Spending Pattern Explainer for Uploaded Transaction Lists

Added Nov 2025 3 design docs

Most people cannot say where their money actually goes each month, and banks increasingly compete on helping customers understand their own behavior. Behind every such feature is a system that finds patterns in transactions and explains them in words a customer immediately grasps. In this project the intern builds that system in miniature. Users upload anonymized banking transaction lists, and python code parses and cleans the records, grouping them by merchant, category, and timing. A langgraph workflow structures the analysis into stages that detect common patterns, such as recurring subscriptions, weekend spending spikes, or regular deposits, and then pass each finding to a generation stage where openai models write a short, friendly explanation of the pattern and what it suggests. An agentic-ai layer manages the flow between detection and explanation, ensuring every notable pattern receives a clear write-up. The result is an annotated report of the user's financial habits. The intern learns practical data analysis, simple pattern recognition, and AI-powered explanation, and completes an accessible project that mirrors personal-finance features shipped by real retail banks.

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