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HS Today - How Do We Find the Real Criminals?

HS Today - How Do We Find the Real Criminals?

This article was originally published in HSToday.

Investor Warren Buffett once said in defense of JPMorgan CEO Jamie Dimon, “If a cop follows you for 500 miles, you’re going to get a ticket.”

Buffett was speaking colorfully to make a point, but his words ring true when focusing on the meaning and significance of law enforcement. Ultimately, law enforcement success requires balancing time, money, threat, and consequence. Sophisticated criminal organizations continue to adapt and evolve. Solutions for law enforcement that can learn and therefore produce high level leads within a defined set of priorities can balance these challenges and mitigate against the risk of writing “tickets” that fall short of the noble mission of policing.

Most of the 10 million arrests made every year are for noncriminal or low-level offenses. This is similar to the problem the financial industry faces, in which compliance professionals feel pressure to submit “more” Suspicious Activity Reports (SARs) to the U.S. Treasury, without a way to measure “better” SARs. The Financial Crimes Enforcement Network (FinCEN) receives more than two million SARs per year, and much less than 5% of these ever contribute to public safety. As Chief of Staff at US Customs and Border Protection (CBP), we faced similar challenges in screening millions of travelers to identify those that posed a legitimate threat.

The question, of course, is how do public security professionals discover the most dangerous people so that they can invest their time in delivering the biggest impact? Drug dealers, sex traffickers, and other criminals succeed at larger-scale criminal acts only to the extent that they can remain unnoticed by law enforcement. One could argue that all laws require enforcement but acknowledge that some laws are more important than others seems right and fair. When I was in the military, I learned that “if everything is a priority, then nothing is a priority.”

Imagine if law enforcement professionals could screen a population of people to find risky patterns of behavior; prioritize cases based upon quality; and then move to investigation. Arrests would lead to greater public safety, greater return on investment and more meaningful careers for those working for the good of our families, neighborhoods, and communities. Additionally, such tools would enhance public trust by ensuring that investigations and operations balanced were drawn from stated law enforcement priorities.

Screening is the important idea -- discovery of the unknown and successful criminal. Proactively fighting crime means finding good leads; the good leads come from good screening technology. This technology didn’t exist five years ago, but it does today, and I have seen the impact firsthand. The GOST® Machine Learning platform (this author is the CEO of Giant Oak, Inc.), for example, has led law enforcement leads on child exploitation perpetrators, drug traffickers, money launderers, and terrorist financiers. In a recent independent test, a Machine Learning platform was shown to discovered 600% more criminal leads in a population than the leading name-matching list provider uncovered.

Law enforcement professionals should be given the tools they need to do their jobs well and focus on quality and not quantity when quantity does not move the needle on public safety. By focusing on screening to generate high quality leads prior to the investigation phase, law enforcement professionals can find the right balance and perform the noble mission of keeping us all safer and more secure.

Gary M. Shiffman, PhD, is an economist working to solve problems related to human violence. A Gulf War veteran and former Senate National Security Advisor, Chief of Staff at US Customs and Border Protection, DARPA Principal Investigator, and Georgetown University professor, he founded two technology companies, Giant Oak, Inc, and Consilient, Inc. He is the author of The Economics of Violence (2020), and his essays have appeared in media outlets such as The Hill, the Wall Street Journal, USA Today, TechCrunch, and others.

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