DHS’s predictive policing is unconstitutional, un-American and should be stopped

Recent reporting shows the Department of Homeland Security has been combining Americans' financial and travel data to generate leads for local police, a practice critics call predictive policing. Coin Center's Laz Pieper argues this approach treats transaction records as pre-emptive evidence, erodes Fourth Amendment protections, and risks politicizing financial surveillance.

By AI Newsroom· Reviewed by Pranav, Founder & Editor-in-ChiefPublished 44 minutes agoUpdated 44 minutes ago0 views
DHS’s predictive policing is unconstitutional, un-American and should be stopped

Why It Matters

The use of aggregated financial and location data to flag individuals for law enforcement raises constitutional and civil-liberty concerns because it shifts people from presumed innocent to perpetual suspects based on patterns in their private records. Given the volume of financial reporting to federal databases, such methods could enable broad, biased monitoring and enforcement.

Key Facts

  • Agency involved: U.S. Department of Homeland Security (DHS)
  • Practice described: Predictive policing using aggregated financial and travel data
  • Notable case 1: Kyle William Olson — Montana traffic stop instigated by Border Patrol Predictive Intelligence Targeting Team (PITT); DHS memo cited 'financial activity patterns commonly associated with illicit narcotics activity'
  • Notable case 2: Alek Schott — stopped and searched after alleged lane drifting; no drugs found; Schott is suing Bexar County, the sheriff, and deputies
  • Possible data source: Financial Crimes Enforcement Network (FinCEN) via reports required under the Bank Secrecy Act (BSA)

Recent disclosures indicate DHS units have been aggregating Americans' financial and travel records to identify people local law enforcement should investigate, a technique commonly labeled predictive policing. According to reporting cited by Coin Center’s Laz Pieper, these programs rely on large-scale data collection and analysis to generate tips rather than waiting for evidence of a specific crime.

The reporting highlights several concrete encounters where predictive leads appear to have prompted stops. In Montana, Border Patrol’s Predictive Intelligence Targeting Team flagged financial activity that it described in a DHS memo as matching patterns tied to illicit narcotics; that lead preceded a traffic stop of Kyle William Olson, where officers later found marijuana. In Texas, Alek Schott was pulled over and subjected to a vehicle search after federal monitoring of his travel patterns; no contraband was located and Schott has filed suit alleging Fourth Amendment violations.

Pieper contends the domestic use of financial records as investigative triggers is particularly fraught because the Treasury Department’s Financial Crimes Enforcement Network collects extensive reports under the Bank Secrecy Act, and banks often over-report suspicious activity to avoid regulatory risk. Those aggregated records, he argues, can be repurposed into surveillance tools that treat transaction histories as indicators of belief or association rather than as neutral commerce.

The piece also places U.S. practices in a broader context of financial censorship and account freezes used against protesters in other countries, citing Canada’s 2022 temporary account freezes and China’s bans on protest-related accounts. Pieper points to a 2024 Congressional report that described pressure on banks to look for spending patterns allegedly tied to extremism after the January 6, 2021 attack, using examples such as searches for purchases of 'religious texts.' He warns that relying on financial signals to infer politics or criminality risks turning routine transactions into grounds for state action without the fact-based suspicion required by the Fourth Amendment.

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