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AI can make investment-banking teams faster at research, document review, drafting and other information-heavy work, but it is not evidence that bankers—or their judgment—can be removed. The practical opportunity is to use the right mix of traditional machine learning, automation and generative AI for bounded tasks whose outputs a qualified professional can check. The difficult work is proving that the data are permitted and traceable, the output is reliable, and the deployment meets securities, privacy, cybersecurity and supervisory obligations.
What “AI” means in an investment bank
“AI” covers several technologies, not one universal tool. Traditional machine-learning models, rules-based automation and natural-language processing may be better choices than a generative model for a tightly defined prediction, classification or control. KPMG’s Artificial Intelligence in Investment Banks (October 2023) advises selecting a model according to the use case and the data it will receive.
Generative AI (GenAI) produces text, code, summaries or other content from a prompt and its available context. That makes it attractive for work in which employees spend substantial time reading, organizing and drafting, while a banker can inspect the result before it is used.
Where generative AI can help investment-banking teams
The applications below are potential workflows described by Deloitte and FINRA, not proof that every bank has deployed them in production. In each case, the model should assist a defined step rather than make an unreviewed client, valuation or regulatory decision.
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Research and information retrieval
FINRA’s GenAI: Continuing and Emerging Trends — 2026 FINRA Annual Regulatory Oversight Report, published December 9, 2025, identifies “Summarization and Information Extraction” as the most common observed GenAI use among its member firms. Early implementations emphasize internal processes and information retrieval. An investment-bank team could use that capability to summarize filings, transcripts, diligence materials or internal knowledge, then verify important statements against the source documents.
Pitch books and client materials
Deloitte’s 2024 analysis lists draft support for pitch books, industry reports, investment theses and performance summaries. A model can suggest an outline, turn approved data into a first draft or adapt language for a particular audience. A banker still needs to check every number, attribution, confidentiality boundary and representation before anything reaches a client.
Diligence, valuation and transaction documents
Potential uses include organizing due-diligence findings, proposing an initial deal structure, assisting with valuation analysis and drafting portions of prospectuses or term sheets. These outputs depend on the quality of the supplied assumptions and source data. They are drafting and analytical aids—not legal advice, approval of a transaction or a substitute for an independent valuation and counsel review.
Coding and internal operations
Deloitte cites Goldman Sachs as an example of a firm using GenAI to help developers and coders create code more efficiently. That example does not establish a measured, firm-wide productivity result. Code generated by a model still requires security review, testing, dependency checks and controls over access to proprietary data. More broadly, FINRA observes that firms’ early GenAI efforts concentrate on internal-process efficiency and information retrieval.
Trading and market analysis
Deloitte discusses natural-language processing and sentiment analysis, synthetic data for risk modelling and strategy optimisation, and assistance with summarising company or industry fundamentals and backtests. These are possible workflows, not evidence that GenAI outperforms established quantitative methods or that autonomous trading is suitable. A trading application requires especially careful testing for stale information, data leakage, model drift, market-impact assumptions and controls around orders.
What the productivity numbers actually show
Deloitte’s figures are forecasts or estimates from its 2024 Center for Financial Services analysis. They should not be read as measured results achieved by every investment bank.
| Figure | What it measures | How to interpret it |
|---|---|---|
| 27%–35% | Potential productivity improvement for front-office employees by 2026, after Deloitte’s stated inflation adjustment | Deloitte estimate; not a guaranteed or universally observed outcome |
| 34% | Estimated average productivity improvement for the investment-banking division, including equity and debt issuance, M&A advisory and related advisory work | Deloitte estimate; the definition covers a division, not a controlled experiment at every firm |
| 35% of surveyed institutions | Financial institutions reporting that they adopted or improved GenAI capabilities in the prior 12 months, versus 25% in 2023 | Finastra’s 2024 vendor-sponsored survey of more than 1,100 professionals across 12 countries; not an investment-bank-only adoption rate |
No independent, investment-bank-only measured adoption percentage is established by these sources. Adoption, where it exists, may refer to a pilot, an improved capability or a production system, so percentages from broad financial-services surveys are not interchangeable with deployment across investment-banking divisions.
Why high-effort, easy-to-check work is the best starting point
Deloitte describes GenAI as most fruitful “in areas where the output generation effort is high and validation is relatively easy.” That principle gives a practical way to rank projects.
| Question | Favourable characteristics | Warning signs |
|---|---|---|
| How much effort is generation? | Large volumes of reading, extraction, formatting or first-draft writing | Little generation work, or a task whose value depends mainly on human relationships and judgement |
| Can a qualified person validate it? | Source documents and acceptance criteria are available; a reviewer can check the result quickly | No reliable reference set, ambiguous answers or review that costs more than doing the work manually |
| What is the error cost? | Low-consequence internal drafts with a clear approval gate | Potentially misleading clients, incorrect disclosures, unsuitable advice, erroneous orders or missed compliance issues |
| What data are involved? | Approved, permissioned and traceable internal or public data | Material non-public information, personal data, confidential client files or data sent to an unapproved provider |
| Can the process be controlled? | Prompts, model version, sources, output and approval can be logged and monitored | Opaque third-party processing, no audit trail or no way to detect quality drift |
Regulation and governance are part of the product
FINRA’s Regulatory Notice 24-09 (June 27, 2024) says existing rules and securities laws continue to apply when member firms use GenAI. Its 2026 oversight report points to supervision, communications, recordkeeping and fair-dealing obligations that may be implicated, depending on the deployment. The notice states: “This Notice does not create new legal or regulatory requirements or new interpretations of existing requirements, nor does it relieve member firms of any existing obligations under federal securities laws and regulations.”
In practice, a firm should evaluate the tool and the specific use case before testing or deployment, rather than assuming that a general-purpose model is outside existing controls.
Controls FINRA highlights
- Formal approval and documented governance or model-risk procedures.
- Robust pre-deployment testing and ongoing monitoring.
- Logs of prompts and outputs, with the model and data context identifiable.
- Human-in-the-loop review for material outputs.
- Checks for reliability, accuracy, privacy, bias, cybersecurity, data provenance and the actions an agent is allowed to take.
Risks identified across the financial sector
The U.S. Department of the Treasury’s December 19, 2024 AI-in-financial-services report release highlights data privacy, bias and third-party-provider risk. The U.S. Government Accountability Office’s review, released May 19, 2025, adds data-quality and cybersecurity concerns alongside potential efficiency, cost and customer-experience gains. GAO says federal regulators primarily oversee AI through existing laws, guidance and risk-based examinations while considering whether guidance needs updating.
A practical implementation path for an investment bank
- Define the job and the owner. Specify the exact output—such as extracted covenant terms or a draft market summary—and name the business, compliance and technology owners.
- Classify the data. Decide whether prompts and outputs contain public information, confidential client material, personal data or material non-public information. Confirm that the proposed provider may receive and retain it.
- Set an evidence standard. Require citations or links back to source documents where feasible, and define what a reviewer must check before approval.
- Test before launch. Use representative historical cases, adversarial prompts and known-error examples. Measure factual accuracy, omissions, consistency, latency and failure severity rather than relying on a fluent demonstration.
- Keep people accountable. Give a qualified banker authority to reject, correct and escalate an output. Do not let a draft flow automatically into a client communication, filing, valuation, order or approval.
- Log and monitor. Record the model version, prompt, data context, output, edits and approver. Monitor quality, access, incidents and model drift, with a rollback or suspension procedure.
- Reassess periodically. Recheck legal obligations, vendor terms, data use, performance and the continued value of the workflow whenever the model, provider, data or business process changes.
What AI does—and does not—mean for banker roles
AI can compress the time spent finding information and producing a first version of a document. It does not supply accountability for a client relationship, the commercial judgement behind a transaction, the reasonableness of assumptions, or the duty to comply with securities laws. The most defensible near-term model is augmentation: software handles repeatable information and content work while bankers validate facts, exercise judgement and own the decision.
Further reading
Readers wanting a broader finance perspective can consult Springer Nature’s 2025 book Generative AI in FinTech: Revolutionizing Finance Through Intelligent Algorithms (ISBN 978-3-031-76956-6). Its scope is financial technology generally rather than investment banking alone.
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