Document Type
Poster
Publication Date
Fall 2024
Abstract
Investment banking professionals spend significant time and effort creating client-facing pitch materials, synthesizing large volumes of financial data, and crafting compelling narratives. This project proposes a novel pipeline that leverages large language models (LLMs) and synthetic data generation to streamline and automate the creation of investment banking pitch documents. By drawing on publicly available filings and investor materials of comparable companies, the system generates high-quality, anonymized financial datasets and seamlessly transforms them into investor-ready deliverables—such as valuation ranges and pitch deck materials The result is a more efficient, consistent, and secure approach to preparing client presentations, freeing bankers to focus on strategic advisory rather than manual data wrangling.
Recommended Citation
Cleary, Dillon, "PitchAI: Streamlining Investment Banking Pitches Using LLMs" (2024). IPHS 391: Interdisciplinary AI Frontiers. Paper 4.
https://digital.kenyon.edu/dh_iphs_391/4
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.