If you follow False Claims Act (FCA) enforcement, you’ve probably noticed a shift over the last few years. The classic whistleblower – the insider, the employee who witnesses fraud firsthand and comes forward – is no longer the only game in town.
A new kind of relator has arrived: the data miner.
Data miners don’t work inside the companies they’re targeting. Instead, they analyze publicly available government datasets, looking for statistical anomalies that suggest fraud.
In theory, it’s a powerful model. The government generates enormous amounts of data, and patterns that might indicate fraud can hide in plain sight. A company billing a particular procedure at 30 times the national average, for instance, is a signal worth investigating.
And the model has clearly caught on. Total FCA jumped from around 980 in the 2024 fiscal year to roughly 1,300 in 2025. Data miners now account for approximately 45 percent of whistleblower-initiated (qui tam) FCA lawsuits. That is a dramatic shift in a short period of time.
But as artificial intelligence (AI) tools have made it easier to generate statistical analyses, the volume of data-driven filings has grown faster than their quality. Many cases rely on thin inferences that may have entirely lawful explanations. And every filing, regardless of merit, consumes finite U.S. Department of Justice (DOJ) resources.
That’s the backdrop for DOJ Civil Division’s April 30 announcement on the launch of its FOCUS initiative (Fraud Oversight...
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