Financial entity record linkage with random forests

Kunho Kim, C. Lee Giles

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations

Abstract

Record linkage refers to the task of finding same entity across different databases. We propose a machine learning based record linkage algorithm for financial entity databases. Record linkage on financial databases are essential for information integration on certain financial entity, since those databases do not have common unified identifier. Our algorithm works in two steps to determine if a pair of record is same entity or not. First we check with proposed rules if the record pair can be exactly matched after cleaning the entity name and address. Second, inspired by earlier work on author name disambiguation, we train a binary Random Forest classifier to decide the linkage. To reduce and scale the computation, this process is done only for candidate pairs within a proposed heuristic. Initial evaluation for precision, recall and F1 measures on two different linking tasks in the Financial Entity Identification and Information Integration (FEIII) Challenge show promising results.

Original languageEnglish (US)
Title of host publicationProceedings of the 2nd International Workshop on Data Science for Macro-Modeling, DSMM 2016 - In conjunction with the ACM SIGMOD/PODS Conference
PublisherAssociation for Computing Machinery
ISBN (Electronic)9781450344074
DOIs
StatePublished - Jun 26 2016
Event2nd International Workshop on Data Science for Macro-Modeling, DSMM 2016 - San Francisco, United States
Duration: Jul 1 2016 → …

Publication series

NameProceedings of the ACM SIGMOD International Conference on Management of Data
Volume01-July-2016
ISSN (Print)0730-8078

Other

Other2nd International Workshop on Data Science for Macro-Modeling, DSMM 2016
CountryUnited States
CitySan Francisco
Period7/1/16 → …

All Science Journal Classification (ASJC) codes

  • Software
  • Information Systems

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