Financial entities are often referred to with ambiguous descriptions and identifiers. To tackle this issue, the Financial Entity Identification and Information Integration (FEIII) Challenge requires participants to automatically reconcile financial entities among three datasets: the Federal Financial Institution Examination Council (FFIEC), the Legal Entity Identifiers (LEI) and the Security and Exchange Commission (SEC). Our approach is based on the combination of different Naive Bayes classifiers through an ensemble approach. The evaluation on the Gold Standard developed by the challenge organizers shows F1-scores that are above the average of the other participants for the two proposed tasks.

An ensemble approach to financial entity matching for the FEIII 2016 challenge / Palumbo, Enrico; Rizzo, Giuseppe; Troncy, Raphaël. - ELETTRONICO. - 01-:(2016), pp. 1-2. (Intervento presentato al convegno 2nd International Workshop on Data Science for Macro-Modeling, DSMM 2016 tenutosi a usa nel 2016) [10.1145/2951894.2951906].

An ensemble approach to financial entity matching for the FEIII 2016 challenge

PALUMBO, ENRICO;RIZZO, Giuseppe;
2016

Abstract

Financial entities are often referred to with ambiguous descriptions and identifiers. To tackle this issue, the Financial Entity Identification and Information Integration (FEIII) Challenge requires participants to automatically reconcile financial entities among three datasets: the Federal Financial Institution Examination Council (FFIEC), the Legal Entity Identifiers (LEI) and the Security and Exchange Commission (SEC). Our approach is based on the combination of different Naive Bayes classifiers through an ensemble approach. The evaluation on the Gold Standard developed by the challenge organizers shows F1-scores that are above the average of the other participants for the two proposed tasks.
2016
9781450344074
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11583/2676445
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