AI in Investment Banking
An educational, governance-first guide for MBA/MFin students and investment banking professionals. GenAI is treated as drafting and structured reasoning support—not “autopilot deal execution.” As capability increases across the maturity ladder, risk increases too: confidentiality and MNPI controls, disclosure discipline, supervision chains, recordkeeping, and verification gates must scale so outputs remain defensible, reviewable, and reconstruction-ready.
Download and Run
The book PDF is published as a versioned GitHub Release asset. The companion notebooks live in this repository and open directly in Google Colab.
Book PDF
Download the current release edition of the book as a single PDF.
Notebooks Folder
Browse all chapter notebooks in the repository (Chapters 1–5).
Chapter Notebooks (Colab)
Each notebook is executable, auditable, and classroom-ready. Labs produce structured outputs and governance artifacts (run manifest, prompts log, risk log, deliverables bundle) so results are reviewable rather than ephemeral.
Chapter 1 — Level 1 (Chatbots)
Drafting: emails, trackers, memos, and shells with facts-first discipline and “Not verified” labeling.
Chapter 2 — Level 2 (Reasoners)
Structured reasoning: valuation logic maps, issue trees, trade-offs, assumptions and verification planning.
Chapter 3 — Level 3 (Agents)
Supervised multi-step workflow: intake → structure → draft bundle → QA → verification plan → archive.
Chapter 4 — Level 4 (Innovators)
Firm assets: reusable playbooks, evaluations, red-team suites, and controlled releases with change management.
Chapter 5 — Level 5 (Organizations)
Operating model simulation: routing, ownership, evidence, QA gates, sign-off, supervision-ready artifacts.
The Five-Level Maturity Ladder
The book’s organizing spine is a progression from individual drafting assistance to fully governed, organization-grade operations. The point is not “more AI.” The point is usefulness that survives review, inspection, and reconstruction.
- Level 1 — Chatbots: supervised drafting of emails, trackers, memos, and shells with clear labeling and redaction.
- Level 2 — Reasoners: explicit reasoning structures: valuation maps, comps rationale scaffolds, trade-offs, assumptions, verification tasks.
- Level 3 — Agents: multi-step workflows with human checkpoints, role separation, and immutable logs.
- Level 4 — Innovators: reusable firm assets (templates, playbooks, evaluations) with controlled releases and regression tests.
- Level 5 — Organizations: operating model + governance: intake, classification, QA, approvals, retention, retrieval, incident handling.
Recurring Mini-Cases (Used in Every Chapter)
The ladder is taught through recurring investment banking mini-cases so readers can isolate what changes when AI capability increases: the structure improves, the risk scales, and governance must become more explicit.
Valuation & Fairness Context Discipline
Drafting and reasoning scaffolds that surface assumptions, separate facts, and enforce verification gates.
M&A Execution (Buy-Side / Sell-Side)
Process coordination, diligence artifacts, and reviewable workstreams under confidentiality discipline.
Capital Markets (Private Placement / IPO)
Disclosure-safe drafting support with strict “no invented market claims” posture and review checklists.
Teaching / Methodology
Classroom-ready labs: repeatable cases, grading clarity, and governance-native artifacts each run.
Pedagogical Evaluation (MBA / MFin)
This section is intentionally labeled as non-authoritative. It is included to demonstrate how third-party feedback can be documented and versioned without being confused for verification of factual correctness or professional compliance.
This book has exceptionally high pedagogical value for MBA and MFin students and banking professionals because it is built as a complete implementation framework rather than a collection of prompts. It makes AI an institutional feature by teaching a stable governance model: as capability increases, controls, verification, and accountability must increase in parallel. The companion Google Colab notebooks reinforce this by producing supervision-ready artifacts—run manifests, prompt logs, risk registers, and draft deliverables—shifting learning away from “AI outputs” toward defensible deal-process design.
The recurring mini-cases allow readers to revisit the same deal scenarios with increasing rigor, creating a strong spiral curriculum in judgment, confidentiality discipline, disclosure posture, and operating-model design. Because the author is also the instructor, expectations and evaluation criteria can be enforced consistently, reducing ambiguity and superficial AI use. Overall, the book excels at teaching how banking teams should govern AI under time pressure, not merely how to use it.
- Source: OpenAI (generated evaluation text).
- Status: Not verified; included as an example of how to document independent opinion under governance-first norms.
License and Disclaimers
Educational disclaimer: This material is provided for educational purposes only. It does not constitute investment advice, legal advice, accounting advice, tax advice, or any other professional advice. A qualified human professional must review, verify, and approve any use in practice.
Confidentiality, MNPI, and data hygiene: Do not paste confidential deal information, MNPI, or data-room content into external model prompts. Use redaction/anonymization and “minimum necessary” inputs by default. Document any exception and follow firm policy and applicable law.
Professional responsibility posture: Treat all model-generated content as Not verified until validated by evidence and human review. Outputs must separate facts provided by the user from assumptions and open questions.
No fabricated sources or claims: Zero tolerance for invented citations, market facts, precedent transactions, “standard terms,” multiples, policy interpretations, or compliance conclusions. When evidence is missing, the correct output is a verification task list.
Repository license: See the repository’s LICENSE file for terms.