Machine Learning: New Open Source Tool Developed by GLEIF and Sociovestix Labs Enables Organizations Everywhere to Automatically Detect and Standardize Legal Forms

J.P. Morgan has successfully tested the new machine learning tool and is currently evaluating its integration in its data pipeline

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In line with its commitment to advancing the availability of open, accurate, and relevant entity identification data around the world, the Global Legal Entity Identifier Foundation (GLEIF) has collaborated with Sociovestix Labs to create a machine learning tool that recognizes an entity’s specific legal form and automates the assignment of its corresponding Entity Legal Form (ELF) code. The ‘Entity Legal Forms (ELF) Code List’ is based on the ISO standard 20275 ‘Financial Services – Entity Legal Forms (ELF)’ and assigns a unique alpha-numeric code of four characters to each entity legal form.

An entity's legal form is a crucial component when verifying and screening organizational identity. The wide variety of entity legal forms that exist within and between jurisdictions, however, has made it difficult for large organizations to capture legal form as structured data. The new tool, trained on GLEIF’s Legal Entity Identifier (LEI) database of over two million records, will allow banks, investment firms, corporations, governments, and other large organizations to retrospectively analyze their master data, extract the legal form from the unstructured text of the legal name and uniformly apply an ELF code to each entity type, according to the ISO 20275 standard.

Tier-one global bank, J.P. Morgan, has successfully tested the new tool and is currently evaluating its integration in its data pipeline.

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