GLEIF releases machine learning tool for automation
01 November 2022 Switzerland
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The Global Legal Entity Identifier Foundation (GLEIF) has released a machine learning tool that recognises an entity’s specific legal form and automates the assignment of its corresponding Entity Legal Form (ELF).
In collaboration with Sociovestix Labs, the tool was created in line with GLEIF’s objective to advance the availability of open, accurate and relevant entity identification data globally.
The ELF Code List 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 organisational identity.
Due to the wide variety of ELFs, it has become difficult for large organisations to capture legal form as structured data, according to GLEIF.
The new tool will allow banks, investment firms and other large organisations to analyse 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.
J.P. Morgan has successfully tested the new tool and is currently evaluating its integration within the firm’s data pipeline.
GLEIF says the machine learning tool will provide benefits for the broader global marketplace, including fostering greater data quality through automating the standardisation of unstructured data. In addition to presenting the legal form of an entity in a machine-readable format, which can be utilised by AI tools and in other digitised business processes and applications.
CEO of GLEIF Stephan Wolf says: “GLEIF is providing the open-source data library to enable other organisations 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 ELF 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.”
Sameena Shah, AI research executive and client onboarding chief transformation officer at J.P. Morgan, adds: “J.P. Morgan already utilises the entity relationship data in the LEI database to improve our detection of umbrella structures in funds.
“We are 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 organisation seeking to benefit from AI solutions.”
In collaboration with Sociovestix Labs, the tool was created in line with GLEIF’s objective to advance the availability of open, accurate and relevant entity identification data globally.
The ELF Code List 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 organisational identity.
Due to the wide variety of ELFs, it has become difficult for large organisations to capture legal form as structured data, according to GLEIF.
The new tool will allow banks, investment firms and other large organisations to analyse 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.
J.P. Morgan has successfully tested the new tool and is currently evaluating its integration within the firm’s data pipeline.
GLEIF says the machine learning tool will provide benefits for the broader global marketplace, including fostering greater data quality through automating the standardisation of unstructured data. In addition to presenting the legal form of an entity in a machine-readable format, which can be utilised by AI tools and in other digitised business processes and applications.
CEO of GLEIF Stephan Wolf says: “GLEIF is providing the open-source data library to enable other organisations 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 ELF 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.”
Sameena Shah, AI research executive and client onboarding chief transformation officer at J.P. Morgan, adds: “J.P. Morgan already utilises the entity relationship data in the LEI database to improve our detection of umbrella structures in funds.
“We are 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 organisation seeking to benefit from AI solutions.”
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