Recurring Transaction Pattern Detection and Reporting System

Added Nov 2025 3 design docs

Underneath every personal finance insight feature is a harder engineering task: reliably detecting recurring structures in messy transaction data, including subscriptions that change price, salaries that arrive a day early, and transfers that repeat irregularly. This project tackles that detection problem and pairs it with generated explanations and reporting. The intern builds a python system that ingests uploaded anonymized banking transaction data and parses it into a normalized form. A langgraph-orchestrated workflow runs the analysis in stages: candidate patterns are identified from timing, amount, and counterparty regularities; each detected pattern is characterized and visualized in output data artifacts; and openai models generate clear, beginner-friendly explanations of what each pattern is and why it was flagged. An agentic-ai layer manages progression between detection, visualization, and explanation, retrying weak explanations and consolidating overlapping findings. Final deliverables are structured data artifacts plus readable text reports describing each recurring pattern. The intern practices data parsing, heuristic pattern detection, workflow automation, and AI-assisted reporting, a combination that maps directly onto analytics engineering roles in banking.

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