Agentic AI Streamlines Regulated Finance Workflows
Data accuracy and fragmentation slow down regulated finance. "Data accuracy is the heart of AI in financial services," said Nej D'Jelal, group head of workspace platform at London Stock Exchange Group (LSEG).
His team is embedding AI into the tools finance professionals already use, grounded in trusted, regulatory-grade data. The goal: collapse disjointed workflows and cut manual rework without compromising oversight.
Why Trusted Data Matters
AI is only as useful as the data behind it. In regulated settings, errors carry real cost-operational, compliance and reputational.
LSEG's approach centers on reliable reference and market data, so analysts and risk teams can automate with confidence rather than double-check outputs all day.
Collapsing Fragmented Work
LSEG is integrating productivity and collaboration tools with its data stack to remove context switching. Instead of bouncing across portals and spreadsheets, users can search, compare and act inside a unified workflow.
The result is less copying, fewer ad hoc exports and a faster path from question to answer.
Deep Integration with Microsoft Tools
D'Jelal described LSEG's tight integration with Microsoft Copilot, Teams and Excel. Through one interface, users can discover filings, news and research, then bring insights straight into their models and chats.
Single sign-on lets people move between Excel and LSEG Workspace while preserving permissions and context. That keeps access clean and audit-friendly.
"Being able to speak to the machine in your chosen native language and ask it to compile the report and modify the report is something that we see as a basic capability of an agentic workflow," he said.
If you're exploring this stack, start with a clear security model and prompt guardrails, then layer in task-specific copilots tied to approved data sources. See Microsoft's overview of Copilot for Microsoft 365 for the core building blocks.
What This Means for Finance Teams
Analysts spend less time finding information and more time testing scenarios. Risk and compliance teams get consistent data lineage across tools.
Leaders gain a clearer view of how work flows through the organization, which makes it easier to standardize best practices and retire brittle processes.
Practical Moves to Consider
- Define your "trusted data" layer first. Lock down sources, permissions and lineage.
- Activate single sign-on across Excel, Teams and your market data tools to keep context intact.
- Start with one agentic use case: e.g., a natural-language report builder that drafts and updates a weekly credit or liquidity pack.
- Embed discovery where work happens. Let users pull filings, news and research inside Excel and Teams-no extra portals.
- Instrument the workflow. Track accuracy, response quality and time saved to guide rollout.
Where LSEG Is Focused
D'Jelal leads the integration of LSEG's data and workflow solutions into tools such as Excel and Teams. His focus is adopting agentic AI to simplify complex, repetitive tasks and support community-aligned, AI-assisted processes.
The aim is straightforward: help finance professionals move from gathering data to making decisions-faster, with fewer manual steps, and with controls that satisfy regulators.
Want to Go Deeper
- Explore practical tools for front-, middle-, and back-office teams: AI tools for finance.
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