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Show HN: Finsight – A Privacy First, AI Credit Card and Bank Statement Analyzer

6 points by aj 7mo ago

AI-powered personal finance analyzer — runs 100% locally, no cloud, no login. Upload a PDF / CSV / Excel bank or credit card statement → AI extracts and categorizes every transaction → interactive dashboard, spending insights, recurring payment detection & chat with your data.

aj 7mo ago
I built FinSight because I wanted to analyze my spends, inflow and outflow. But I did not want to upload a statement to a cloud LLM for data privacy. Finsight provides LLM-assisted transaction categorization without uploading bank or credit card statements to a 3rd Party service. Architecture: PDF parsing client-side via pdfjs-dist, AI inference via local Ollama/LM Studio API, storage in localStorage/sessionStorage via Zustand. No backend (yet) A few things I found technically interesting: 1. Context window management is the main challenge with long statements. I'm handling it by chunking transactions and doing a second pass aggregation. It works but it's the messiest part of the codebase — would genuinely value feedback on better approaches. 2. 1B parameter models are sufficient for parsing. 7B models give meaningfully better categorization accuracy. The main constraint isn't model capability — it's context window length with large statements and speed. 3. Personally, Qwen 3 gave me the best results but was the slowest in processing a large file. Gpt-oss-20b was faster but the categorization wasn’t as good. Speed is of course, hardware dependent. 3. PDF statement formats vary enormously between banks. LLM-based extraction handles this variation better than any regex approach I’ve tried. Caveats: setup requires Ollama or LM Studio plus a model download, which is 20-30 minutes on a fresh machine. Installation & Demo Video - https://youtu.be/VGUWBQ5t5dc https://youtu.be/VGUWBQ5t5dc GitHub - https://github.com/AJ/FinSight?utm_source=hackernews&utm_medium=post&utm_campaign=finsight https://github.com/AJ/FinSight?utm_source=hackernews&utm_med... MIT licensed.

6 points by aj 7mo ago

aj 7mo ago

I built FinSight because I wanted to analyze my spends, inflow and outflow. But I did not want to upload a statement to a cloud LLM for data privacy. Finsight provides LLM-assisted transaction categorization without uploading bank or credit card statements to a 3rd Party service. Architecture: PDF parsing client-side via pdfjs-dist, AI inference via local Ollama/LM Studio API, storage in localStorage/sessionStorage via Zustand. No backend (yet) A few things I found technically interesting: 1. Context window management is the main challenge with long statements. I'm handling it by chunking transactions and doing a second pass aggregation. It works but it's the messiest part of the codebase — would genuinely value feedback on better approaches. 2. 1B parameter models are sufficient for parsing. 7B models give meaningfully better categorization accuracy. The main constraint isn't model capability — it's context window length with large statements and speed. 3. Personally, Qwen 3 gave me the best results but was the slowest in processing a large file. Gpt-oss-20b was faster but the categorization wasn’t as good. Speed is of course, hardware dependent. 3. PDF statement formats vary enormously between banks. LLM-based extraction handles this variation better than any regex approach I’ve tried. Caveats: setup requires Ollama or LM Studio plus a model download, which is 20-30 minutes on a fresh machine. Installation & Demo Video - https://youtu.be/VGUWBQ5t5dc https://youtu.be/VGUWBQ5t5dc GitHub - https://github.com/AJ/FinSight?utm_source=hackernews&utm_medium=post&utm_campaign=finsight https://github.com/AJ/FinSight?utm_source=hackernews&utm_med... MIT licensed.