Question-Driven Explorer for Banking Transaction Datasets

Added Nov 2025 3 design docs

Analysts exploring a new banking dataset, whether transaction logs or customer profiles, spend their first days just orienting: what is typical, what is anomalous, and where the interesting segments are. An exploration tool that answers natural-language questions about the data collapses that orientation period from days to minutes. The intern builds the tool in python with a streamlit interface. Users upload anonymized banking datasets, and the system creates embeddings of records and summaries, storing them in a vector database. From there, exploration is conversational: a user asks about spending trends, unusual clusters, or how one customer segment differs from another, and a retrieval-augmented generation pipeline retrieves the most relevant data points and slices while generative AI composes a summary of what the data shows. RAG keeps each answer tied to actual retrieved records, and the interface lets users drill from any summary into the underlying rows to verify it. The project develops a distinctive skill set: representing tabular financial data for semantic retrieval, pairing retrieval with faithful summarization, and building an analytics experience where AI accelerates human judgment instead of replacing it.

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