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The saying “Mining is the law of life. Those who only look to farming, or exploring, are certain to miss the diamonds” is popular in the game Minecraft to emphasize the importance of gathering resources. The Department of Justice (DOJ) is leveraging outsiders with resources in a new initiative aimed at identifying fraud. DOJ announced the Fraud Oversight through Careful Use of Statistics (FOCUS) initiative which is designed to prioritize effective data miners who file qui tam complaints under the False Claims Act (FCA).1 Data miners analyze massive datasets to attempt to identify certain activity and anomalies that might indicate fraud. Just as the healthcare industry is using artificial intelligence (AI) and machine learning (ML) to assist in medical care, data miners and healthcare entities can use these new tools to process vast datasets to identify non-obvious patterns of fraud as well.
Not being traditional insiders or whistleblowers, data miners may not be an original source of information forming the basis of a complaint. A complaint under the FCA is barred if it is based on publicly disclosed claims when the Relator is not an original source.2 Whether data miners are relying on publicly disclosed information has been and will be the subject of litigation if this initiative takes off. In a recent decision, a federal district court dismissed a complaint based on data gleaned from a government funded public website regarding pandemic relief funds and programs based on the public disclosure bar.3
Another hurdle to overcome when FCA complaints are brought by data miners and not insiders is meeting the heightened standard of pleading fraud, or false claims, as required by Federal Rule of Civil Procedure 9(b).4 Courts have declined to relax the 9(b) pleading standard in FCA cases to allow a case to go forward and permit discovery by an outsider when the FCA is designed to encourage insiders to come forward with information necessary to prevent fraud on the government.5 Courts require the who, what, when, where, and how of the alleged fraud, which may be difficult for data miners to provide.6
DOJ recognizes these hurdles as the announcement encourages data miners to meet with DOJ and demonstrate their ability to support legally sufficient allegations among other things. If data miners can leverage AI and machine learning to support FCA complaints, so too should the industries that contract with the government to make sure they don’t miss any diamonds.
Footnotes
1. Civil Division Announces FOCUS Initiative for Data Miners Filing Qui Tam Complaints, April 30, 2026, www.justice.gov/opa/pr/civil-division-announces-focus-initiative-data-miners-filing-qui-tam-complaints; 31 U.S.C. §§ 3729-3733 (False Claims Act).
2. 31 U.S.C. § 3730(e)(4).
3. U.S. ex rel. Relator LLC v. Kootstra, et al., Case No. 1:22-cv-00924 (E.D. Cal. Aug. 6, 2024).
4. Fed. R. Civ. P. 9(b) states: “In alleging fraud or mistake, a party must state with particularity the circumstances constituting fraud or mistake.”
5. See U.S. ex rel. Ebeid v. Lungwitz, 616 F.3d 993, 999 (9th Cir. 2010).
6. See, e.g., U.S. ex rel. Jacobs v. Walgreen Co., No. 21-20463, 2022 WL 613160, at *1 (5th Cir. Mar. 2, 2022)(“conclusory allegations that do not provide specifics as to the ‘who, what, when, where, and how of the alleged fraud’ are insufficient under Rule 9(b)”) (quoting Colquitt, 858 F.3d at 371).
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