How Fraudsters Are Bilking the Government Out of Billions of Dollars

24:14And here, the insurance company is the government. Right. And so rather than trying to make just top-down solutions, and there are some top-down solutions, don't get me wrong, it's really important that we also allow the individuals who have the information, the valuable information, to be rewarded for that. So the whistleblower program, private information, there's also the private cause of action. The idea that every person can become, literally the legal term sometimes used is private attorney general, right? You can go and hire a lawyer and sue, and that lawsuit is on behalf of the United States of America.

24:47And this has been an extremely effective program historically. Say more about that. So I ran a Freedom of Information Act request on the Department of Justice for data on every whistleblower lawsuit from 1987 through about 2017 when I first filed this. There are thousands of these cases. They've brought in billions of dollars for the government. Fifty-five percent of them are in health care, but they're also used all over the government. We see cases related to the Transportation Department, the Department of Education, the Department of Defense. And the idea is the same. Instead of trying to have the government figure out how to run anti-fraud, anti-fraud can pay for itself.

25:20There are lots of people who would love to earn a million dollars being a whistleblower. And these whistleblowers get paid pretty well. Since you mentioned transportation and education just then, can you talk about some examples of fraud outside of the medical sector? Absolutely. So I have a recent paper about the unemployment insurance market during COVID. So I'd be happy to talk about that. Oh, that's great. So during COVID, we had the biggest expansion of unemployment insurance in history. I think you guys know about this. You've talked a little bit about PPP, and there's some great research there on fraud.

25:49PPP was for companies. Unemployment insurance was for individuals who lost their job. And we expanded it to also include gig economy workers through a program called PUA. You guys probably know about this. So this is starting to feel like history to me. It's so crazy because this is like yesterday, but also it's like - It feels so long ago. Yeah, but yeah, keep going. The time distortion is real. So unemployment insurance rose heavily during the pandemic, and with it came a lot of fraud. Now, why is there fraud in the unemployment insurance sector? Well, the government's cutting checks. And the government's cutting checks really fast.

26:19So you might remember at the beginning of the pandemic, there was this immediate recession. And there was fear of the big macroeconomic consequences of everybody being out of a job, or many people, I should say, being out of a job. And so the government really loosened and expanded its use of unemployment insurance. But whenever the government says, hey, we're going to write$800 billion in checks, people get creative about ways to build the government. And in particular, this type of fraud was really identity theft. It is super easy, super easy to go on the dark web and buy a social security number.

26:48And so that's what a lot of criminals and organized criminal groups did during the pandemic. There was a widespread fraud where individuals would go, purchase identities, apply en masse to state unemployment insurance programs, collect the money, take it out of the system. And then we think in many cases it was either offshored or rerouted to criminal organizations. So this was so incredibly widespread. Actually, my wife got a prepaid debit card in the mail from the unemployment agency and she didn't lose her job. I've talked to just dozens of people across the country and in all sorts of sectors who said, yeah, this actually happened to me.

27:26Wait, this leads to something that I want to ask as well, which is how is fraud propagated? Because this kind of gets to Joe's question earlier. How do people actually do this? How do I learn to do fraud? Is it like I just look it up on the Internet or is it like someone I'm associated with tells me? So different frauds have different mechanisms by which people learn them. But in general, there is a social learning component to this. Absolutely. So with some of the institutional frauds that we've been talking about, a hospital that decides that they're going to suddenly charge a lot of money through some loophole, often there are business decisions being made by executives.

28:01Sometimes there are consultants involved. And big national chains often are the ones that spread these because they have kind of centralized management. The hospital administrators from different hospitals look at this. I'm thinking, for example, of the Tenet Hospitals, which paid$900 million back to the government for a small little loophole that was supposed to be for an outlier payments program. And they just drove a truck through that loophole. They ended up stealing billions. What was that about? So if you go inpatient in a hospital, the hospital gets paid a fixed amount under Medicare through what's called a prospective payment system.

28:29They don't pay per cost. They just say, you know, you have a pneumonia. We're going to pay this much. The government was worried when they set this program up that some very expensive patients wouldn't get treatment because if the hospital knows that and they know that they're going to lose money on you. So they made this asterisk. A lot of these things are asterisks to have an outlier payment system where if a patient is super expensive, then they pay extra. And the tenant hospitals figured out how to make every patient look super expensive by manipulating some of their balance sheets. And they ended up spending$900 million to, excuse me, they ended up to settle claims so that Tenet never admitted fault, I should say.

29:05But the government received$900 million back from Tenet because, you know, these allegations were, I think, true that Tenet had done this. And I estimate actually that the government lost billions of dollars to that. OK, so but I want to go back to this question. So how did that one happen? Well, there was a consultancy in New Jersey that was going around telling people, hey, do you know about this outlier payment system? And that's how we think that that fraud spread. And it spread all over the country. So there's some of this corporate learning from other companies, and there's also a lot of social learning.

29:31So in the case of unemployment insurance fraud or PPP fraud, you can go and you can find Telegram groups and Facebook groups of people that are like, here's how you apply for a PPP loan. There's a great new paper by John Griffin at the University of Texas on these Facebook groups. And they're called like fraud kings. They know what they were, maybe PPP loan kings or something. It's like really obvious what they're doing. and everyone knows. And so in the case of healthcare fraud, in the case of non-healthcare fraud, often it's you're surrounded by people who know how to do this, or you meet them through digital platforms, and then people learn.

30:04And so a lot of the fraud we see propagates through communities. In the case of the ambulance market, we saw that there were certain Eastern European groups that were responsible for this in different parts of the country, and often from the same, from original areas. And so, you know, generally, the understanding, at least from the government, is that there must have been some social learning going on there. It's very hard to prove, of course. Right. Someone figures it out and then tell family members. That's right. That makes sense. Let's talk about detection via data and more. And you talked about how you have access to all of this data and so forth.

30:35Obviously, you can describe fraud qualitatively by saying, this is how an ambulance company cheats the government, et cetera. What do the fingerprints of fraud look like when you look at it on the macro scale? What do you what pops up in the data that would at least be a yellow flag and say this is something we need to look at more? So there are huge run ups in spending in every type of fraud I've ever seen. And every time I mean, the whole point is, if you're not making money, it's not a good fraud. Right. And so if you just make a plot of spending against, you know, time, you can often just see these really big exponential growth.

31:10Now, some of those are legitimate because if there's a new great medical technology and people start using it, that also looks like a technological adoption. curve, right, of a kind of big upward ramp. But when it's fraud, you know, first of all, it looks like, you know, massive year over year increases. The second is that it'll often be way too much to the point where it's obvious that nobody's getting this. For example, sometimes you'll see a doctor who's just billing for too many home care visits, but like the home care visits are 60 minutes and the doctor's billing for 5 ,000 of them a year.

31:42And it's like, well, that's 5 ,000 hours. There aren't 5 ,000 work hours in the year. The government should just be able to detect that. We should just not pay those. So where the failure is, is not in how hard it is to detect in data. It's actually not that hard to detect in data. It's on the incentives for the enforcers to look at that data and use it appropriately. And that's where we get back to this limited enforcement capacity. How do you measure benefits on the other side? Because that seems, again, like a potential avenue where there could be some disagreement. So I think it's super important that we preserve access to the public programs.

32:14and my research is not focused at all on, you know, how do we take things away from people? And in particular, I always try to measure very hard whether there are health effects associated with this. So in the case of the ambulances, we're able to show pretty definitively that there are no negative health effects associated with cutting this. And this is something I do in all my papers. You really have to ask the question, were people losing care that they needed? And so super, super great question. In the context of some of these very obvious frauds, sometimes people aren't even getting the service.

32:44So if the government's paying for something and nobody ever got it, taking it away is costless. So that's the best efficient thing that we could do is just stop paying for things that aren't even happening, right? Let's not even talk about waste. Let's just talk about these outright frauds. And so how do you measure it? I mean, if you look at very large scale claims data, as I do, and the government can, you can see, okay, we cut out this provider. Let's look at their patients. Did they go to the hospital more? That's an empirical question, right? And so there's no reason that it just has to be a guess, right?

33:13This is something that we should be measuring as part of our data analysis associated with anti-truck.

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35:00Crypto trading provided by Backed Crypto Solutions, LLC. Complete disclosures available at public.com slash disclosure. So it's very obvious why, whether we're talking about the government or private insurance, that it's just rules on top of rules on top of rules and asterisks and so forth, because this is very complicated stuff. Rules also create problems and compliance by the rules can, you know, strict adherence to the rules can also have negative effects. I have to imagine, for example, that there are, say, many people in the U.S. currently on some sort of GLP-1 drug, but maybe technically they don't have the thing.

35:41But, you know, there seem to be a lot of benefits from weight loss and so forth. Have you found examples in your research in which there is some sort of positive externality from deviation from the rules? Absolutely. Absolutely. Thanks for queuing this up. I have a great story. So let's talk about the hospice industry. Hospice care is an end-of-life benefit for patients who have a prognosis of six months or less. So if you're dying and a physician certifies that you're dying within six months, you qualify for hospice. What is hospice? You give up the curative care and you stop taking all of these meds with the horrible side effects and you stop going to the hospital as often and you can die peacefully at home with pain medication.

36:20And I think that this is a great program. I think it's really important. We spend$20 billion a year on the federal hospice program. And more than 50 % of Medicare patients who die every year will have had a hospice claim. Okay, this is important. This is the way that we're treating people at the end of life. It's really hard to know who's dying within six months. That is not a trivial estimate. And historically, there was subjectivity in this. So over the 20-year period, 1999 through 2019, there was a quadrupling of for-profit hospices, and many of them increasingly took Alzheimer's and dementia patients.

36:59Why? Because they stay for a long time on these hospice programs, and the hospice programs are paid about$200 a day. And the federal government cried fraud. And we saw 163 federal whistleblower lawsuits against for-profit hospice companies, more than$300 million in settlements saying, this is fraud. You shouldn't have taken the patients. You should have known that they weren't dying fast enough that they did not qualify for hospice. So I wrote a paper with John Gruber at MIT, as well as one of our PhD students and David Howard at Emory, some really top-notch economists. And we looked at this program.

37:34We said, okay, this is something people are really concerned about. And what we found really knocked our socks off. The fraud actually was not as bad as people said, in particular, because did some patients go to hospice who may otherwise not have because they were not dying fast enough? Sure. But it's still a heck of a lot cheaper to go to hospice for six months than it is to go to the hospital and the nursing home and the home care agency and the durable medical equipment and the pharmaceuticals. And we estimate that these patients saved tens of thousands of dollars and the patients liked it.

38:03They and their families are picking hospice. This is something that they want. They don't want the hospital. So they're getting a service they want and the government is saving money. And yet we've decided that this is a fraud because there's this rule. Now, when you think about it from that way, six months is totally arbitrary. Why is it six months and not eight months? And so this paper, it's called Dying or Lying. It just was accepted last week at the AER. It shows pretty conclusively that the policies aimed at limiting fraud threw the baby out with the bathwater. You know, we started this conversation mentioning Doge, the new Department of Government Efficiency.

38:36I guess one obvious question to ask you would be, you know, if you were in Elon or Vivek's choose, what would be your, you know, the first thing you would start with when it comes to rooting out fraud? Or I don't know if you want to get into wider types of inefficiencies, but what's the first thing you would do? Yeah, they make you the real point person on this. How do you start? So first, I'm optimistic about the Department of Government Efficiency because I think that there are some clear, evidence-based, obvious wins that we have left on the table in terms of saving government money. The first is we know that anti-fraud efforts, particularly in the Medicare and Medicaid programs, have huge return on investments and we underfund them.

39:19So we could staff up those offices at the Department of Justice, at the Department of Justice districts, at the Office of the Inspector General, and literally just chase down the leads that we already know about. We don't even have to go after other leads. If you look at your average civil assistant U.S. attorney, they've got 30 good cases on their desk and they get to pick two of So there and many of these cases allege millions of dollars of fraud. And they say, I'm sorry, we have a number. We can't go under anything right now less than 10 million. And they and they drop the case. So even if we just doubled the number of people or at the Department of Justice who focus on fraud against the government, that would pay for itself many times over.

39:53OK, so that's an obvious one. The second is to start getting serious about data. If we do not hire and recruit excellent analysts to look at government data from the government, we are going to miss obvious frauds. Everything I've talked about so far is not rocket science. We're talking about huge amounts of billing for patients who obviously don't need it in some of these cases. And the government pays it and they miss it. They have access to that data in real time. They're writing the checks. Why are we not screening that? And the answer is not a lot of data analysts work at the Department of Justice.

40:23And there are some. There are some that work at the Office of the Inspector General of Health and Human Services, but not that many. Why? Because they pay like$70 ,000 a year. And if you're a good data analyst, you don't go to work for the government for$70 ,000 a year. If the government wants top talent, it's got to be willing to recruit. And those are competitive positions. And so if we get serious about data, we get serious about machine learning, we get serious about investing in the attorneys who are doing the good work, and just the programs work really well. The second is rigorous evaluation, right?

40:53We need to know when we try policies, do they work or do they not work? When we put in a prior authorization program, when we put in a new screening for people to take a selfie on their phone before we pay their unemployment insurance claim. That's actually how we fix the unemployment insurance problem. It was an identity theft problem. You just got to take a selfie on your phone. These ideas work, but we need to evaluate them as serious policy. I just have one more question, and I don't even know whether this is capable of being ascertained in a substantive way. But when you think about the stuff that you research, and we're talking about like, you know, big question is, can we meaningfully move the dial on fraudulent spending or maybe wasteful spending?

41:36And do we have a number that exists? Do we know how much could potentially be saved? So I think healthcare fraud alone in the United States is something like$100 billion a year. Now, that's across private and public, but we spend$1.5 trillion on Medicare and Medicaid. So the idea that - So that's a big chunk. No, it's not that big of a chunk. We spent$2.3 trillion overall. And so to say$50 to$100 billion a year of fraud, yeah, I think that that number is reasonable. So can we move the needle on it? Absolutely. There are things that we know work really well, huge treatment effects at very low cost.

42:12Now, government spending overall, we spend, what, six, seven trillion dollars a year in the government. When we look at these other public programs, there are some that have obvious frauds going on. Like I mentioned the unemployment insurance system that had tens or possibly a hundred billion dollars of fraud there as well. The PPP program we spent, you know, a hundred billion dollars or at least on fraud in that program. And so not every program is rife with fraud. There are programs that are very hard to defraud. It's hard to defraud Social Security. Why? Because they have your full earnings record and they pick the number and they send you a check.

42:45Very limited fraud in the social security system. I think that there's probably a lot of fraud in some of the infrastructure and defense. And so our framework has to be like, where does the government know the least? The government doesn't really know what's going into every element of a defense spend or every element of, you know, how is the road being built or what is the hospital doing? And when you have those big information asymmetries, that's where the big fraud is. So if I think that there's$100 billion just for Medicare and Medicaid spending, or maybe, you know, throw in the advantage spending and the, you know, other federal employees health benefits and the VA to get to the$100 billion number.

43:18Of that health care fraud, I think that it very easily, that$100 billion also occurs yet again in some of these other, like defense, I'm sure that there's plenty. Jensen Leader Luis, amazing. Thank you so much for coming on the podcast. Thanks for having me.

43:45Tracy, I am pro getting rid of fraud. No, I'm for real. I don't take a lot of it. It seems like a low bar, although you did ask repeatedly how you could commit it. But I know that was for informational purposes only. No, I'm going to actually I'm not going to get into the business of medical fraud. I'm going to get into the business of being one of those independent whistleblowers suing out. The bounty hunters. Yeah, that sounds great. But I feel like I need to know how it works so I can identify it. But I really do think, you know, setting aside everything, fraud is bad. I do think, and I kind of said this in the beginning, that we can all agree that fraud and waste is bad.

44:22Like no matter where you fall on the political spectrum, because if you free up money that's not doing anything, then you could, in theory, put it to a different use and get more bang for your buck, so to speak. I think, obviously, and we touched on this, like a lot of these decisions over what's fraudulent or especially what's wasteful, maybe not necessarily fraud, come down to specific judgments. And they can be subjective. And I think that's where a lot of the disagreement is going to be going forward. But I do think the point about using the data better, the government must have some amazing data.

44:58We kind of talked about it, especially in these specific sectors like healthcare? Two things on the data that are really interesting. So one is just this idea that if you're a talented data scientist - Go to the DOJ. And this has come up in some of our past episodes that we've done with a few other guests. There does seem to be this structural issue, right, of how government pays and how compelling a job in government is, et cetera. And so there does seem to be an issue with how do you staff up a big team of data scientists that are incented and have the agency and capacity to use that data or do something when they discover it.

45:36And then it was a very interesting comment, that last one, about it's very hard to defraud Social Security. And so this idea of like, where is the fraud most likely to exist? Areas in which there is some limited asymmetrical, as economists like to use, information, which is, you know, the government doesn't know what happens when you and I go into a doctor, right? There's some level of information asymmetry there. It doesn't really know what kind of walker you or I are going to need in 30 or 40 years to get around, et cetera. And then furthermore, we didn't touch on it, but I do think we're absolutely as a podcast are going to need to do more, should do way more on defense spending.

46:22There's a million angles that we have to do on that. But you can see like, oh, we don't really know what went into the assembly of this and the cost of this program. And is this part really worth this much money or maybe it's a competition thing? There's a lot of fruit there for future episodes. But I do think one of the big tensions here is the sort of government generalists versus like the experts that they're listening to. What I mean is, for instance, if you're a defense contractor and you're building, I don't know, like a submarine launch pad or whatever. like the government official isn't necessarily going to know all the nuts and bolts that need to go into that.

46:58And so, yeah, I always wonder how you sort of overcome that informational gap. There's a lot there. Let's do more on this topic. All right. Shall we leave it there for now? Let's leave it there. This has been another episode of the All Thoughts podcast. I'm Tracy Allaway. You can follow me at Tracy Allaway. And I'm Joe Weisenthal. You can follow me at The Stalwart. Follow our guest, Jetson Leader Luis. He's at Jetson Econ. Also, he has a number of papers on his website that you can just click on and go read. They're all really fascinating. Follow our producers, Carmen Rodriguez at Carmen Armand, Dashiell Bennett at Dashbot, and Kale Brooks at Kale Brooks.

47:32Thank you to our producer, Moses Andam. For more Odd Lots content, go to bloomberg.com slash oddlots. We have transcripts, a blog, and a newsletter. And you can chat about all of these topics in our Discord with fellow listeners, discord.gg slash oddlots. And if you enjoy Odd Lots. If you like it when we dive deep into the data around government fraud, then please leave us a positive review on your favorite podcast platform. And remember, if you are a Bloomberg subscriber, in addition to getting our new daily newsletter, you can listen to all of our episodes absolutely ad-free. All you need to do is find the Bloomberg channel on Apple Podcasts and follow the instructions there.

48:11Thanks for listening.

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