How FactSet automated entity matching against official registry data

FactSet has built a model that automatically matches the companies in its database to official registry records on OpenCorporates, as part of a larger strategy to ensure their legal entity data coverage is updated and accurate. It uses software to achieve what a researcher used to do by hand: search for a company, work through the candidates that come back, and identify the one official record that is genuinely the right match. Across several thousand test companies, it reaches match rates of up to around 70 percent, with almost no false positives.

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How the Treasury’s Do Not Pay program uses OpenCorporates data to check a business is real before it pays it

On August 18, 2026, the U.S. Department of the Treasury published a one-page notice in the Federal Register titled: Designation of Databases to the Do Not Pay Working System. It marks the moment OpenCorporates data has officially been integrated into the system the federal government uses to decide whether a business should be paid, closing a process opened two months earlier, on the 25th of June 2026, when the Treasury published its proposed designation publicly.

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How NICB uses OpenCorporates to detect insurance fraud and verify business legitimacy

The National Insurance Crime Bureau (NICB) is a nonprofit organization dedicated to combating insurance fraud and crime. Using OpenCorporates' legal entity data covering all North American jurisdictions, NICB has enhanced their fraud detection capabilities, allowing them to verify business legitimacy, track entity relationships, and identify fraudulent patterns. This partnership has also enabled NICB to build a data-driven case for policy advocacy around registry transparency, demonstrating the societal value that open legal entity data provides.

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The registered agent called “Located In”

Every company register insists on a registered agent: a name and a street address where lawsuits and state letters can reach someone who will actually notice. The OpenCorporates API exposes this twice. Once on the company record, as agent_name. Once in the officers list, as whoever holds the agent role. I wanted to see what edge cases of those names, so I searched the officers endpoint for a phrase instead of a person. I typed LOCATED IN. The API came back with two thousand and eleven matches.

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The church incorporated in the year one

The other week I was testing a company data API, running harmless searches against Connecticut to see how the search behaved. I asked for companies whose names contain the word "New". Back came the usual spread of LLCs and property vehicles, and then a result that literally stopped me mid-scroll: ST. PETER'S CHURCH OF NEW HAVEN, CONNECTICUT. Company number 0065179. Status: Active. Company type: Special Chartered. Incorporation date: 3 January 0001 (over 2025 years ago).

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Case study: Why automating fund verification is the new standard for Client Lifecycle Management (CLM) providers

This case study examines how leading CLM providers are addressing unprecedented challenges through automated fund verification, drawing on real-world implementations that demonstrate measurable improvements in efficiency, accuracy, and client satisfaction. The evidence shows that automation has evolved from a competitive advantage to a fundamental requirement for CLM providers serving the financial services sector.

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Case study: How Tradeverifyd automatically identifies and analyzes supply chain risks

Supply chains are more complex than ever. Global systems are susceptible to disruption due to environmental, geopolitical, financial and other events outside of the control of Enterprise Leaders. Tradeverifyd allows those same leaders and managers to see deep into their supply chains - well past their Tier 1 suppliers - to proactively manage their supply networks before disruptions occur. 

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Seconds vs days: The business impact of automated verification

Every transaction flows through a web of legal entities and regulations. Today, most organizations rely on laborious manual processes to verify legal entity existence. Staff must navigate global government websites and tackle paywalls. They have to create accounts for each new registry, and deal with inconsistent data. This process can take hours or days per entity, creating significant operational drag.

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