Arcane Tracer monitors $2.1 trillion in federal spending and maps financial networks in real time, surfacing fraud risk before investigators know where to look.
The data to catch government fraud has always been public. The tool to connect it automatically has not existed until now.
When Arcane Tracer flags an entity, it shows specific risk signals in plain English. Here is what each signal means, why it matters, and what it tells investigators to look at next. Hover over any badge in the feed above to see the explanation inline.
The Feeding Our Future fraud stole $350 million from a federal meal program and ran for three years. Every signal was in public data from the beginning. Here is the timeline.
Feeding Our Future was a nonprofit founded in 2016. It applied repeatedly to the Minnesota Department of Education for grants and was rejected each time for mismanagement and irregularities. The IRS revoked its nonprofit status in February 2020.
When COVID-19 pandemic relief funding opened with intentionally low barriers to entry, the organization began receiving millions in federal meal program payments almost immediately. Within months it was processing tens of millions per month through a growing network of shell sites.
By 2022, federal investigators uncovered a network of 79 connected entities — shared registered agents, overlapping officers, shell distribution sites. All of it traceable through public records that had been available since the first payment was made.
The fraud ran for three years. Not because the data was hidden. Because no one had a tool to connect it automatically.
Arcane Tracer's anomaly detection would have flagged Feeding Our Future on its very first federal payment — a nonprofit with revoked IRS status, previously rejected for mismanagement, suddenly receiving millions. The full 79-entity network would have been mappable within minutes.
This is what an investigation looks like inside Arcane Tracer. Every connection is sourced from a public record.
Every data point in Arcane Tracer is sourced from official public databases. We do not scrape, estimate, or infer. If we show it, we can cite it.
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