Hidden spend detection - bank/card CSV or PDF to plain-English fixes

Executive Summary

Many small, local businesses struggle to find recurring, wasteful expenses in messy bank statement exports and PDFs because accounting is time constrained and tools either require clean integrations or expensive human reconciliation. This problem affects an estimated 50 million addressable small businesses globally that would pay a modest diagnostics fee or $10 per month for a solution that delivers clear, immediate savings. You could build a low-touch SaaS that ingests CSVs and PDF statements via connectors and OCR, applies hybrid ML plus rule-based logic, and outputs plain-English fixes ranked by annualized savings with one-click export to popular accounting systems. Charge a $10 monthly plan plus a higher-priced annual plan around $120 ACV, with an option for a paid one-off diagnostics report that converts hesitant buyers. The market is attractive now because SMB digitization, improved OCR and bank connector tooling, and post-pandemic cost pressure make owners willing to pay for measurable cost reduction; the model maps to a $6.0 billion TAM using 50 million SMBs at $120 ACV. Strengths include clear ROI messaging, relatively low engineering complexity for MVP, and defensibility through data and patterns learned across customers. Challenges will be data quality variability, privacy and compliance requirements, and a medium-competition landscape that requires rapid trust building; differentiation comes from combining high-accuracy parsing, transparent savings calculations, human-in-the-loop verification for early customers, and embedded workflows that turn detected issues into immediate corrective actions.