Business Insight Generator for Banking Scenarios and Data

Added Nov 2025 3 design docs

Turning raw observations into a recommendation is the daily work of banking analysts: a branch sees deposit outflows, a product team sees rising card usage in one segment, and someone has to say what it means and what to do. This project builds a tool that assists that leap from data to insight. The intern creates a streamlit application in python where users describe a banking scenario or paste in summary data. The system embeds a library of prior cases and analytical patterns in a vector database, and a retrieval-augmented generation pipeline finds the situations most similar to the user's input. Generative AI then drafts actionable insights and recommendations that are grounded through RAG in those retrieved precedents, so suggestions echo what has actually been observed rather than generic advice. The interface presents the insight, the supporting cases, and the reasoning in a format a business reader can scan quickly. By building it, the intern learns how retrieval and generation combine into a decision-support tool, how to structure a case library for useful similarity search, and how to present AI-generated business advice with appropriate transparency.

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