Scalable, Trie-based Approximate Entity Extraction for Real-Time Financial Transaction Screening
Emrah Budur
TL;DR
This paper introduces a scalable, trie-based method for real-time, approximate entity extraction from financial messages to help institutions detect terrorism affiliations without disrupting legitimate transactions.
Contribution
It presents a novel scalable approach combining trie data structures with approximate matching for real-time entity extraction in financial screening.
Findings
Enables real-time processing of large transaction volumes.
Achieves high accuracy in detecting terrorism-related entities.
Reduces false positives in transaction screening.
Abstract
Financial institutions have to screen their transactions to ensure that they are not affiliated with terrorism entities. Developing appropriate solutions to detect such affiliations precisely while avoiding any kind of interruption to large amount of legitimate transactions is essential. In this paper, we present building blocks of a scalable solution that may help financial institutions to build their own software to extract terrorism entities out of both structured and unstructured financial messages in real time and with approximate similarity matching approach.
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Taxonomy
TopicsData Quality and Management · Data Mining Algorithms and Applications · Web Data Mining and Analysis