AI Agents for Investment Banking: Research and Document Review

AI agents help investment banking teams with research preparation, document review, and diligence coordination. A senior professional reviews every output before it informs a client deliverable, an internal model, or a transaction decision. Tools like fenbs preserve the public-source links, analytical assumptions, and approved decisions that a team needs to stay consistent across a deal's long timeline.

Investment banking work is time-pressured and document-intensive. An agent can accelerate the research and first-draft work; the professional judgment, client relationship, and regulatory accountability stay with the banker. Nothing in this article describes using AI agents to replace human decision-making on transactions.

What AI agents help investment banking teams do

The most productive applications are in structured research and document review tasks. Agents help with gathering public information, organizing it, and producing first drafts that analysts and associates then review and refine.

• Compile publicly available financial and market data from identified sources
• Draft a comparable company analysis structure from descriptions you provide
• Review a pitch book section for completeness and consistency against a checklist
• Summarize a company's public filings and investor presentations
• Organize diligence request lists and track item status
• Draft an internal deal memo section from an outline and factual inputs

Research preparation from public sources

Industry research and company analysis start with public information: SEC filings, earnings call transcripts, investor presentations, and industry association reports. An agent can compile and summarize that material from sources you identify. Verify each claim on the primary source before it enters a client document.

Comparable company analysis involves identifying publicly traded companies in the same sector, pulling key financial metrics, and building a benchmark set. An agent can help draft the framework and identify candidate comparables from public information. Analysts confirm the selection criteria and the figures from primary data sources.

Record the sources you use and the analytical assumptions you make as you work, not after the fact. An assumption that a particular metric is appropriate for a given industry comparison changes the analysis significantly. If that assumption lives only in an analyst's head, it is invisible to the next person who touches the model.

Pitch document review and consistency checks

Pitch books and information memoranda are long and change frequently as deals progress. An agent can review a document section against a checklist: are all required components present, are financial figures internally consistent, do the narrative and the exhibits match? Give it the section and the checklist; it returns a structured list of gaps.

Consistency checks are a common source of late-stage rework. An agent checking whether the EBITDA figure in the executive summary matches the one in the financial model section catches that error before the document goes to a client. This is pattern matching, not analysis; the professional review focuses on judgment questions the pattern check cannot answer.

Diligence coordination

Due diligence involves coordinating requests across legal, financial, operational, and commercial workstreams. An agent can organize a master diligence checklist from a template, track which items are open and which are received, and draft follow-up requests for outstanding items. A deal team member confirms status and sends follow-ups.

Diligence produces a large volume of documents. An agent can help prioritize the review queue by flagging items that match a specific risk category or search term from descriptions you provide. The professional reviewer reads and assesses the actual documents; the agent helps triage the stack.

Analytical assumptions and decision tracking

Investment banking analysis involves assumptions at every level: which multiples apply, which cost synergies are achievable, which market growth rate is supportable. Those assumptions are often made in conversations and never written down. When a transaction committee asks why a particular number was used, the analyst who made the assumption may no longer be on the deal.

Recording analytical assumptions in fenbs as they are made gives the deal team a traceable record. A senior banker reviewing a model can see not just the number but the reasoning and the primary source behind it. Decisions made by the transaction committee belong in that same record, with the date and the approver.

Compliance and confidentiality

Investment banking work involves material non-public information, client confidentiality, and regulatory requirements under SEC rules, FINRA regulations, and firm compliance policies. Do not enter material non-public information into any AI agent or third-party tool without confirming it meets your firm's information security and compliance requirements.

Limit AI agent sessions to publicly available information and internal analysis of that public information. Confidential client data, non-public financial projections, and transaction terms belong in your firm's approved systems. An agent is useful for the public research phase; the confidential work stays in controlled environments.

How to do this with fenbs

Set up one project per transaction or pitch. Keep confidential client data out of fenbs; use it for the public research framework and process decisions.

1. Create a free account. Use a team kanban board for a deal team, with roles that reflect who can approve analytical assumptions and decisions.
2. Add the transaction as a project in fenbs. Write the deal type, the key workstreams, and the timeline. Keep the description at the level of publicly available information.
3. Record public source links and analytical assumptions as they are made. Note the primary source and who confirmed the assumption.
4. Connect your AI app from Integrations. At the start of each research session, ask the assistant to read the project context and existing source links before compiling any new research.
5. Track diligence checklist items as tasks. Before each session ends, update item statuses so the deal team knows what is open and what is outstanding.

When AI agents are not the right tool

An agent does not make valuation judgments, negotiate terms, or assess transaction risk. It does not replace the experienced professional reading a set of financials and forming a view about the business. It accelerates the research and preparation work that precedes that judgment.

For broader context on AI in banking and finance, see the AI in banking industry overview. For an introduction to agent types and patterns, types of AI agents covers the different designs and their appropriate uses.