Improving Record Linkage for Counter-Threat Finance Intelligence with Dynamic Jaro-Winkler Thresholds

Counter-Threat Finance Intelligence (CTFI) is a discipline within the U.S. intelligence enterprise that illuminates and prosecutes terrorist financiers and their supporting networks. Relying on voluminous, disparate financial data, efficient and accurate record linkage is critical to CTFI, as succes...

Full description

Saved in:
Bibliographic Details
Published in2019 Winter Simulation Conference (WSC) pp. 2467 - 2478
Main Authors Kloo, Ian, Dabkowski, Matthew F., Huddleston, Samuel H.
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.12.2019
Subjects
Couplings
Finance
Government
Terrorism
Online AccessGet full text

Cover

More Information
Summary:Counter-Threat Finance Intelligence (CTFI) is a discipline within the U.S. intelligence enterprise that illuminates and prosecutes terrorist financiers and their supporting networks. Relying on voluminous, disparate financial data, efficient and accurate record linkage is critical to CTFI, as successful prosecutions and asset seizures hinge on the association of designated, nefarious entities with financial transactions falling under U.S. jurisdiction. The Jaro-Winkler (J-W) algorithm is a well-known, widely used string comparator that is often employed in these record linkage problems. Despite J-W's popularity, there is no academic consensus on the threshold score at which strings should be considered likely matches. In practice, J-W thresholds are selected somewhat arbitrarily or with little justification. This paper uses a simulative approach to identify optimal J-W thresholds based on an entity pair's string lengths, thereby improving the lead-discovery process for CTFI practitioners.
ISSN:1558-4305
DOI:10.1109/WSC40007.2019.9004945