Fraud costs the federal government hundreds of billions of dollars every year. But before agencies can recover those taxpayer dollars or hold bad actors accountable, they have to sort through an overwhelming amount of evidence to prove whether fraud actually occurred.

If you ask investigators what makes these types of cases so difficult, they’ll likely tell you it’s the massive amounts of disparate data involved. A single investigation can include years of emails, financial records, contracts, text messages, surveillance footage, handwritten notes and data pulled from multiple government systems.

This challenge has become even more significant since the pandemic, when federal agencies were responsible for distributing unprecedented levels of financial assistance to individuals, businesses and nonprofit organizations. Those programs generated an enormous amount of records and, in some cases, created significant opportunities for fraud.

In some investigations, agencies have spent more than six months with teams of contractors tracing relationships between bank accounts tied to suspected fraud schemes. That kind of work can cost hundreds of thousands of dollars. Today, the exact same data can be analyzed using AI tools in minutes.

As a result, agencies have begun looking beyond traditional investigative methods and legacy case management systems, recognizing that while these systems are designed to store records, they are not built to analyze them. Before investigators can even begin analyzing a case, documents have to be digitized and evidence from multiple sources brought together into a single place where they can be searched and compared.

One example of a tool that can do this is TimePilot by Tranquility AI, which allows investigators to synthesize evidence rather than simply store it. Investigators can bring together emails, financial records, reports, spreadsheets, images, videos, etc. into a single platform where it can all be analyzed at once. That allows investigators to identify relationships, build timelines, surface patterns and quickly move from individual pieces of evidence to a clearer understanding of the case.

David Gersten, director of ecosystem development at Tranquility AI and former senior executive at the Department of Homeland Security, says the biggest misconception about AI is that it’s meant to replace investigators. “AI doesn’t replace investigative expertise,” Gersten says. “It takes the same types of evidence investigators have always relied on and rapidly brings the hidden patterns to the surface.”

That philosophy has become increasingly important as agencies work through questions surrounding AI governance and courtroom admissibility. Judges and investigators alike want to understand how technology reached a particular conclusion. They want audit trails, transparency and the ability to verify every finding rather than simply accepting an answer generated by a computer.

As a result, many agencies are treating AI less as an automated decision-maker and more as another investigative tool. Instead of spending weeks manually organizing evidence, investigators can devote that time to interviewing witnesses, following new leads, corroborating evidence and validating findings. The time AI saves gives investigators more time to apply their expertise and make informed decisions.

The impact is already being felt by state and local law enforcement. After using TimePilot for several years, Sheriff Max Dorsey of Chester County, South Carolina, calls it “a game changer,” adding, “It found a needle in a haystack of data during a highly complex investigation.”

The same shift is underway at the federal level. As fraud investigations grow in size and complexity, agencies are recognizing that traditional investigative methods alone are no longer enough. For years, much of the federal government’s fraud response followed a “pay and chase” model, where benefits were distributed first and investigations began only after taxpayer dollars had already gone out the door. That approach placed greater emphasis on recovering losses and building cases after the fact than on preventing fraud from occurring in the first place.

Policymakers are now placing greater emphasis on identifying suspicious activity before payments are made while continuing to aggressively investigate existing fraud. The creation of the Department of Justice’s National Fraud Enforcement Division and the White House Task Force to Eliminate Fraud reflects that shift and signals that fraud prevention and enforcement have become a higher priority across the federal government.

Achieving those goals depends on investigators being able to quickly connect information across multiple data sources and focus their attention where it matters most. AI gives teams the ability to work through far larger volumes of evidence without proportionally increasing staffing. This can help agencies keep pace with growing caseloads while making better use of existing resources. No other technology can analyze the volume and variety of investigative data at the speed required for today’s complex fraud investigations.

None of that eliminates the need for experienced investigators. If anything, the growing volume of digital evidence has made their expertise even more valuable. AI can organize information, surface patterns and identify potential connections, but investigators are still responsible for validating the evidence and making critical decisions. Agencies that successfully combine AI with experienced investigators will be better equipped to combat the increasingly sophisticated fraud we see today.

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