Plain-Language Explanation Generator for Bank Transactions

Bank statements are full of cryptic merchant codes, abbreviations, and reference numbers that leave many customers unsure what they actually paid for. Support teams field a steady stream of questions that could be answered automatically if raw transaction lines were translated into clear, human-friendly sentences. In this project the intern builds exactly that translator. Users supply anonymized banking transactions, and a python program parses each entry, cleans the fields, and passes them through a generation pipeline that uses openai models to produce a short, plain-language explanation of what the transaction likely represents. The workflow is structured with langgraph, which defines the steps from ingestion to explanation, while an agentic-ai coordination layer decides how each record moves through parsing, generation, and output formatting. The finished tool reads a list of transactions and returns an annotated version anyone can understand. Along the way the intern practices careful data handling, prompt design for financial text, and workflow automation, and finishes with a working example of generative AI applied to a genuinely common retail banking pain point.