In July 2020, the Small Business Administration published a database showing the award and forgiveness of hundreds of billions of dollars in money dispensed under the Paycheck Protection Program. Soon thereafter, the phones of whistleblower lawyers throughout the country began to ring off their hooks, as disgruntled employees, spurned lovers, and direct competitors sought to report entities that had received PPP funds under false pretenses. This is an example of a data-driven qui tam False Claims Act case: where an individual develops a whistleblower case from publicly available data.
As the Government makes more billing, lending, and contractor data available to the public, whistleblower cases from outsiders have become more common. “Data miners” with no specific knowledge of the defendants identify “outliers” in the data that suggest fraud. For example, one industrious attorney scoured the PPP data and filed more than thirty cases alleging that companies had applied for multiple loans in the same amounts. He did not need to know the loan recipient to make this basic allegation.
Although he had some success, many of his cases were dismissed because, simply, you don’t know what you don’t know. It is easy to find outliers in the data, but it is not always as clear why they are outliers. For example, a healthcare provider billing for more hours than exist in a day may actually be supervising a team of nurse practitioners. Suddenly, claims that initially appear to be impossible are easily explained.
Similarly, data miners often lack evidence of defendants’ knowledge that they submitted false claims. This is called scienter, and it is a required element of a False Claims Act violation. While this evidence can be obtained through witnesses, data miners cannot generally identify which witnesses have that information and would be willing to cooperate with a government investigation.
Ideally, your data-driven case includes some additional inside information, such as knowledge that the company had fired most of its staff prior to certifying to the PPP loan or that the doctor works as a solo practitioner. Alternatively, you may have an area of expertise that enables you to uncover fraud that is not otherwise obvious from the raw data. For example, a specialist may recognize that a healthcare provider is billing a high-reimbursing Medicare code without also billing for any of the cheaper, prerequisite procedures.
It is important that you can give the Government some evidence that the data miner did not obtain from the Government in the first place. Even minor additions can make a big difference when trying to avoid a reduced relator’s share or outright dismissal, which the False Claims Act statute permits when a relator bases a case on publicly disclosed information. If you are going to file a data-driven case, it can be a good idea to hire an investigator to dig up dirt and basic background information.
If you are relying entirely on public data to assert your claims, it can be difficult to convince the Government to prioritize and intervene in your case. Data is the start—not the end—of a good False Claims Act case.
Written by Jason Marcus of Bracker & Marcus LLC