White Collar Crime Risk Zones
with Sam Lavigne and Francis Tseng
White Collar Crime Risk Zones uses machine learning to predict where financial crimes will happen across the U.S. The system was trained on incidents of financial malfeasance from 1964 to the present day, collected from the Financial Industry Regulatory Authority (FINRA), a non-governmental organization that regulates financial firms.
The system uses industry-standard predictive policing methodologies, including Risk Terrain Modeling and geospatial feature predictors, which enables the tool to predict financial crime at the city-block-level with an accuracy of 90.12%.
Predictive policing apps are designed and deployed to target so-called “street” crime, reinforcing and accelerating destructive policing practices that disproportionately target impoverished communities of color.
Unlike typical predictive policing apps which criminalize poverty, White Collar Crime Risk Zones criminalizes wealth.
To learn more about our technical methodology, read our white paper.