Chairman Comer Presses for Solutions After Massive Fraud Exposed in Minnesota’s Social Services System

WASHINGTON-House Committee on Oversight and Government Reform Chairman James Comer (R-Ky.) questioned witnesses at today's Government Operations Subcommittee hearing, "Curbing Federal Fraud: Examining Innovative Tools to Detect and Prevent Fraud in Federal Programs," about ways to prevent the massive theft of taxpayer dollars, including schemes exposed in Minnesota under Governor Tim Walz's watch.

Last week, the House Oversight Committee held a hearing on misuse of federal funds in Minnesota with witnesses who sounded the alarm on massive fraud in the state's social service programs. The witnesses testified how fraudsters stole an estimated $9 billion from Minnesota's social services system, yet Governor Walz, Attorney General Ellison, and Minnesota Democrats ignored warnings, failed to act, and retaliated against whistleblowers.

Below are Chairman Comer's remarks and exchanges with witnesses.

Chairman Comer: "Last week, the House Oversight and Government Reform Committee held a hearing to examine the massive fraud in Minnesota's social services program that resulted in more than $9 billion being stolen from American taxpayers.

"Much of this fraud occurred because there were not proper safeguards put in place to prevent fraud before the funds were paid. There was also too little oversight of how the money was used once it went out the door to recipients. During the Biden Administration, Democrats rushed out federal funds with virtually no safeguards, resulting in massive theft of taxpayer dollars. Republicans repeatedly warned that the absence of guardrails would invite waste, fraud, and abuse.

"Identifying fraud before money goes out the door is necessary to prevent any further repeat of these failures and protect taxpayer dollars. We need to get ahead of the criminals, continue to help the Department of Justice in arresting, prosecuting, and jailing those responsible, and ensure federal programs serve those who are truly in need.

"This Committee and Congress are ready to assist with these efforts."

Chairman Comer: "Mr. Dieffenbach, how could PRAC tools and analysis have been used to stop the large identity and eligibility schemes that defrauded programs in Minnesota?"

Ken Dieffenbach, Executive Director, Pandemic Response Accountability Committee: "The hallmark of most fraud schemes is that people hide information. So leveraging data analytics allows us to see patterns, trends, anomalies, hidden connections to shine a bright light on what's actually happening. That is the path forward. So we have to assemble the right data, the right team, the right tools, which we already have at the PRAC, thanks to your support. We just need to think more about the jurisdiction of how we're employing those tools. But data is the solution."

Chairman Comer: "Ms. Miskell, given that Treasury is the last stop before payments from federal programs get executed, what authorities would help the Department identify and stop high-risk payments for additional agency review?"

Renata Miskell, Deputy Assistant Secretary for Accounting Policy & Financial Transparency, U.S. Department of Treasury's Bureau of Fiscal Service: "Thank you, Chair Comer. We are, as I mentioned, implementing a number of payment verification processes. So we're applying the technique of trust but verify doing some basic checks before agencies can certify a payment. One of the pieces that we are missing is the ability to ping authoritative federal databases to confirm a Pay ID, such as a Tax Identification Number or a Social Security Number. So we already received the data. We just can't verify it. So there are a number of databases that would help.

Chairman Comer: "How does Treasury partner with states to prevent fraud in state-administered federal benefits programs? Are there ways that Treasury could increase or enhance their assistance? And I'm sure Minnesota, wants no assistance, based on what I've determined thus far in our investigation. But how do you partner with states that that want to work to prevent fraud?"

Ms. Miskell: "'Do Not Pay'. Thanks to the payment integrity improvement act of 2019, authorizes Treasury to provide do not pay services to states that administer federal funds. However, it has been underutilized. We think that it can be part of a multi-layered approach. So things like Do Not Pay before a state issues payments to subrecipients would be very useful. We can also work to address some of their common challenges by adding additional data. We know this works."

Chairman Comer: "Mr. Thomas, how can AI and machine learning be used to detect and prevent large-scale fraud schemes? What types of anomalies do these tools flag for investigators to follow up on?"

Sterling Thomas, Chief Scientist, U.S. Government Accountability Office: "So, machine learning, I mean, all data science algorithms inclusive of machine learning and AI are going to produce indicators of fraud. It's critically important in each of the programs we've talked about today. 'Does this [have] a fraud investigator, an analyst who's an expert in the tools, techniques and technologies that fraudsters use that to look at the data coming out?' So the types of things you're looking for are just as what were mentioned earlier, patterns of behavior that don't fit the expected patterns of behavior of someone who's using the money for the intended purpose or for the intended program design. We talk about GAO, and we've published this, and we support the federal government and states and local governments in using our fraud risk framework, which is designed to help them develop these indicators for a fraud risk management plan, which would then feed into algorithms, machine learning, AI, other data science methods, all acceptable that could then be used to track and monitor potential fraud while the program is in execution. It's the purpose of it is you design the tool to find the behaviors that you want to get rid of."