Dun & Bradstreet, a global provider of data and analytics, has partnered with Anthropic to integrate its business risk data into Claude, a move aimed at helping organisations automate customer onboarding, compliance and due diligence processes through artificial intelligence.

The collaboration will embed Dun & Bradstreet’s Commercial Graph, a global database of business identities and risk indicators, into Claude, allowing financial institutions and other regulated organisations to carry out Know Your Customer (KYC) and Know Your Business (KYB) checks within a single AI-powered interface.

According to the company, the integration is expected to reduce the manual verification processes and lengthy onboarding cycles that have traditionally characterised corporate compliance, while enabling organisations to make faster and more informed decisions.

Commenting on the partnership, Group Director, Products, Dun & Bradstreet South Asia, Middle East and Africa, Nauman Lakhani, said the collaboration combines artificial intelligence with trusted business intelligence to improve decision-making.

“The value of this collaboration lies in connecting AI with trusted business context, making it usable in real-world decision environments where accuracy and confidence matter. Embedding verified data directly into workflows can help organisations strengthen governance and make decisions with greater clarity,” he said.

The company said the solution is built around the globally recognised D-U-N-S Number, which provides a consistent way of identifying businesses across different markets, making compliance and verification more reliable.

It added that the collaboration reflected a broader shift towards intelligence-led enterprise workflows, where artificial intelligence is increasingly being combined with trusted business data to improve governance and operational efficiency.

Dun & Bradstreet said it supports organisations in making informed, data-driven decisions through its global network, which covers more than 600 million business entities.