Executive Summary

Many retail investors (≈20M active) and ~100K professionals waste time parsing noisy SEC filings (Form 4/3/5) and basic feeds that don’t reliably distinguish true signals—buybacks vs option exercises, prearranged 10b5‑1 sales, or insider sales clustered before material events—so they miss actionable patterns or get false leads. Analysts and advisers also face manual work to normalize transactions across issuers and time series. You could build an API-first product plus consumer app that ingests filings, applies NLP to extract and classify transactions into a richer taxonomy with confidence scores and timelines, visualizes aggregated insider behavior, and exposes embeddable signals and watchlists for fintech partners. Offer a consumer tier (~$10 ARPU) and a pros/professional tier (targeting ~$7K ACV) to capture both retail and B2B distribution. This is an attractive moment: retail and prosumer demand for data-driven signals is rising, and the $1.2B TAM (20M × $10 + 100K × $7K) supports both subscription and enterprise routes while API-first fintechs open B2B2C channels for embedding premium signals. Advances in NLP make reliable extraction practical at scale, reducing a historic technical barrier. The real edge is combining higher signal‑to‑noise via automated, well-tested extraction and a deeper transaction taxonomy with modern UX and embeddable APIs so partners can surface patterns rather than raw dumps. Key challenges are achieving and maintaining >90% extraction/classification accuracy, handling edge cases and compliance, and funding distribution—if you solve accuracy and demonstrate measurable predictive lift, this idea has clear commercial legs.

Market Opportunity

Track and classify insider transactions with modern UX and deeper granularity targets a $1.2B = 20M active retail investors × $10 ARPU + 100K professionals × $7K ACV total addressable market with medium saturation and a year-over-year growth rate of 10% YoY — based on growth in fintech subscription services and increased retail trading activity (Statista and industry reports, 2022-2024).

Key trends driving demand: Retail and prosumer demand for data-driven signals is rising, creating room for verticalized data products that surface actionable patterns rather than raw dumps.; API-first fintech adoption is growing, which opens B2B2C distribution channels where other fintechs embed premium insider signals.; Advances in NLP allow reliable extraction and classification of semi-structured SEC filings, making richer transaction taxonomies practical at scale..

Key competitors include OpenInsider, GuruFocus, WhaleWisdom.

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Track and classify insider transactions with modern UX and deeper granularity

Executive Summary

Market Validation

Market Opportunity

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