AI Tools Automate Research for Investment Banks
Financial institutions are integrating AI agents and third-party data to automate tasks previously performed by human analysts.
Updated on Oct. 26, 2026 in Financial Services
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Financial technology firms and AI developers are rapidly scaling through new funding and major acquisitions to automate investment research for banks. These platforms integrate external financial data into foundation models to replace tasks previously performed by human analysts.
Why it matters
Financial institutions are shifting toward AI-driven analytical tools to handle complex modeling and research tasks more efficiently. This consolidation of data and software providers aims to automate high-level analysis that was once the exclusive domain of junior human analysts.
Investment in the sector includes Daloopa's $47 million Series C raise and AlphaSense's $350 million funding round, with LSEG recently agreeing to purchase credit data provider Octus for $3.5 billion. Software providers like Hebbia now support thousands of users, with one contract covering 5,000 Morgan Stanley employees.
The players
LSEG
A global financial market infrastructure provider that manages data, analytics, and clearing services for institutional clients.
AlphaSense
A market intelligence platform that provides AI-powered search and analysis of financial documents.
Morgan Stanley
A multinational investment bank and financial services firm that operates as a major consumer of institutional research tools.
Daloopa
A data software company that provides structured financial modeling and research data for over 6,000 corporations.
Bloomberg
A provider of financial software, data, and media services central to the daily operations of global investment banks.
The details
AI developers are bridging the gap between proprietary financial datasets and large language models to automate complex research. By embedding these agents directly into platforms like the Bloomberg Terminal or Copilot Studio, firms streamline the workflow for analysts who monitor data across thousands of companies. This shift allows banks to scale analytical output by automating modeling tasks that previously required manual data entry and synthesis.
Timeline
October 2025: LSEG launched a framework for banks to build custom AI agents.
December 2025: ChatGPT expanded its integration of LSEG and PitchBook data.
May 2026: Daloopa secured $47 million in Series C funding.
June 2026: AlphaSense raised $350 million at a $7.5 billion valuation.
October 22, 2026: LSEG agreed to acquire credit data firm Octus for $3.5 billion.
Market Landscape
The integration of AI agents into established systems marks an evolution in how firms augment their long-standing reliance on the Bloomberg Terminal for market data. This trend mirrors the broader shift toward embedding large language models directly into the primary software used by professional analysts.
Operators should evaluate how their existing software stacks incorporate these AI agents to reduce the labor burden on research tasks. Assess whether current data subscriptions can be consolidated as vendors increasingly bundle access to foundational AI models with proprietary financial datasets.
The takeaway
The automation of complex analytical modeling through third-party data integration signals a departure from human-intensive research processes. Monitor the performance of these AI tools against your current internal analyst output to determine if current workflows remain cost-effective.
Further reading
For additional context on the evolution of research tools, visit our Financial Services section.
Source note: This article includes information reported by PYMNTS.
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