Massive Mass Tort Settlements and Liability Forecasting with Mark Eveland and Ed Silverman

Today we talk about liability forecasting and the role it plays in the administration of massive, sometimes multi-billion-dollar mass tort settlement trusts. These mechanisms were built to fairly and judiciously compensate current and future claimants for their injuries. 

Mass tort litigation is a complicated beast as is the administration of these trusts. There are many overlapping, interlocking, intersecting, and dynamic layers involved with people, companies, diseases, certain financial externalities, and "black swans" that complicate the oversight of settlement funds. 

Listen to my conversation with Mark Eveland and Ed Silverman, both with Verus LLC, which provides litigation support services to law firms working on mass torts, such as case management and medical review services, settlement administration, business and advisory services, and analytics. They explain that liability forecasting is a practice best understood through the three areas it analyzes: (i) the risks and injuries created by a product and its use, (ii) claims filed and approved, and (iii) finances. Liability forecasting is both a science and an art, they say, with plenty of risks.

Eveland, founder, CSO, and chairman of the board, is an expert in building settlement and claims management programs for mass torts, class actions, and insurance runoffs. Throughout his career, Mark has provided research, discovery, analytics, settlement administration, and expert witness support litigators around the country.

Trained in molecular biology, genetics and epigenetics, and biochemistry, Silverman is an analytics executive, a life science expert, and biomedical communications specialist. Ed assists with data analytics, scientific collaboration networks, patient based medical claims, and more.

I hope you enjoy the episode!

Bonus: I left in a little introductory jazz in the beginning, then  encouraged Ed to discuss his background and research, which I was thrilled to find included how fruit flies (Drosophila melanogaster) form memories. I can't tell you how glad I was I asked. Yes I can. Very glad. 

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This podcast is the audio companion to the Journal of Emerging Issues in Litigation. The Journal is a collaborative project between HB Litigation Conferences and the vLex Fastcase legal research family, which includes Full Court Press, Law Street Media, and Docket Alarm.

If you have comments, ideas, or wish to participate, please drop me a note at Editor@LitigationConferences.com.

Tom Hagy
Litigation Enthusiast and
Host of the Emerging Litigation Podcast
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Transcript

Tom: 0:05

Welcome to the Emerging Litigation Podcast, a co-production of HB Litigation and critical legal content, custom content for law firms and litigation service providers, and the newly formed VLAC's Fast Case, your World of Legal Intelligence and our friends at LawStreetMedia. I'm your host, tom Hagee, litigation Content Producer and enthusiast and an average bongo player. Contact me if you have an idea for an episode. In addition to often being polite, I'm always looking for new twists on the law, whether it's a new regulation, legislation or an important new opinion, or it could be a development in the world that will test existing law or anything you're dying to share with other litigators, organizations or individuals. And, if you like what you hear, give us a rating. That always helps. And now here's today's episode.

Tom: 1:00

Today we're going to talk about liability forecasting and the role it plays in the administration of massive mass tort settlements. They are a product of the Asbestos Wars, where there was a creation of a massive I'm going to say that word a lot settlement trust. I think it was like $30 billion at one time. That's a lot of billions, and these are employed, of course, to compensate current and future claimants for injuries in mass tort litigation. Mass tort is a complicated beast exposures and injuries and the discovery of injuries. All these things can take place over decades and in the meantime, technology changes, medicine changes, companies come and go, acquisitions are made, people are exposed to new elements in the environment and in products and in the workplace. And then, on the financial side, I'm going to say it again, these massive funds can be affected by market fluctuations beyond what's going on with the population. Probably the biggest monkey wrench in recent times that no one really had on their radar was the COVID pandemic. Of course, that offered another confounding factor when many Americans suddenly had virus-related respiratory issues, perhaps joining injuries. They may have suffered or were quietly developing from some other toxic exposure.

Tom: 2:24

It's not a simple matter of divvying up a pot of money to an established group of people. Here's your $8 billion and here's your 8,000 people. So carve it up. No, it's not that easy. I think I saw a monkey do that on TikTok. But there are overlapping, intersecting, dynamic layers involved with people, companies, diseases, certain financial externalities, as one of my guests said, and black swans that complicate the oversight of these sometimes once again massive settlement funds.

Tom: 3:01

So our guests explained the liability forecasting. It's best understood through the three areas that are analyzed. One is the risks and injuries created by a product and its use. Then there are the claims filed and approved and then, as I said, the financial aspects. Our guests say that liability forecasting is both a science and an art. Who's in charge of these trusts and what can go wrong in administering them? What forces come into play that make predicting such a challenge?

Tom: 3:34

I've got two guests, and they're both with Verus LLC, which provides litigation support services to law firms working on mass torts, case management and medical review services, settlement administration, business and advisory services and analytics, which we're going to talk a lot about today. Mark Eveland is founder, cso and chairman of the board of Verus. He's an expert in building effective settlement and claims management programs for mass torts, class actions and insurance runoffs. Throughout his career, he's provided research, discovery, analytics, settlement administration and expert witness support to litigators around the country. Joining Mark is Ed Silverman, who's an analytics executive, analytics expert extraordinaire.

Tom: 4:21

That's French. He's a life science expert, biomedical communications specialist, offers assistance with data analytics, scientific collaboration networks, patient based medical claims and more. There's always more. He's got a background in molecular biology, genetics, epigenetics and biochemistry. I asked Ed to say more about his background and experience and, as you will hear there were fruit flies involved and I welcomed it. Here's my conversation with Mark Eveland and Ed Silverman with Verus LLC. I hope you enjoy it. Yeah, all right. So, yeah, what are we talking about? What did I say? I was going to write to some friends and say this morning I'm talking to someone, a molecular neurobiologist.

Ed: 5:11

That's maybe overstating a bit, but yeah.

Tom: 5:16

I play drums. Okay, all right, you're smart, we get. It All right, we can go.

Ed: 5:24

I get my background if you want. I think a bit. It's definitely all like. Probably you wouldn't see very much of my specific background in litigation. I give a background started off in academia doing, you know, ben's research in molecular biology. But the truth, the throughline, is dealing with lots of data, generating lots of data, analyzing it, composing it, making sense of it. Did work on cancer biology. Looking at everyone knows mRNA from the vaccines that came out recently. Looking at mRNA processing at the city of Hope. Spend time after that Caltech researching memory formation fruit flies of all things and looking at memory formation in fruit flies.

Ed: 6:07

Yeah, which is interesting.

Tom: 6:09

Fruit flies. Remember yeah, which is yeah. I want to be sure.

Ed: 6:14

People think of fruit flies and as an analogy for things they don't have like a very long memory, but there are ways that you can look at things.

Ed: 6:21

Yes, they can learn some behaviors and there's a very specific type of protein that will change shape. We thought it might have some that shape change can have a role in memory formation. I studied that, then moved on to a job in data analytics in the life science space and was doing that for 13 years and then found various and it's been a great way to apply the things I've learned over the years and Wow, great team that I have there.

Mark: 6:51

It's a great fit for what we do, because so much of what we do centers around personal injuries and really understanding, sure, the health care system, how health care data is compiled, how these, what these records mean, is a big part of the analytics component of estimating damages.

Tom: 7:09

Okay, all right, well, we'll jump into it, but I'm also thinking. A side bonus is that Ed must be a hoot at the cocktail parties when he talks about the fruit flies. I'm not a hoot at the bar yet. I make fun because I'm jealous. You obviously were good in school.

Mark: 7:27

He hasn't had a chance to meet my wife yet, but she's also a molecular biologist Awesome.

Ed: 7:34

It's about cloning genes and everything.

Mark: 7:36

Yes, I'm skeptical about eating anything that she cooks without me witnessing what she's doing in the kitchen, because she's experimenting on me.

Tom: 7:45

Okay, that's food isn't such a bad option. All right, so I'll just jump in, and so I'll start off by saying Mark, evelyn and Ed Silverman, thank you very much for talking with me today, and you can say something like it's pleasure to be here.

Ed: 8:04

Thanks for having us. It's a pleasure to be here. That was not.

Tom: 8:08

Have we started yet? Yeah, we started. So let's jump into the first question what role does an expert liability forecast play in resolving mass tort claims, and at what stages in litigation and settlement life cycle do these estimates come into play?

Mark: 8:26

That's a great question. Estimating liabilities comes into play almost from the earliest days of any mass tort litigation. Now, it's often considered to be something you do just before settlement. But the folks who really know this business they'll start thinking about the data they need to collect and what they're going to use it for early on, because they know that eventually, the whole goal is compensation for claimants on the plaintiff side.

Mark: 8:55

On the defense side, they also want to know well, what's this? What's this litigation likely to cost us? If plaintiffs can get past the hurdle and prove the damages, what is this likely to cost us? So they want to know that as early as possible. So both parties have their own interests in mind the plaintiffs and structuring demands and the defense and understanding. What's this going to cost the company? How are we going to pay for this? And it extends throughout the life cycle of litigation, even after there's a settlement, if that settlement is going to be in place for multiple years, they want to constantly be monitoring. What do we, what's the future of the liability related to this settlement look like and how do we have the necessary assets in order to cover those liabilities?

Tom: 9:49

Ed, did you want to add anything?

Ed: 9:52

That makes a lot of sense to me. And there's. You know I always like looking at things from the data angle. You want to be able to provide people so they know what's going on, and the earlier they have it, the more. The more high quality data they have, the better in terms of having that being able to see what's happening in the future.

Tom: 10:09

Okay, how much data does an estimation expert need to prepare a good liability forecast?

Ed: 10:18

It changes a lot, so it really depends on the need, but it's always better to have high quality data than just a ton. So if it's so, what would high quality data be? It'd be, you know, good estimates of what liabilities might be out there. It could be epidemiology, for something that's very new. That might be something you have to generate on your own, so you would go to reliable government sources, medical sources, figure out instant rates. You do fact finding and come up with estimates on your own to get that information.

Ed: 10:53

Other data, good financial data, making sure that you have very accurate information like how much is something going to cost, and we have to base models based on that. So that can get very messy. So getting into a good structure you know, having a collated, having it all brought together, is really important because you can imagine okay, I'm trying to figure out what the average cost is for a particular injury You're missing something huge and you only have a small number of information. It's hard to really get at that Once you have a sufficient amount, and so you might. This question might be what's the sufficient amount of data that your certain is complete? You could start basing estimations based off of that and the way you tell how much is sufficient amount. You run statistics on it. You run a model. If the model falls in the same range at a certain percentage of the time, you could say this is a good amount of data. There's statistical things probably the people in podcasts won't care about used to measure that, but aerobates and such.

Mark: 12:03

I think there's a common misconception. Everybody talks about big data in data science. It's a common misconception that you need mountains of data in order to do this work. I think what it's saying is it's not the quantity of data, it's the quality of the data. Yeah, you need a sufficient number of data points, but that's not as many as most people in the popular understanding think it is.

Tom: 12:35

You mentioned the value of doing this even after a settlement. Tell me about liability forecasts and their relevance after a settlement.

Mark: 12:46

Ed has a team that does this all day long, so I'll just talk for 30 seconds and let Ed draft behind me. If you have a settlement that is intended to pay claimants for a decade or even longer in some instances in asbestos 524G bankruptcy settlements, these funds are intended to last for 40 years, sometimes even longer than that you have to make sure that, as a trustee overseeing a settlement fund like that, you have to make sure there are sufficient assets and you can maintain the cash flow to pay claims as they arise over the life of that settlement. If you're settling a defined population, all the claimants are already known, that's not so important, but if there are going to be future claimants, that estimation becomes critically important. You can manage the assets to make sure you have sufficient cash generated from investment, so those assets to pay claims over an extended period of time.

Ed: 13:44

Health claims can be very messy. There's diseases that arise over a long period of time Cancers, neurological diseases take a long time to manifest. So these trustees really have to protect for the future claimants as well as make sure that the current ones can get what they need. When we're looking at current forecasting, making sure that the amount of assets are distributed in time, we have to take a lot things into account the different scenarios, to make sure that the trustees have all the information they need to make the right decision. If you have this much in assets right now and the investment return rates are at this rate, how much can we burn down and still have money to pay deserving claimants in the year 2050 or whatever the year in the future is because to give enough time for the people exposed or the people using a particular product to have developed the theoretical disease, and then you have to model using epidemiology how many might come in.

Ed: 14:52

Sometimes it can be very complex. Sometimes it can be very straightforward. Some cancers, like mesothelioma, has a very specific cause. Other ones might have multivariate causes and you have to account for that when trying to figure out what the future population might be. So you can imagine exposure to something causes Parkinson's disease. Many things might cause it. You might not know exactly Now. You have to model based on that and there are statistics that you can run to help the trustee understand that. And then, once you do that, you run various scenarios what will happen if your stock market crashes or what will happen if inflation stays above a certain rate? And these are all things we have to deal with and we've dealt with in the recent past to make sure that trustee is armed with the worst case scenario, best case scenario, and can navigate having all this information in hand.

Mark: 15:47

The thing that when I listen to Ed describe what he and his team do, it strikes me in layman's terms it takes a village to do this work well, because Ed is really describing something where you got to pull on a lot of expertise. Beyond just the statistics. You need to be talking to financial professionals who understand and can provide you with information on where they think the stock market is going to be in the future. What do we expect inflation environment to look like in the future? Financial externalities beyond just what are those underlying diseases and how often do they happen in the population?

Tom: 16:28

If these forecasts are wrong, and so I think one of the obvious consequences in the case of long tail settlements is that people don't get paid. Are there any other consequences if the forecasts are wrong?

Ed: 16:49

The rate at which they, even if there's money still left behind, the rate at which they would still get paid would go way down. And that's something we try to make sure that the trustees know. You can afford to pay at a certain rate and still have things left over. If you get that wrong, all of a sudden you're you know someone that comes in claiming needs the money and can't get anything worthwhile. It might be, you know, like pennies on the dollar and that's, you know, tough for everyone. And then I think and Mark and I speak more to this there's liability for the trustee too if they allow the trust to become bankrupt.

Mark: 17:26

Yeah, if there are substantial inequities between past, present and future claimants, that creates risk for the trustees and their professionals. So it's important to get these forecasts as accurate as possible, and what we already hit on is the impact on the claimants themselves. These settlements are created and intended to distribute money to the beneficiaries and maximize what every beneficiary receives. This is critically important to make sure that happens.

Tom: 17:58

So, in the spirit of times change what considerations go into your models? Now that maybe didn't, you didn't used to have to include, that's a good one you just rubbed your hands at like you were. I can't wait to talk about.

Ed: 18:17

Yeah Well, it seems to me like seeing all these things and I'm relatively new to the process, so I'm seeing everything through fresh eyes and how we do it, and it's like learning of the history of this and the lot of things happened recently. You know, with COVID that our models had to take into account you know they're very good looking things long term, short term. When that happened, you know, and the knock on effects. There's a few things we had to really start recommending and things we just never. You know, we we expected in some ways but didn't expect the magnitude. One of those would be, you know, the rate that inflation was after COVID.

Ed: 18:57

During COVID people stopped going to the doctor as much, except for some of the most you know the worst cases of disease. So that impacted filing rates. And so when we build our models right, we look long term and we know long term how many people are going to, you know, are likely to come in and make a claim. We calculate the propensity that the file claim. But that falls off the short term.

Ed: 19:21

And then later on, what we're seeing now it's catching up as things open back up again and the models have to be able to adjust to catch up to that and you know, in normal times you'd expect there to be a tail. If you have, like, an exposure and a set period Over time, the people who are going to develop the disease that's going to peak and then level off. But when you had something like you know COVID occur, it went down artificially. Now it's going back up again and probably at some point in the future we hypothesize that it's going to go back down. But the models are built in a certain way and we have to. We had to account for that and we're, you know, even adjusting as we go forward, making sure we have all that. Now, inflation no one expected like there to be eight, nine percent in a lot of trusts that you know have caps to on various things because of that.

Mark: 20:17

COVID is a great example of how we have to make sure these models are adaptable, because it just speaks to the need to be able to do more sensitivity testing around. What are the uncertainties and the? And accounting for the unaccountable, the black swan events, and that may throw these things into a period of a wider cone of uncertainty for a period of time. And COVID was such a perfect storm where you had, as they had already hit upon the shutdown of the healthcare market he was going to doctors, but for critical issues. You had the supply chain disruptions that spiked inflation. You had the stock market crash that thankfully, you know, recovered relatively quickly, but all that happened in such a short, contracted period of time. These models didn't account for that and we're still seeing the knock on effects of that.

Tom: 21:15

So now I guess in your models you throw in something like a black swan.

Mark: 21:20

We throw in the meteor strikes. What is?

Tom: 21:23

well, there's one headed this way apparently.

Ed: 21:26

Yeah, absolutely Like you know what will happen if assets fall. You know 20%. What happens if you know filings unexpectedly jump 10%? What happens if all this stuff happens at once and inflation is really high? You know, we, we have all these. We haven't set up in such a way we can run like the worst or the worst case scenario. So the not to scare like the trustee, but so they have that at their fingertips and they know, okay, if everything goes wrong, this is what we'll have to do, and then we can adjust accordingly. And then, more importantly, since we'd like to have this, this available on a, you know, very frequent basis and in that case we'll see it coming get early warning that something's happening and the trust can adjust as need be.

Mark: 22:09

I think that's an important point it is is these these models are not set in for get it type things, and the more frequently not looking at them every day but the more frequently you update them monthly, quarterly the better decisions that the parties in this, these mass torts, can make. Yeah.

Tom: 22:32

That's like any business. Forecasting Working in a large company was every quarter, and we so look forward to that. There's also a good time to for the CEO and others to say why aren't you hitting your numbers? You mean the numbers that I gave you, that you told me to increase more than I wanted to. That's why I'm missing. No, I'm not bitter about any of that. The thing that just occurred to me too, though, and I'll cut this if it's completely irrelevant. But let's just say, in the mass tort, you've got people who are often sick so say you're dealing with cancer or something, or their immune system is compromised. Then COVID comes along, and then more people may pass away because of that.

Mark: 23:17

That's a great point because we've seen that there may be a long term effect. When you're talking about lung diseases in particular, covid was a pulmonary virus and cases involving lung cancers and mesothelioma, like talc and asbestos and some of these other toxic exposures that also are contributing causes to lung cancer, a certain percentage of the population that would normally be diagnosed with lung cancer probably passed away because of that. Their lungs were compromised and those claims will never arise, and that's something that the epidemiological studies didn't account for. The at risk population may have declined as a result of COVID. Just don't know to what extent that may be true.

Tom: 24:10

We'll know that three, five years down the road there were a million, a million people. What role do model scenarios You've got a best case and we've been talking about worst case play in helping the parties understand future liabilities? Is anything like Monte Carlo simulations in the investment world?

Ed: 24:49

Yeah, I think it has a lot in common with that, because you want to know what your best case is, you want to know what your worst case is and you want to present this is the most likely case and that's where the Monte Carlo will come in like. We have all these data points, what's the most likely scenario? And then how can you plan based off the most likely and whereas the best case and worst case can kind of set some guard rose for the trustee or might even give us some ideas. If you have a best case scenario where investment returns, say, are 7%, maybe they're getting like four and a half, they might say you know what? Maybe I'll talk to my investment company and see what we can do about increasing the rate of return and that in turn will lead to there being more funds available for future claimants. So it helps guide them in that way. It helps.

Ed: 25:32

You know, we don't the trustees tend to be very conservative, as you would want them to be, but you wouldn't like not give too many bad scenarios. We give them like a whole range and that way they're prepared so you could say all right, this is what's going to look like if inflation is at 6% a year for the next three years and like three and a half after that. This is what you're what's going to look like, and then you could have a whole battery of inflation scenarios after that. What if it's 7% this year and you know, 3% the next and then two after that? And you have a flexible model. If you set up your machinery in the right way, you can run all kinds of scenarios and even in discussion with the trustee saying, okay, here's what we have. You know, is there anything else you'd like us to consider? We'll give you that information. So you have everything you need to make the right decision for the trust.

Mark: 26:23

And if I put my CEO hat on for a second when I hear that, I love it, because what that does is it allows the parties managing these things to make contingency plans, so when things occur, they already have an idea of what they can do, what the levers are that they can pull on to force correct, as opposed to being reactive in the moment.

Tom: 26:47

And what people didn't see because it's a podcast, is Mark actually changed hats during that. It's a. It's an odd thing. You've talked about some of the challenges. Do you want to want to share any other challenges occur and been trying to predict future filing and approval rates?

Ed: 27:08

There's, there's things out there like technology I think can really potentially affect it. And I mean by that let's even look at the example of just diagnosing a disease. Let's say that a new technology comes out that makes it. That's great for humanity, makes it really easy to detect lung cancer early. All right, well, that's great for humanity. But if you're looking at trying to predict how many lung cancers in the future, you're detecting things early. These people might have died with it being undetected Previously. Now a sudden you have a larger population of people that might have a certain disease and then, if they can, you know if they have exposure. Then there's more liability potentially for a trust. So technology can do it and it's hard to imagine what hasn't occurred yet. But that's something that kind of came out that I could change things quite a bit. We know we saw COVID and there could be any number of economic things that happen in the future that could Really affect these these trust?

Mark: 28:07

Yeah, and when you, when you talk about technology impacting how we forecast liabilities, and I start thinking about changes in Changes in the technology environment related to AI and how that may impact the number of cases filed in the future in mass torts, and I'm specifically thinking I hate to use the F word, but I'm thinking of fraud and thinking of boiler rooms in other regions of the world that may see mass tort litigation as a financial opportunity to make some money and generate fictitious claims. That's something that nobody thought about five years ago because the technology didn't exist. It may not quite be there today, but I will guarantee you there are smart people with nefarious intent who are out there thinking about that Deep fake plaintiffs Deep fake pathology reports Wow, yeah, that's all right.

Tom: 29:09

Well, if they, if they didn't think of it and they listen to this, they will now.

Mark: 29:12

And I'm pretty confident they are. We've I've had conversations with people in the class action space who have seen this happening and there are ways to combat it and mitigate it. But you got to be aware of that and you have to. I think we start factoring that into forecasts. It may not lead to liability if you can detect that fraud, but it certainly increases the cost of it, of the litigation and administering a settlement if and when a settlement occurs, because that fraud detection comes at a price.

Tom: 29:46

OK, so anybody in mass torts is going to know who Varis is, but for those who don't, can you mind sharing what are some of the big cases that people might have heard of that you all have dealt with.

Mark: 30:00

Anyone who's known Varys since we founded the company 20 years ago probably is familiar with our work in the asbestos space, the granddaddy of all mass torts. We currently administer 45 of the currently operating asbestos bankruptcy trust and I go back to the early 90s in the asbestos litigation, been involved for a long, long time. But we've also done a lot of work on talc cases, administered one of the first talc settlements that's been been done. We have another one that we're working on right now. We were involved in doing estimation for the roundup litigation and helped structure a class action which unfortunately or unfortunately depending on your perspective was not confirmed by the northern district of California, but that was a very interesting project. We've also done a lot of work in the sexual abuse cases and that would seems pretty far afield from asbestos and pesticide exposures but very similar in terms of the impact on people and the way you think about forecasting claims and and and and liabilities. I'm sure I'm forgetting a dozen others, but those, those are the big ones that come to mind.

Ed: 31:29

We've got some identity theft too, I believe, right.

Mark: 31:31

Yeah, yeah, we've done some data breach cases and a lot of of medical device and and drug cases as well.

Tom: 31:45

Well, congratulations on all your your success. It's always, it's always heartening to see good people do well in business and and you've obviously done that and I've known you for years, and congratulations on all that and thank you guys both for for talking with me about this today.

Mark: 32:02

Well, tom, I really appreciate this. This has been fun, and the reason I've stuck at this for 30 years is it's always different, it's always interesting, it's constantly changing, so it satisfies that intellectual curiosity.

Ed: 32:18

Right, yeah, absolutely, and thank you very much for having me.

Mark: 32:21

Really appreciate the time today, tom. Thanks guys.

Tom: 32:27

That concludes this episode of the emerging litigation podcast, the co-production of HB litigation, critical legal content, vlex fast case and our friends at LostG Media. I'm Tom Hage, your host, which would explain why I'm talking. Please feel free to reach out to me if you have ideas for a future episode and don't hesitate to share this with clients, colleagues, friends, animals you may have left at home, teenagers you irresponsibly left unsupervised, and certain classifications of fruits and vegetables, and if you feel so moved, please give us a rating. Those always help. Thank you for listening.