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

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

Stephan Wolf - Global Banking | Finance

Stephan Wolf

01 November 2022 – Basel – 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.

The tool delivers a range of benefits to both the organization and the broader global marketplace. These include:

  • Automating the standardization of unstructured data (entity legal form as part of the organization’s name), fostering greater data quality.
  • Overcoming legal form data classification problems stemming from, for example, language variations and abbreviation inconsistencies and promoting greater insight and transparency into the global marketplace.
  • Presenting the legal form of an entity in a machine-readable format which can be utilized by AI tools and in other digitized business processes and applications.
  • Bypassing the risks and limitations associated with manual engagement with data, including time, inefficiency, human error, and high administrative costs.

By creating richer data sets with improved categorization of legal entities, the new tool promotes greater insight and transparency into the global marketplace and works in tandem with the LEI to create a globally consistent data set.

Stephan Wolf, CEO, GLEIF, comments: “GLEIF is providing the open-source data library to enable other organizations to integrate this ISO standard into their data without deploying costly and inefficient manual processes. This will help to improve data quality on a broad scale by enabling the swift adoption of the universal Entity Legal Form codes. Through this initiative, we have both improved the quality of LEI data and produced a highly trained machine learning tool which we can now make freely available as a public good.”

Prof. Dr. Damian Borth is Co-founder of Sociovestix Labs and a director of the Institute of Computer Science at the University of St.Gallen, where he holds a full professorship in Artificial Intelligence and Machine Learning (AIML). He adds: “The automatic identification of the legal form of a company and its linkage to ELF codes is fundamental to many successive tasks in the industry. The released Python library “Legal Entity Name Understanding” does this by encapsulating the global knowledge of 175 jurisdictions into one unique open source tool – free to use for everybody who appreciates data quality.”

Sameena Shah, AI Research Executive and Client Onboarding Chief Transformation Officer at J.P. Morgan, comments: “J.P. Morgan already utilizes the entity relationship data in the LEI database to improve our detection of umbrella structures in funds. We’re excited to engage further with GLEIF and evaluate the new tool for automated ELF code detection. We applaud GLEIF’s commitment to enhancing data quality and its decision to make this tool freely available to any organization seeking to benefit from AI solutions.”

The ‘Entity Legal Forms (ELF) Code List’ contains more than 3,250 Entity Legal Form codes spanning more than 175 jurisdictions, including legal forms and types in their native language, such as limited liability companies (Ltd), Gesellschaft mit beschränkter Haftung (GmbH) or Société Anonyme (SA). GLEIF has integrated ELF codes into the standardized set of reference data on a legal entity available within the Global LEI Index, an open data set. The tool has been used to retrospectively apply these codes to LEI records where they were absent. The inclusion of ELF codes within LEI data further enhances the business card information included in each of the more than two million LEIs used globally today.

Global Banking & Finance Review


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