In enterprise finance, AI has been long on promise but short on trust. Most CFOs know the problem: the algorithms are clever, but the data is messy. Now, FinQore thinks it has a fix. The financial data platform just launched the first financial data connector for Anthropic’s Claude, positioning itself as the bridge between generative AI and CFO-grade accuracy.

Why This Matters

The headline isn’t just “Claude gets another integration.” It’s that FinQore is addressing the single biggest reason AI stalls in corporate finance: bad data.

According to the Association for Financial Professionals’ FP&A Survey 2025, over 60% of finance teams cite inaccessible or inconsistent data as their top barrier to analytics. Meanwhile, MIT’s State of AI in Business 2025 report claims 95% of enterprise AI pilots fail—largely because models are only as good as the data you feed them. FinQore’s pitch is that its connector doesn’t just give Claude access to numbers, it gives it clean, context-rich, and continuously validated financial data that CFOs can bet their reputations on.

In short: It’s not just a new integration, it’s an attempt to solve finance’s AI trust gap.

What FinQore Adds to Claude

FinQore isn’t another “plug it in and pray” middleware. Its connector is built on the Model Context Protocol (MCP), a rising standard for grounding AI models in domain-specific data. That means Claude doesn’t just ingest data; it interprets it with the same definitions, rules, and audit trails that finance teams already rely on.

Key features include:

• Expert-validated data foundation – Data isn’t just aggregated, it’s harmonized by FinQore’s in-house finance experts.
• Context-aware AI queries – Finance teams can ask Claude plain-language questions and get responses grounded in reconciled, company-specific metrics.
• Audit-ready controls – Every insight comes with an audit trail and override options—think “spreadsheet flexibility meets enterprise-grade governance.”
• Deeper analysis – Beyond balance sheets, Claude can now interpret complex business models, analyze financial metrics, and recommend next actions.

Vipul Shah, FinQore’s CEO and co-founder, puts it bluntly: “When data sources conflict, definitions vary, and there’s no audit trail, finance can’t rely on AI-generated insights. With FinQore, CFOs can finally harness Claude on data they truly trust.”

The Competitive Context

FinQore is hardly alone in chasing the AI-for-finance opportunity. Oracle, Workday, and Anaplan are all layering AI assistants on top of their platforms. The difference is that most enterprise vendors are bolting AI onto existing systems, while FinQore is architected for AI-native workflows from the ground up.

That might sound like marketing spin, but there’s a real distinction: FinQore’s system is optimized for agentic AI—the next phase where AI doesn’t just analyze but actively guides decisions. Instead of being a “co-pilot” in finance, it’s aiming to be the control tower.

What’s Next for CFOs?

FinQore’s roadmap hints at AI that won’t just flag financial anomalies but also recommend budget adjustments, pricing refinements, and even strategic growth opportunities. “We’re evolving into the system of action for finance,” said Jim O’Neill, the company’s CTO and co-founder.

That’s an ambitious claim. But if FinQore can deliver reliable, explainable, audit-ready insights at scale, it could carve out a real edge in the crowded enterprise AI market. After all, CFOs don’t just want faster answers—they want better ones.

And if FinQore can finally solve finance’s data trust problem, Claude might have just found its most serious business use case yet.