Fine Catch

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  Know what you are signing.

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  Student financing doesnt stop at borders.

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  The problem isnt always the loan.

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  Build for students.

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  All the works does fine catch do.

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  Fine Catch

Inspiration- Student-loan agreements can contain important financial details spread across pages, tables, clauses, and small print. I wanted to build something that could help students understand what they are actually agreeing to instead of simply giving them another document summary. That led to Fine Catch: an AI-powered tool that helps students see, catch, explain, calculate, and verify the important details in their loan document. How I Built It -
I built Fine Catch as a web application that accepts student-loan documents such as PDFs, images, and scanned pages. The application processes the document, identifies important financial information, and organizes the results into understandable findings. Fine Catch looks for information such as interest rates, fees, repayment terms, penalties, grace periods, prepayment conditions, and missing or unclear information. Each finding is connected back to its source in the document so users can verify where it came from. I also built financial calculations that clearly distinguish between information found directly in the document and values calculated by Fine Catch. The application avoids inventing information when the document does not provide enough data. What I Learned- The biggest lesson was that document AI is not just about extracting text. The important part is understanding the relationship between the information, its location in the document, its meaning, and its financial impact. I also learned how important it is to make AI outputs traceable. A result is much more useful when a user can immediately check the original clause or page behind it. Challenges- One of the hardest parts was making the AI connect its findings to the original document instead of producing generic answers. Handling scanned documents, tables, small print, conditional clauses, and missing information also required careful design. Another challenge was preventing the system from presenting calculations or assumptions as facts. I designed Fine Catch to clearly separate document-stated information, Fine Catch calculations, and information that is not specified. Building the complete experience also meant working through document processing, AI analysis, calculations, source mapping, interface design, and error handling as one integrated system. What Fine Catch Became- Fine Catch is more than a document summarizer. It is designed to give students a clearer way to understand, question, and verify the financial terms inside student-loan agreements before signing them.

Built With

• replit