Building a Transparent Future with Open Data, AI, and the Legal Entity Identifier
Building on the pioneering Transparency Fabric solution, the Global Open Data Integration Network (GODIN) is a new strategic initiative from GLEIF and Open Ownership. By harnessing the power of the LEI and artificial intelligence (AI), GODIN enhances transparency into organizational ownership. With new members joining, GODIN stands to grow as a collaborative ecosystem promoting open data interoperability and transparency.
Author: Zornitsa Manolova
Date: 2025-07-02
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Money laundering poses a systemic and seemingly intractable threat to the global economy. Despite public demand for greater corporate transparency and increased enforcement globally through stringent anti-money laundering (AML) and counter-terrorism financing (CTF) regulations, the challenges appear to be growing. According to estimates from the Bank for International Settlements (BIS), $3 trillion was subject to money laundering and terrorist financing in 2024.
Criminal enterprises evade detection by constructing complex laundering operations that span borders and jurisdictions, channelling and obfuscating illicit funds through an intricate web of financial institutions, legal entities, and individuals. But the already complex task of connecting the dots between the legal entities, beneficial owners, and sanctioned individuals to detect these networks and prevent illegal activity is made even harder by fragmentation, inconsistency, and inaccessibility across the underlying datasets.
As compliance spending continues to spiral in response to the challenge, it is clear that innovative and cost-effective methods for detecting suspicious transactions are urgently needed. To address this, GLEIF and Open Ownership launched the Global Open Data Integration Network (GODIN), a new strategic initiative poised to play a crucial role in overcoming the enduring challenge of data opacity and fragmentation in cross-border finance.
From Transparency Fabric to GODIN
The genesis of GODIN lies in the Transparency Fabric – a joint initiative introduced by GLEIF, Open Ownership, and OpenSanctions in 2023. It demonstrated how linking LEIs with beneficial ownership and sanctions data can create an open, interoperable system to better detect and prevent financial crime.
By combining authoritative datasets from multiple open data initiatives – and using the LEI as a standardized connector to ensure accuracy and consistency – the Transparency Fabric pioneered a new, global approach to due diligence and risk assessment that can be trusted by government bodies, regulators, and financial institutions, as well as journalists and civil society organizations (CSOs). The clear potential earned it a spot as a finalist in the G20 TechSprint 2023 – an annual competition recognizing the technology innovations addressing challenges faced by the global regulatory and banking community.
In 2025, the Transparency Fabric made a significant leap forward with the integration of artificial intelligence (AI). In addition to the use of structured datasets, Large Language Models (LLMs) were deployed to extract and analyze information from unstructured documents, such as company annual reports, to map complex ownership structures better. This drastically reduced manual effort while maintaining full transparency in data sourcing and auditability, with the LEI remaining as a linchpin to connect disparate data sources into a coherent and consistent global view.
Introducing GODIN: A Global Vision for Open Data Integration
In light of this proven success, GLEIF and Open Ownership officially launched GODIN in March 2025. GODIN is a strategic initiative that builds upon the Transparency Fabric to further strengthen global data interoperability and accessibility by aligning open data with globally recognized frameworks, such as the Global LEI System and the Beneficial Ownership Data Standard (BODS). By embedding LEIs into open data sources, it aims to create a transparent, efficient, and interconnected ecosystem that enhances the utility of data across the public and private sectors.
Building a Trusted and Transparent Digital Ecosystem
By linking verified identity data to ownership and sanctions information, and utilizing AI to enhance data extraction and analysis, GODIN demonstrates how combining open data, global standards, and advanced technology provides a path toward a more transparent and accountable digital economy. And looking ahead, there are vast opportunities to take further strides in making open data even more connected, accessible, and impactful.
This is why a core ambition of GODIN is to foster meaningful collaboration among all organizations that publish or steward open data, encouraging the co-development of tools and solutions that support a shared vision of the future where transparency is the default expectation. The network already comprises a diverse range of active founding members, including:
Global Energy Monitor (GEM) – Developing open, accessible data on energy infrastructure to support a sustainable energy future.
Supply Chain Data Exchange (SC-DEX) – A machine-readable schema enabling transparency of supply chain locations and organizational relationships.
Anti-Corruption Data Collective (ACDC) – Uniting journalists, analysts, and advocates to expose and combat global corruption.
Global Media Registry (GMR) – Promoting transparency and accountability in digital media through a non-profit, open data approach.
Now, GLEIF calls on all organizations publishing or working with open data, including government bodies, non-governmental organizations, non-profit organizations, academic institutions, and the private sector, to participate in GODIN to realize the promise of a more connected, transparent, and trustworthy data ecosystem. For more information and to get involved, visit godin.gleif.org.
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Zornitsa Manolova leads the Data Quality Management and Data Science team at the Global Legal Entity Identifier Foundation (GLEIF). Since April 2018, she is responsible for enhancing and improving the established data quality and data governance framework by introducing innovative data analytics approaches. Previously, Zornitsa managed forensic data analytics projects on international financial investigations at PwC Forensics. She holds a German Diploma in Computer Sciences with a focus on Machine Learning from the Philipps University in Marburg.
