00:00Time has expired. I now recognize Mr. Timmons from South Carolina.
00:04Thank you, Madam Chair. The greatest national security threat we face is debt.
00:08We have $37 trillion in debt. We have a $1.8 trillion annual deficit.
00:13The work this administration has done over the last few months has been designed to try to find waste, fraud, and abuse to help mitigate that.
00:21They're also working to help grow the economy through tax reform, through renegotiating trade agreements, through deregulation, through streamlining permitting.
00:28The list goes on and on, but I think that AI has a huge opportunity to grow the work that Doge has done as it relates to seeking out waste, fraud, and abuse.
00:40The main thing that they did early on was to get all the government agencies to communicate using the same language, to code the same,
00:48to then be able to create transparency and track data from all the different government agencies.
00:53And that effort was really the beginning, because now that everything goes through Treasury, now that we can track payment systems, we now have to layer on technology.
01:01And some of that is fairly simple.
01:03For example, I've introduced the TABS Act.
01:05The TABS Act, Timely Accurate Benefits.
01:07And what that act does is it uses a pilot program that was done in Missouri, where they, instead of using antiquated ways of confirming income eligibility for Medicaid,
01:20they use third-party website to verify eligibility immediately, and then the second you're no longer eligible, you are removed from whatever benefit it is.
01:32And so that resulted in a 17% reduction.
01:35So the way this technology works is you say, all right, I'm on hard times.
01:39I want to get whatever government benefit.
01:41And they say, all right, well, you've got to log into your bank account.
01:43And then when you log into your bank account, it says, okay, you have a Venmo, you have a Cash App, you have a Zelle, you have to log into that, too.
01:49And then immediately they say you're eligible or you're not eligible.
01:52And so you're actually getting benefits faster than normal, and this has a huge potential to save money.
01:59And that 17% of Medicaid is about $150 billion if you do it over the entire federal government.
02:05And, you know, one thing that the One Big Beautiful Bill did was instead of confirming eligibility once a year, which I think is ridiculous, we're now confirming eligibility twice a year.
02:14Well, why don't we confirm eligibility every day using technology?
02:17This is not hard.
02:18The technology exists.
02:19All we have to do is implement it.
02:20So that's one way that we can further find waste, fraud, and abuse, because I think we can all agree that if you're not eligible because you make too much money,
02:28or you're not eligible because, and this is the next step, because you're not the person who you say you are, then you shouldn't get government benefits.
02:35So I'm working on tabs, too, which is using technology to confirm you are who you say you are.
02:41And this is just using billions of publicly available pieces of data that, you know, for example, if you have an email address that was created in the last year and you're 45 years old, you're probably not who you say you are.
02:52If you have three different phone numbers, that's not normal.
02:54So they're able to use technology, publicly available information to confirm an individual requesting benefits is who they say they are.
03:02And honestly, that would not only help means-tested programs, it would help any government program.
03:09If you're saying you are who you say you are for Social Security, I mean, that's not means-tested, but we could use it to seek out waste, fraud, and abuse.
03:15So those are two things, two ways we can use technology to reserve government benefits for American citizens that deserve them.
03:27How can we grow that effort?
03:29How can we build on that effort using AI?
03:32Mr. Barak Tari?
03:39Sir, I think those two examples indicate the potential that we have in AI adoption for government services.
03:45I think we've got to do three things.
03:47The underlying issue that we have here is that we need to upgrade or modernize our IT infrastructure.
03:53Because you cannot deploy AI if you don't have the necessary infrastructure underneath that.
03:58And so to do that, we need to do three things.
04:00Number one is we need to upgrade our platforms.
04:02Number two is we have to update our policies, obviously.
04:05And number three is we have to work on retraining and re-skilling our people.
04:10Ms. Miller, what are your thoughts?
04:11There's a lot we can do in this area.
04:13I mean, this is what I, the top work company that I created just does this.
04:17And mining open source intelligence to identify whether somebody is who they say they are is the lowest hanging fruit and the best use cases we can do in the fraud space for government programs.
04:27And not just means-tested programs.
04:29Every grant program, we put a trillion dollars in federal money out every year to states for grants.
04:35And we have very little understanding of where that money is going.
04:38But we can use AI and open source intelligence to very quickly identify if it's a shell company or even an, you know, an organized crime ring that's actually getting that money.
04:47And on the organized crime front, a lot of these involve changing addresses once they request benefits and things of that nature.
04:55And AI could easily say, oh, it's weird.
04:57There's a P.O. box in Eastern Europe that gets 43 checks a day.
05:01That's weird.
05:02A hundred percent.
05:02There's geolocation.
05:04There's all kinds of behavioral biometrics that AI can flag.
05:07You can use, you know, probably dozens of different data inputs to identify fraud today using AI.
05:13And this is not a partisan issue.
05:15I think everyone can agree that only people that deserve benefits, that are entitled to benefits, should get them.
05:19So, again, I think using technology to make sure that we're being wise with our taxpayer dollars is something that is a no-brainer.
05:27And I look forward to working with my colleagues across the aisle to do just that.
05:30Thank you, Madam Chair.
05:30I'll go back.
