About 1% of recipients of a pandemic-era pension rescue plan were deceased. Their applications were filed fraudulently. The improper payments were $127 million.
About 1% of recipients of a pandemic-era pension rescue plan were deceased. That is, their applications were filed fraudulently. The improper payments amounted to $127 million. To find out how they had fixed the problem, the Federal Drive with Tom Temin turned to Ken Dieffenbach, the executive director of the Pandemic Response Accountability Committee.
Interview transcript:
Tom Temin: So, tell us about this program? This was through the Pension Benefit Guaranty Corporation?
Ken Dieffenbach: Yes. So, one of the many pandemic-funded programs was the Special Financial Assistance Program, which was administered by the Pension Benefit Guaranty Corporation and the aim of it was to ensure that multi-employer pension funds could survive to pay the benefits that their employees had had earned.
Tom Temin: Right? These were multi-employer pension funds. So, a lot of union working people basically their pensions?
Ken Dieffenbach: Correct.
Tom Temin: And what happened? I mean, how did the pandemic affect pension funds?
Ken Dieffenbach: So, the pandemic-affected pension funds in that it gave a lot of instability to their investments, and some of the funds were not funded appropriately before the pandemic. So, it really created a real crisis for all the folks that rely on these pensions.
Tom Temin: Right? So, Congress, then, in its wisdom, through the pandemic relief mechanism, provided the counterbalancing to those losses.
Ken Dieffenbach: That’s correct. They allocated about $91 billion for pension funds that were suffering the most.
Tom Temin: All right. And what happened? What did you discover with respect to people applying for pension benefits?
Ken Dieffenbach: So, we at the Pandemic Response Accountability Committee worked with the Pension Benefit Guarantee Corporation Office of Inspector General, which is one of the smaller OIGs, to build a risk model where we took a look at about 10 years’ worth of data. We looked at the applications from some 1,300 eligible applicants and we built a risk model that looked at a wide variety look at well over a dozen factors in that data to help the OIG identify the most risky applicants so they could then do their traditional oversight work to determine who was most likely to have issues or problems in their data.
Tom Temin: All right, and what did you discover?
Ken Dieffenbach: So, we found that there were a number of applicants that had anomalies that needed further follow up and it resulted in high volume of question cost. But most importantly, or most telling, was that two particular applicants listed deceased people in their projections in their data and that resulted in over $127 million in recoveries, actually, a total of $135 million in recoveries from those pension funds who had already received that money. So, it’s kind of clawed back from them, if you will.
Tom Temin: This money didn’t really go to deceased people. It went to people that had filed using deceased people’s names to be accurate?
Ken Dieffenbach: Not exactly. It went to the pension funds who were prepared to pay out benefits, but they had done either inaccurate or had incomplete data in their applicants to the PBGC. And one important thing to remember is that 1,300 pension plans applied to be part of this program to receive benefits, but only 250 were able to be funded because, as with all government programs, there’s a limit to the amount of money. Amount of money that Congress allocates to a program. So, what this risk model was really doing was helping to ensure a level playing field for all of the applicants. And again, at the end of the day, we found that over $135 million in payments should not have gone out. So, that money was recovered.
Tom Temin: Got it. So yeah, your report says that 3,500 deceased participants were among the 360,000 planned participants that got money.
Ken Dieffenbach: That’s correct.
Tom Temin: Yeah. So that’s about 1%?
Ken Dieffenbach: Yes.
Tom Temin: Well, in federal money world, 1% is a lot of dollars.
Ken Dieffenbach: Indeed, it is.
Tom Temin: So, this money was able to be restored to those pension funds?
Ken Dieffenbach: It was able to go back to PBGC, the entity that had awarded them, and then they were able to reallocate those funds as they deemed appropriate, to more deserving pension funds. Yes.
Tom Temin: I mean, how does that turn up in the data?
Ken Dieffenbach: So again, there’s rather complicated, but there are dozens of risk flags where we look at over the last 10 years, what did you report last year? What did you report the year before? There are trends in the data we expect to see if they’re following the rules, if they’re doing what is expected via an actuarial table, for example. So really, none of it is black and white. Determinative of this is a problem. They all when you combine all those risk flags together and you have multiple risk flags, it points to this deserves more attention. And it’s important to note too that none of the analytics that we do, that we’ve ever done is a black and white answer. It always points us to things that deserve more exploration. And that’s what we do in the oversight world is we explore more deeply. But analytics allows us to use those resources more effectively.
Tom Temin: But vital records, for example, the list of all deaths people, is not part of the data mix that you’re analyzing?
Ken Dieffenbach: No, it probably would be part of it. But I don’t know in this instance if, for each applicant, we went and compared the participants in their plan with death records. I also don’t know, off top my head, if they’re even required to give us that listing of all those people. I believe it’s more of an actuarial statistical table type exercise.
Tom Temin: Of course, a lot of John Smith’s die every year too. So even that only gets you close necessarily.
Ken Dieffenbach: Indeed, death data is actually very complicated because it’s collected at the state, local level, often county coroner level. It’s not collected in the first instance by the federal government at all. So, it’s very challenging data to work with.
Tom Temin: We’re speaking with Ken Dieffenbach. He is executive director of the Pandemic Response Accountability Committee. And, by the way, give us the latest disposition with respect to Congress and the life of PRAC and what’s going to happen with that analytical engine that you have built once the PRAC eventually sunsets.
Ken Dieffenbach: Yes, there’s a pending bill in Congress called the Government Spending Oversight Committee that is being debated. But separately the Senate Appropriations Committee, just a month ago, in a budget bill extended us for two years. We’re supposed to expire, as you know, in September of 25. The budget bill extended us till 2027 but we have not heard how the House of Representatives is going to respond to that. So those are two things that are up in the air, but we certainly hope that we are able to extend this important work with the pandemic, but potentially with other government programs too, because we found that this model works. Looking at big data, applying risk scores, helping again, target limited resources is proven, I think, to be an effective strategy.
Tom Temin: Right? And the PRAC itself is not really a gigantic entity. Most of the members are already inspectors general, part of CIGIE in their agencies. So, there’s really a tiny little staff that constitutes the marginal costs of PRAC.
Ken Dieffenbach: Indeed, small but mighty Tom. The PRAC right now has about 50 employees. We do have over a dozen inspectors general that serve on our on our board of directors, essentially. But we run at about $17 to $20 million a year right now. So, you’re right. I would agree that the marginal cost of that staff, which can impact all OIGs, and most importantly to me, our mandate is cross agency work. So, most inspectors general are funded and have jurisdiction to look at their programs. We are able to look at all federal programs and it’s important to note that fraudsters are agnostic to the organizational chart. They don’t just rip off from labor or just the Small Business Administration. They find the weakest program or programs and exploit this. We’re able to look across all agencies and identify some of the same bad actors who are impacting multiple programs.
Tom Temin: And by the way, a lot of these bad actors are not even based in the United States, right? These are foreign players.
Ken Dieffenbach: Yes, that’s absolutely true. And it’s interesting to think about the fact that 5-10 years ago to obtain public benefits. You had to get your records together and go to the county facility and apply. The internet has reduced the friction of getting public benefits, which is a positive for most in the country and people that don’t need to leave their home to get benefits. But absolutely, it has opened up the door to people anywhere in the world applying for benefits, finding the weakest program, finding the state that administers the program in the weakest manner and requires the least amount of information. So, it’s a whole new problem we’re grappling with.
Tom Temin: I think it’s 35 years since the on the internet, nobody knows you’re a dog cartoon, and it’s much more so now 35 years later. And by the way, you yourself have only taken the PRAC executive directorship a few weeks ago. Tell us a little bit about where you came from.
Ken Dieffenbach: Sure. I started this position July 1, but I’ve spent my whole career in the oversight community, first with the Department of Justice OIG for about 18 years, and then at the Department of Energy OIG for about three years. And when this opportunity came up, it was an easy yes because it combines my passions, data analytics, fraud prevention, working with really smart, dedicated people. And it’s been a really fun several weeks and I hope we get to extend and keep doing what we’re doing and get better every day.
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Tom Temin is host of the Federal Drive and has been providing insight on federal technology and management issues for more than 30 years.
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