A False Claims Act case used to conjure up an insider with evidence of fraud. Increasingly, it may start somewhere very different: a public dataset, a spreadsheet, and an algorithm looking for patterns.

In this episode, I’m joined by Tom Suddath and David Bender of Reed Smith to talk about data-driven FCA litigation and DOJ’s Focus Initiative—Broad Oversight Through Careful Use of Statistics—and what it could mean for healthcare enforcement in 2026 and beyond.

Healthcare is particularly fertile ground because Medicare and Medicaid generate enormous volumes of billing data. But spotting an unusual pattern and proving fraud are two very different things. We get into the role of Rule 9(b), the importance of pleading fraud with particularity, and perhaps the hardest question for a case built largely on statistics: How do you prove someone knowingly submitted a false claim?

We also talk about AI’s role on both sides. The same tools that can help uncover patterns and build a complaint can help defendants find counterexamples, test assumptions, and offer innocent explanations for seemingly suspicious data.

For healthcare organizations, there’s a practical lesson here too: the data being mined by potential relators can also be used internally to identify compliance problems before someone else does.

Jump in for a conversation about False Claims Act enforcement, healthcare fraud, data mining, AI, and what happens when an anomaly becomes the beginning of a fraud case.

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