Show HN: Auto-Match – How We Built Receipt-to-Transaction Matching (Open Source)

Summary

Financial reconciliation has always been one of the most tedious aspects of running a business. Manually matching receipts to bank transactions is time-consuming, error-prone, and frankly, mind-numbing. That's why we built a reconciliation engine that does this automatically with high accuracy. Today, we're pulling back the curtain on how our automatic transaction matching works. From the preprocessing pipeline that cleans and enriches your data, to the algorithms that learn from your behavior and get more accurate over time. The Problem: Why Clean Data Matters More Than Ever Traditional reconciliation systems rely on exact matches—same amount, same date, maybe some basic text matching. But real-world financial data is messy: Currency conversions create slight discrepancies in amounts Banking delays mean transaction dates rarely match receipt dates exactly Merchant names vary wildly between receipts and bank feeds ("Apple Inc." vs "APPLE.COM/BILL" vs "APL*ITUNES.COM") Processing fees and exchange rates introduce small amount variations This messiness means traditional systems either miss obvious matches or flood you with false positives. We needed something better. Our Approach: Multi-Dimensional Matching Our reconciliation engine doesn't just look at one or two data points. It analyzes transactions and receipts across multiple dimensions simultaneously, using embeddings to understand the semantic meaning behind the data. 1. Data Preprocessing & Enrichment Before any matching happens, we run both transactions and inbox items through an extensive preprocessing pipeline: Transaction Enrichment: Merchant name standardization using our provider network Legal entity resolution (connecting "AMZN" to "Amazon.com, Inc.") Category prediction based on merchant patterns Currency normalization with real-time exchange rates Document Processing: OCR extraction for receipts and invoices Tax rate and type detection (VAT, GST, Sales Tax) Date parsing with format normalization Amount...