Automatic Section Tagging for Uploaded Banking Documents

Added Nov 2025 3 design docs

Banks receive and produce documents constantly, including account agreements, KYC files, loan applications, and audit reports, and someone must label each one so it can be found again. Manual tagging is tedious and inconsistent, which makes document classification one of the most immediately useful applications of AI in back-office work. The intern builds a streamlit application in python where users upload banking documents and receive automatic tags for key sections and topics. The system embeds reference content in a vector database, and a retrieval-augmented generation pipeline matches each document section against known topics, while generative AI produces concise, descriptive tags such as fee schedule, dispute procedure, or collateral terms. The interface displays the document alongside its generated tags so users can review, accept, or adjust them, reinforcing the idea that AI organizes and humans confirm. Through the project the intern learns document segmentation, semantic matching with embeddings, and controlled tag generation with RAG. It is an approachable first build that still mirrors production document-intelligence systems, and it leaves the intern able to explain how AI-powered content organization works end to end.

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