Transformer-based Entity Legal Form Classification
Alexander Arimond Mauro Molteni Dominik Jany Zornitsa Manolova Damian Borth Andreas G. F. Hoepner
We propose the application of Transformer-based language models for
classifying entity legal forms from raw legal entity names. Specifically, we
employ various BERT variants and compare their performance against multiple
traditional baselines. Our evaluation encompasses a substantial subset of
freely available Legal Entity Identifier (LEI) data, comprising over 1.1
million legal entities from 30 different legal jurisdictions. The ground truth
labels for classification per jurisdiction are taken from the Entity Legal Form
(ELF) code standard (ISO 20275). Our findings demonstrate that pre-trained BERT
variants outperform traditional text classification approaches in terms of F1
score, while also performing comparably well in the Macro F1 Score. Moreover,
the validity of our proposal is supported by the outcome of third-party expert
reviews conducted in ten selected jurisdictions. This study highlights the
significant potential of Transformer-based models in advancing data
standardization and data integration. The presented approaches can greatly
benefit financial institutions, corporations, governments and other
organizations in assessing business relationships, understanding risk exposure,
and promoting effective governance.