The ability to automatically detect the legal form of an entity is an important component in understanding exposure to risk and access to capital within financial services transactions. In this talk, held by the Global Legal Entity Identifier Foundation (GLEIF) in collaboration with Sociovestix Labs, we show how machine learning can be utilized to improve the detection and identification of legal forms with potentially unstructured legal entity names by assigning a standardized label – the ISO Standard 20275 Legal Entity Legal Form (ELF) code. We will be utilizing data from the Global LEI Index – an open, standardized and high-quality dataset containing information on over 2 million legal entities and their reference data, which was established as a public good. The LEI data leverages the ELF code standard which currently covers more than 3,100 Entity Legal Forms across more than 160 jurisdictions, thus providing a perfect open and labeled data set to help solve classification problems. We’ll discuss how our approach can deliver insights to help the financial sector foster greater data quality, achieve standardization and minimize costs.