AI-ready financial intelligence, native in Excel and PowerPoint - supported by LSEG
AI is becoming part of everyday financial workflows. But in investment and risk decisions, you need content that is precise, structured, and governed. That is the point of LSEG's approach: trusted, computable data paired with clear methodologies, now accessible in Excel and PowerPoint through Anthropic's Claude via the Model Context Protocol (MCP). Think of it as a meaning layer that lives where you work, ready to support decisions.
Where deterministic meets probabilistic
The key design choice in AI for finance isn't "which model," it's "what should be computed vs. inferred." Language models excel at probabilistic tasks: synthesizing earnings call narratives, surfacing sector themes, and connecting macro signals to market moves. But many numbers in finance are not up for interpretation.
Normalised free cash flow, consensus estimates, yield curve analytics, spread duration, portfolio risk contributions-these require deterministic computation with defined methodologies. Get them wrong and you see it in basis points, P&L, legal exposure, and regulatory scrutiny.
Why LSEG matters in the AI workflow
LSEG's trusted content doesn't just give an AI system more text to reason over. It provides deterministic anchors: standardised fundamentals, calibrated macro series, validated consensus estimates, and verified analytics from platforms such as Yield Book. Those anchors stay exact, consistent, and auditable.
Claude can then reason around them-contextualizing fundamentals against macro trends, comparing analytics to consensus, linking Reuters News to instruments and exposures-while the quantitative foundation stays tight.
Semantic depth: teaching AI what finance actually means
Scale is one thing; semantics is the edge. LSEG brings breadth across company fundamentals, estimates, macro data, cross-asset analytics, and Reuters News. Underneath sits a structured web: taxonomies across asset classes and jurisdictions, standardised definitions for like-for-like comparisons, identifiers that resolve ambiguity, and cross-references linking issuers to securities, peers, filings, and estimates.
That semantic depth lets an AI agent break down a request like "Show me investment-grade European corporate issuers with rising free cash flow and tightening CDS spreads" into precise, executable steps. The result: fluency backed by financially correct outputs.
Why MCP makes this practical
MCP (Model Context Protocol), originally developed by Anthropic and now governed as an open standard, makes this connection straightforward. It provides standardized connectivity while preserving security and licensing controls. For users, the experience is immediate: in Claude's Excel and PowerPoint experiences, LSEG customers can use natural language to retrieve and structure trusted content-no engineering or scripting required.
If you want the spec, see the open standard here: Model Context Protocol. For Claude product details, start here: Anthropic Claude.
What this unlocks for finance teams
- Investment banking: Faster peer screens and comps across jurisdictions, with standardised fundamentals and estimates that arrive consistent and comparable-freeing time for judgment and advice.
- Wealth management: Move across equities and fixed income with coherent analytics. Model allocation changes with validated data and bring in relevant news context without leaving your workbook or deck.
- Quant research and trading: Explore and prototype datasets conversationally in Excel before production pipelines, speeding iteration while preserving methodological rigor.
Across roles, LSEG narrows the gap between accessing data and converting it into actionable insight. If you already have access to Claude's products and LSEG content, this capability is available now inside Excel and PowerPoint.
How it works in Excel and PowerPoint
- Connect via MCP: Authenticate your LSEG entitlements through MCP to keep licensing and governance intact.
- Query in natural language: Ask for assets, metrics, periods, and constraints. For example: "Build a table of European IG corporates with 3-year rising FCF, show latest CDS spread and 1M change, sorted by tightening."
- Compute deterministically: Keep core calculations (e.g., spread duration, risk contributions, FCF normalization) anchored to LSEG-defined methodologies.
- Contextualize with reasoning: Have Claude summarize sector drivers, contrast results with consensus, or link to relevant Reuters stories for each issuer.
- Audit and reuse: Preserve sources, definitions, and parameter choices alongside outputs so teams can review, repeat, and scale the workflow.
Governance, audit, and risk
Because LSEG content is licensed, structured, and validated, the numbers you compute remain defensible. Methodologies are stable. Entities and instruments resolve cleanly. Outputs can be explained and audited.
That reduces the chance of inconsistent comps, silent data drift, or untraceable figures turning into basis-point errors. It also supports internal model risk standards and external regulatory expectations.
LSEG everywhere, in action
LSEG's collaboration with Anthropic-using direct MCP connectivity-extends trusted content into the AI layer while keeping the same licensing and governance principles. Your AI platform choice becomes a workflow decision, not a data-access constraint. LSEG's data shows up natively, governed, and ready.
That is the meaning layer: precise enough to compute with, structured enough to reason over, and governed enough to act on-now inside the tools you already use. Explore more on Claude and practical tips for Office Tools (Excel & PowerPoint) to build repeatable finance workflows with confidence.
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