In addition, FinQore provides specialized tools that enable Claude to interpret complex business models, analyze financial metrics, and deliver deeper, more meaningful analysis.
With these capabilities, finance can ask AI questions in plain language, explore their financial data more deeply, and take action on insights directly within FinQore, enabling them to move faster, see further, and drive operational excellence with confidence.
Breaking Through the AI Trust Barrier
The biggest barrier to AI adoption in finance isn't technology — it's data quality. The AFP FP&A Survey (2025) found that more than 60% of finance teams cite inaccessible or inconsistent data as their top barrier to analytics. The impact is clear: MIT's State of AI in Business 2025 report found that 95% of enterprise AI pilots fail — often because model outputs are unreliable without consistent data and detailed business context. For AI to generate insights that CFOs can trust for high-stakes decisions and reporting, it needs access to a unified, context-rich data foundation.
"CFOs are often stuck in a frustrating middle ground," said Vipul Shah, CEO & Co-Founder of FinQore. "When data sources conflict, definitions vary, and there's no audit trail, finance can't rely on AI-generated insights."
A Trusted Data Foundation, Structured and Segmented for AI
Now, with FinQore's Claude integration, built on the Model Context Protocol (MCP), finance teams gain secure access to continuously validated financial data — delivered and maintained on FinQore's expert-powered financial data platform.
Unlike software-only tools that simply aggregate data, FinQore pairs in-house finance experts with a purpose-built platform that harmonizes data according to each company's unique financial logic and actively resolves discrepancies. With full audit trails and override controls, finance teams gain the flexibility of a spreadsheet with the reliability of an enterprise-grade, AI-ready platform. The result: every analysis and action is powered by reconciled, deeply segmented, context-rich financial data.
"Now, CFOs can harness the power of Claude on data they truly trust," said Shah. "Because FinQore is architected and optimized for AI-native workflows, even the most granular, high-volume financial data can be explored seamlessly and lightning-fast."
Looking Ahead
This integration underscores FinQore's vision for agentic AI in finance, where AI goes beyond analysis to actively guide and drive real business actions. Finance can rely on FinQore to show what happened in the business and recommend what to do next, whether it's allocating resources, adjusting budgets, refining pricing, or identifying the best growth opportunities.
"We're building along a clear trajectory," said Jim O'Neill, CTO & Co-Founder of FinQore. "FinQore already delivers trusted, AI-powered insights; now we're evolving into the system of action for finance — where CFOs, experts, and AI work together to maximize enterprise value."
About FinQore
FinQore is the Expert-Powered, AI-Native Financial Data Platform for strategic CFOs and finance teams. It automates the revenue and customer cube, delivers real-time visibility into performance drivers, and provides actionable insights through purpose-built AI tools. With FinQore, CFOs move beyond error-prone spreadsheets and rigid tools to a system of action for complete financial clarity. Available as a comprehensive platform and through direct integration with leading AI platforms like Claude, FinQore meets finance teams wherever they work.
FinQore customers report measurable outcomes: accurate ARR or revenue by day two of close, reconciliation time cut by over 50%, actionable insights delivered over 10x faster, and over 3x ROI in year one through automation and expert management.
SOURCE FinQore
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