A new initiative called Fraud Oversight through Careful Use of Statistics (FOCUS) has been launched by the DOJ’s Civil Division launched
It is aimed at whistleblower complaints generated through data mining, as outside analytics firms play a growing role in False Claims Act (FCA) litigation. The FCA allows private relators to identify false claims submitted to the government in exchange for a share of the recovery, in what is known as a qui tam proceeding.
Since the False Claims Act went into force amid the Civil War, most relators have been employees, contractors, or competitors with direct knowledge of alleged misconduct. Data mining relators, however, generally have no inside connection to the defendant and instead rely on statistical patterns in public claims, loan, or procurement data.
Essentially, FOCUS signals that the DOJ is open to meeting with data miners before or around when they first file their claims, though it is not mandatory. The DOJ stated that data mining whistleblowers should be prepared to explain their methodology, identify legal violation(s), rule out alternative explanations, and provide a practical investigative roadmap.
The DOJ said qui tam cases have increased sharply in recent years, particularly in cases involving fraud in federal healthcare programs and pandemic relief loans, both of which have also been government enforcement priorities under the second Trump administration. The agency received 980 qui tam complaints in fiscal year 2024, nearly 1,300 in fiscal year 2025, and more than 780 so far in fiscal year 2026.
Since fiscal year 2024, data miners have filed more than 45% of all qui tam complaints, and have been large drivers of pandemic loan fraud identification in particular.
Lingering concerns
The DOJ said data analytics can be a useful tool for detecting fraud, but warned that detecting statistical anomalies alone may not be enough to receive a reward.
The agency’s concern is that some data-driven complaints may identify unusual billing or funding patterns without showing actual fraud. That distinction is critical under the FCA, which requires more than evidence that a defendant was an outlier, and instead must plausibly allege that false claims were submitted to the government with scienter, among other elements.
Courts have already shown skepticism toward some data-driven FCA cases. In an unpublished memorandum, Integra Med Analytics v Providence Health & Services, the Ninth Circuit rejected a qui tam complaint based largely on statistical comparisons of Medicare billing.
The court said the relator had not ruled out an obvious lawful explanation; that the defendant may simply have been more effective at coding reimbursement claims.