you know some stakeholders want just want to see fair value information for financial instruments.
• 40:05
  SPEAKER_03: Um I do think that what one thing we learned through the last four years is that it is possible for analysts to understand uh from disclosures how companies are making judgments.
• 40:19
  SPEAKER_03: We saw companies need to make very significant judgments around their estimated losses.
• 40:25
  SPEAKER_03: We saw them needed to change those estimates quarter after quarter as they incorporated new information.
• 40:32
  SPEAKER_03: We saw analysts be able to absorb that information.
• 40:35
  SPEAKER_03: We saw some analysts like using other third-party data to make other estimates and comparing estimates.
• 40:42
  SPEAKER_03: And so all of that seemed to be sort of a information-rich dialogue around getting at some of this information.
• 40:52
  SPEAKER_03: Uh the fact remains is that the estimate of credit losses is very judgmental.
• 40:59
  SPEAKER_03: And um, you know, different institutions, even two different people within an institution can have two different but both reasonable views of those credit losses.
• 41:09
  SPEAKER_03: And I think a lot about this project is does our companies able to give enough information and granular enough information to help users understand how they made the judgments and then be able to do what they want with that information.
• 41:26
  SPEAKER_03: And I think so far we're seeing that analysts are able to at least glean something.
• 41:31
  SPEAKER_03: That there's always improvements that can be made.
• 41:34
  SPEAKER_03: And you know, I think analysts are still learning how to use this information and how to how to um manipulate or or interpret it.
• 41:42
  SPEAKER_03: Um but I think in in general that while there's no perfect solution to to this finished reporting issue, I think I think the information is is shown to be useful so far.
• 41:54
  SPEAKER_01: All right.
• 41:55
  SPEAKER_01: That's very helpful perspective.
• 41:56
  SPEAKER_01: And I think a perfect lead-in to my final question, which you guys know will be asking what advice you have for companies in preparing their Cecil estimates or anything around it.
• 42:07
  SPEAKER_01: So, Catherine, I'll go to you first.
• 42:09
  SPEAKER_00: Yeah, I think um I think I would just reiterate, you know, one of the points we talked about around governance.
• 42:14
  SPEAKER_00: Um, you know, we've talked about all the significant judgments that are made throughout this process, whether it be, you know, how you're modeling or what qualitative reserves you're using, and you know, and how you're making judgments related to those reserves.
• 42:25
  SPEAKER_00: So I think it's just reiterating the importance of governance around the whole process, right?
• 42:30
  SPEAKER_00: Your models, your data, your qualitative reserves, uh just you know, that it's important to have the right controls and governance in place.
• 42:38
  SPEAKER_01: Very helpful.
• 42:38
  SPEAKER_03: And Brett, from your point of view, I would say uh you know, give a lot of thought to how you are aligning and incorporating all of this information you have around your institution and how it relates to the reserve.
• 42:52
  SPEAKER_03: When you go through analyst calls, credit reports, uh portfolio reviews, um, understanding what the pressure points are to each loan portfolio and where the risks are and what you're talking to investors about, and asking the question, how did I think about that risk in my allowance?
• 43:12
  SPEAKER_03: And you know, to Catherine's point, how did I document all of that?
• 43:15
  SPEAKER_03: Um, and how did I disclose all of that?
• 43:18
  SPEAKER_03: And making sure all of that's tied together so you have a really cohesive um explanation of how credit risk is being translated into this you know one very significant judgmental number.
• 43:32
  SPEAKER_01: All right.
• 43:33
  SPEAKER_01: Well, definitely excellent advice.
• 43:34
  SPEAKER_01: And you both have given listeners a lot to think about in today's podcast.
• 43:38
  SPEAKER_01: So thanks so much for joining me today.
• 43:40
  SPEAKER_03: Thanks.
• 43:42
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