We Built an AI System to Classify Bank Transactions - Demo
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Bank transaction data is messy, inconsistent, and full of edge cases, making fraud detection, risk scoring, and customer behavior analysis hard. In this demo we introduce BankTx Clf, a multi-layer classifier that combines a low-latency text model, a company-metadata database, live web search, and an AI agent to map transactions into user-defined categories. Watch the workflow in action: a transaction triggers DB lookup, falls back to web search when needed, and the reasoning agent synthesizes results and enforces standardized category output via a code-node validator. Using a 30-transaction evaluation we iterated rapidly and improved accuracy from 33% to ~80% through prompt tuning, output formatting, and explicit rules for common edge cases.
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