Why Data Infrastructure Is the Key to AI in Finance

Why It Matters

Robust data infrastructure transforms AI from a pilot‑stage experiment into a reliable, enterprise‑wide engine for risk management, compliance and revenue growth, giving financial firms a competitive edge.

Key Takeaways

• LSEG consolidated data into a single lake using Microsoft ecosystem
• 83% of leaders say stronger data infrastructure speeds AI adoption
• LSEG now offers over 33 PB of AI‑ready financial data
• Legacy siloed stacks hinder AI accuracy and increase compliance risk
• Unified data improves stress testing, scenario analysis, and productivity

Pulse Analysis

Financial institutions have long wrestled with legacy data stacks that are fragmented, duplicated, and poorly governed. Recent surveys underscore the urgency: while 63% of firms claim moderate responsible‑AI maturity, a striking 83% of senior executives believe that a stronger data foundation would markedly accelerate AI rollout. Poor data quality not only skews model outputs but also amplifies compliance and operational risks, forcing costly human oversight. In this context, data infrastructure is no longer a back‑office concern—it is the linchpin for delivering trustworthy AI outcomes.

LSEG’s recent transformation illustrates a pragmatic path forward. By partnering with Microsoft, the exchange migrated its disparate repositories into a unified data lake built on OneLake, with governance enforced through Microsoft Purview and security via Defender. This ecosystem supports the Model Context Protocol and integrates with leading AI platforms such as Claude, ChatGPT, Snowflake and Databricks. The result is a curated, AI‑ready catalog exceeding 33 petabytes, spanning decades of proprietary financial information. Such scale enables analysts to experiment rapidly, while embedded data rights ensure compliance and discoverability across the organization.

The broader implication for the financial services sector is profound. Unified, high‑quality data fuels more accurate stress‑testing, scenario analysis, and pricing models, directly enhancing decision‑making and risk mitigation. Democratizing data access breaks down silos, empowering product, compliance and front‑office teams alike to innovate at speed. As firms emulate LSEG’s model, we can expect a wave of AI‑driven services—ranging from automated underwriting to real‑time market insights—propelling the industry toward a more efficient, data‑centric future.