I

I built AI that turns 4 hours of financial analysis into 30 seconds

Hacker News

I built AI that turns 4 hours of financial analysis into 30 seconds

I built Duebase AI to solve a problem I kept running into in fintech - analyzing UK company financial health takes forever. The process usually goes: download PDFs from Companies House → manually extract data to spreadsheets → calculate ratios → interpret trends. Takes 3-4 hours per company and requires serious financial expertise. The technical challenge: Companies House filings are messy. Inconsistent formats, complex accounting structures, missing data, and you need to understand UK accounting standards to make sense of it all. My approach: Parse 15M+ UK company records from Companies House API Built ML models to extract and normalize financial data from varied filing formats Created scoring algorithms that weight liquidity, profitability, leverage, and growth trends Generate 1-5 health scores with explanations in plain English What it does: Instant financial analysis of any UK company (30 seconds vs 4 hours) Real-time monitoring with alerts for new filings/director changes Risk detection that catches declining trends early No financial background needed to understand results The hardest part was handling the data inconsistencies - UK companies file in different formats, use various accounting frameworks, and often have incomplete information. Had to build a lot of data cleaning and normalization logic. Currently focused on the UK market since I know the regulatory landscape well, but the approach could work for other countries with similar public filing systems. Link: https://duebase.com

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TrustMRRFits verified-revenue profile · Strong signals: ios · Missing: mobile apps, personal, entrepreneurs
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43%43% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
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nativeThis product was originally launched on this platform.
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17%17% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
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0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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