CASE STUDY

Strengthening U.S. Borders withIntelligent Name Matching

BABEL STREET ANALYTICS

U.S. Customs and Border Protection (CBP) provides
security and facilitation operations at 328 ports of entry
throughout the country. CBP takes a comprehensive
approach to border management and control,
combining customs, immigration, border security,
and agricultural protection into one coordinated andsupportive activity. CBP staffers are responsible for
enforcing hundreds of U.S. laws and regulations. On atypical day, CBP processes aproximately one million
visitors.1

The Boston Marathon

The Challenge

either occasion. Why?

The U.S. Department of Homeland Security (DHS)
Targeting and Analysis Systems Program Office(TASPO) wanted to strengthen the screening systemused by the CBP. This system scans and matches

On that January day, there weremillions of names a day to secure U.S. borders andfacilitate lawful international trade and travel, while
enforcing U.S. laws and regulations.

flagged.2 When he returned in

Accuracy is the top priority of many missions using
human language technology. For CBP TASPO, ensuringthat people entering the country are properlyscreened ultimately saves lives.

spelled his last name “Tsarnayev.”

Proper screening requires accurate name matching
to flag possible hits on watchlists. To maximizethat accuracy, TASPO wanted to integrate different
approaches to name matching, so that it could benefit
from the strengths of each.

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In 2011, the FBI issued a detain
alert for Tamerlan Tsarnaev, wholater became known as one of
the two Boston Marathon bomber
suspects in April 2013. In January
2012, Tsarnaev traveled to Russia,
and in July 2012, he returned toBoston. He was not detained on

too many matching watchlist
names for airport agents toprocess, and Tsarnaev wasn’tJuly, the January detain alert
had expired, and a new alert
Because his travel documents
said “Tsarnaev,” his name was notmatched to the FBI alert.3

Some older name matching systems use a “bruteforce” approach, generating all possible spellingvariations in an attempt to increase possible
matches. This approach has weaknesses, such as
dealing with not-yet-seen names and names withadded or missing spaces.

The Solution
CBP tested and added Babel Street Analytics
Name Match, a human language technology-based
solution that uses machine learning and carefully
weighted algorithms to understand phonetic

variations, nicknames, formation patterns, and newvariations within each language. It also links names
across different languages and scripts, using
knowledge of how a sound in one language may be
spelled in a different one.

The Impact
By integrating Name Match technology into its
screening system, CBP added greater namematching accuracy — and ultimately greater
security — to its border protection efforts.

(1) U.S. Customs and Border Protection, “Traveler and Conveyance Statistics,” https://www.cbp.gov/newsroom/stats/travel(2) p. 29-30 House Homeland Security Committee Report “The Road to Boston: Counterterrorism Challenges and Lessons from the Marathon
Bombings,” March 2014, prepared by the Majority Staff of the Committee on Homeland Security. https://irp.fas.org/congress/2014_rpt/boston.pdf(3) p. 33 Ibid

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