Billtrust brings live receivables data into Claude and Microsoft Copilot

Billtrust has launched a Model Context Protocol server that allows finance teams to interrogate live accounts-receivable data from within Claude and Microsoft Copilot, moving invoice-to-cash intelligence into the artificial intelligence tools increasingly used for everyday financial decision-making.

The company says it is the first accounts-receivable platform to provide this type of connection through MCP. Users can ask questions in plain language and receive structured answers based on live information without opening the Billtrust platform or manually combining reports from different systems.

The initial service provides read-only access across four areas: invoicing, payments, cash application and accounts-receivable analytics. A finance director could request a summary of quarter-end receivables risk, while an AR manager could ask which customer accounts are beginning to pay late and receive a ranked response based on outstanding balances and days past due.

Answers can incorporate information from connected enterprise resource planning, customer relationship management and financial planning systems alongside Billtrust’s own payment intelligence. The company says its network covers 13 million buyers and more than US$1tn in annual invoice volume.

The launch addresses a practical barrier to the adoption of generative AI in finance. Receivables data is often spread across accounting systems, payment applications and static reports, limiting the ability of general AI tools to provide reliable answers about cash conversion, overdue debt or payment behaviour.

Billtrust is initially restricting the service to structured, read-only responses. Customer data remains within each organisation’s own environment, AI tools do not receive direct access to raw data and every query is recorded through an audit trail.

A later phase is expected to add a Billtrust interface and prompt library inside the AI platforms. The company also intends to allow users to initiate actions such as collections outreach, payment application, dispute escalation and early-payment campaigns.

The commercial test will be whether finance teams trust the answers sufficiently to use them in collections and liquidity decisions. If successful, the model could move AI in receivables management beyond report summarisation towards workflows where analysis and action take place within the same environment.