LSEG Rolls Out Model-as-a-Service as Societe Generale Joins Its Analytics Marketplace – FinanceFeeds

London Stock Exchange Group (LSEG) has unveiled Model-as-a-Service (MaaS), a marketplace capability built to let financial institutions host, distribute, and operationalize proprietary models on a secure, governed platform. At launch, Societe Generale has joined as a model provider, making a curated set of its analytics and datasets available through LSEG’s distribution rails.

What MaaS Brings to the Market

MaaS is designed as a unified discovery and consumption layer for institutional models and analytics. Instead of building custom integrations and governance frameworks for each provider, buy-side and sell-side firms can onboard and use models at scale through a standardized marketplace. The platform emphasizes enterprise-grade controls—access management, compliance, and operational guardrails—so institutions can commercialize or consume models while reducing the overhead tied to infrastructure and go-to-market execution.

Societe Generale Joins as a Model Provider

Under the collaboration, seven Societe Generale datasets and analytics products will be distributed through LSEG’s marketplace, spanning Fixed Income, FX, ESG, and Equities. The integration enables clients to run Societe Generale’s analytics alongside LSEG’s own datasets and modeling tools within a single environment, streamlining multi-provider workflows for research, trading, and risk teams.

Why It Matters Now

Demand for scalable model delivery is accelerating across the industry. Both asset managers and broker-dealers are looking to plug third-party analytics into their workflows without standing up bespoke pipelines for integration, governance, and oversight. At the same time, banks are increasingly seeking to monetize proprietary research, analytics, and structured datasets through third-party channels rather than limiting access to internal platforms or one-to-one client deliveries. MaaS speaks to both dynamics by offering a governed distribution layer for complex analytics products.

Under the Hood: Microsoft and MCP Connectors

LSEG’s MaaS is powered by its strategic partnership with Microsoft, enabling secure distribution of models into partner AI ecosystems at scale. The platform leverages Model Context Protocol (MCP) connectors to deliver analytics into environments such as Microsoft Copilot Studio—where LSEG has already made an MCP connector available. This architecture aligns with a broader shift toward embedding market intelligence directly into AI-driven tools, allowing users to access analytics, pricing models, and datasets within productivity and decision-support systems rather than relying solely on standalone terminals.

What Clients Can Expect

• Faster adoption: Standardized onboarding and a marketplace framework reduce time-to-value for new analytics and models.
• Governed scale: Built-in controls for access, compliance, and oversight help institutions scale model usage without adding integration debt.
• Unified workflows: A single platform for multi-provider analytics, enabling side-by-side evaluation and deployment of models from LSEG, Societe Generale, and others.

Shifting Economics of Financial Analytics

The collaboration underscores a structural change in how banks package and distribute intellectual property. By using third-party marketplaces, research and analytics teams can reach a broader client base, while buyers gain a consistent, governed experience for discovery and consumption. For model owners, MaaS offers a commercial path that reduces the operational friction of distribution; for clients, it lowers the effort required to access high-value analytics across asset classes.

The Bottom Line

LSEG frames the launch of MaaS as a step toward modernizing the financial model ecosystem—one that prioritizes cross-provider interoperability while maintaining institutional-grade governance and security. With Societe Generale on board at launch and deep integration into Microsoft’s AI tooling via MCP connectors, MaaS positions itself as a scalable conduit for embedding analytics directly where institutional users work.