White Collar Crime Risk Zones

White Collar Crime Risk Zones

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.”

— White Collar Crime Risk Zones,” Sam Lavigne, Francis Tseng, and Brian Clifton, The New Inquiry

Use it online and read the white paper.

Related:

“Why big-data analysis of police activity is inherently biased,” William Isaac and Andi Dixon, The Conversation

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Short link: http://wp.me/p6sb6-pJ4

Image by Mike Licht. Download a copy here. Creative Commons license; credit Mike Licht, NotionsCapital.com

Comments are welcome if they are on-topic, substantive, concise, and not obscene. Comments may be edited for clarity and length.

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