AI Data Mining Is Changing FCA Enforcement

For decades, successful False Claims Act cases typically began with insider information from employees, competitors or other individuals with firsthand knowledge of alleged misconduct. Increasingly, however, FCA investigations are emerging from statistical anomalies identified through artificial intelligence-assisted analysis of publicly available data.

This shift is being driven by a growing class of qui tam relators known as data miners. Unlike traditional whistleblowers, data miners often have no employment relationship with — or firsthand knowledge of — the target company. Instead, they analyze public datasets for patterns that may suggest fraud against the government and seek to share in any resulting recovery. Notably, a relator need not have been personally harmed by the defendant's conduct to bring a qui tam action on behalf of the U.S.¹