Cases / Hate and Discrimination
Hate and Discrimination

Exposing Transnational Corruption with Public Web Data

Combining investigative journalism with data-driven research to create global impact

Internet Transparency
Combating Corruption
Non-Profit Organization
Hate and Discrimination
Anti Corruption Data Collective ACDC Bright Initiative
anti corruption data collective acdc bright initiative

About

The Anti-Corruption Data Collective (ACDC) is a coalition of investigative journalists, data analysts, academics, and policy advocates committed to exposing and combating transnational corruption. With a network spanning over 100 countries, ACDC works closely with local non-profits, reporters, and subject matter experts to shed light on unethical practices in private investment, real estate, and global governance. Their mission is to drive legal and systemic reforms, using public data to expose hidden networks of corruption and increase transparency in governance and finance worldwide.

ACDC’s unique approach combines investigative journalism with data-driven research, enabling their reports to influence policy discussions and create tangible change. With projects ranging from mapping company ownership to addressing the manipulation of global climate policies, ACDC tackles complex issues at both national and international levels.


Project: Investigating Company and Real Estate Ownership in France

ACDC’s investigation project focused on the cloudiness of property and business ownership in one of Europe’s largest economies. This initiative resulted in the publication of the July 2023 investigation report, “Behind a Wall of Secrecy”, which uncovered critical insights into how real estate and corporate assets are often shielded from scrutiny through complex ownership structures.

Key Findings:

  • Identified systematic gaps in beneficial ownership transparency in both corporate and real estate sectors.
  • Highlighted how loopholes in registries allow illicit networks to hide investments and property holdings, undermining local economies.
  • Recommended measures to improve the accessibility of property ownership records and increase due diligence by financial institutions.
Source: Behind a Wall: Investigating company and real estate ownership in France, July 2023

The report focused on the darker layers of French company and real estate registries, which often conceal information regarding beneficial ownership. By analyzing public data from the French land registry plan, ACDC identified how hidden ownership structures facilitate money laundering, tax evasion, and illegal cross-border wealth transfers. The findings underscored the urgent need for legal reforms, including increased access to beneficial ownership data and enhanced regulatory oversight in the real estate sector.

Source: Behind a Wall: Investigating company and real estate ownership in France, July 2023

Impact

ACDC’s ability to access the French company and real estate registries using Bright Data’s tools was pivotal to the success of this investigation. By utilizing Bright Data’s Proxies, the team collected vast amounts of public data efficiently and securely. These insights facilitated a detailed analysis of corporate governance practices, exposing hidden corruption pathways. ACDC’s report has since fueled conversations among policymakers, journalists, and advocacy organizations about the importance of transparency in real estate and corporate ownership in France. It continues to prompt action at both local and international levels.

Report: Behind a Wall: Investigating Company and Real Estate Ownership in France


Project: How Corruption and Undue Influence Threaten Multilateral Climate Action

In this investigation, ACDC focused on corruption and undue political influence during the UN Climate Change Conference in Baku, Azerbaijan that was held in November 2024. In their published report “COP-CO-OPTED” that was released right before the conference, found that through its organization of COP 29, Azerbaijan, with its autocratic government, large fossil fuels sector, and severe levels of public sector corruption, clearly illustrates several risks to the integrity of UN climate conferences.

Key Findings:

  • Documented how lobbying and institutional loopholes allowed certain industries to dilute critical climate agreements.
  • Revealed the influence of political and corporate actors in controlling funding allocation and project priorities at the UN Climate Conference.
  • Recommended stricter governance structures and increased transparency to protect multilateral climate commitments from undue influence.
European countries where Azerbaijan has been accused of influencing politicians, source: How Corruption and Undue Influence Threaten Multilateral Climate Action, ACDC and Transparency International

Impact

By leveraging Bright Data’s proxies and APIs, ACDC effectively gathered and analyzed vast datasets to support its investigation. The “COP-CO-OPTED” report has ignited a global discussion about the integrity of climate negotiations and the need for reform in multilateral decision-making processes. The findings have been referenced by policymakers, nonprofit organizations, and academics advocating for greater scrutiny of international conference proceedings. ACDC’s impactful work in this sphere continues to drive calls for more accountability within global climate governance.

Report: How Corruption and Undue Influence Threaten Multilateral Climate Action


I’ve been floored by the effectiveness of the Bright Data platform. As a co-founder of a non-profit, I qualified for their Bright Initiative and have used a number of their products (proxies, dataset marketplace, etc.) for several years now. We have had astonishing success using their proxies to collect web data for our research, and continuously rely on their ISP services to update our databases. The configurations are easy to use and integrate into our workflow, while the customer service every step of the way has been nothing short of exceptional.

David Szakonyi, Co-Founder, Anti-Corruption Data Collective

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