Our project addresses the problem of disorganized expense data caused by the abundance of finance apps, which often provide poor insights due to manual and limited filter options. To solve this, we developed a robust financial management solution that utilizes advanced AI and automation. The solution begins with PDF bank statement parsing, where users upload statements through an HTML interface. The Python Flask server then sends these documents to the Upstage API for document parsing. The parsed data is retrieved, organized, and categorized using Solar LLM before being stored in an SQLite database. This ensures structured and meaningful financial data for better insights. Users can also ask customized financial questions via HTML forms, which are processed using TogetherAI’s Llama 3.1 70B Instruct Turbo model. The system generates a corresponding SQLite query to extract the required data, performs necessary calculations, and returns the relevant information. Future improvements include AI fine-tuning for more accurate results, enhanced data visualization tools, advanced query options for more detailed insights for real-time updates and seamless user experience. Integration with Hedera and direct banking APIs for tampered-proof data for legal and audit purposes.
Category tags:"Nice use of the sponsor's tech, very barebones demo though"
JLD Adriano
"Good idea, don't see a differentiator from services like Mint that connect to your bank account and can track your expenses"
Galina Fendikevich
"I really like this project—it’s extremely practical and taps into a big market need for better bank transaction understanding and expense tracking. The idea of automating expense management is great, and using document parsing with Upstage for private financial data is a brilliant application. However, my main feedback is that the user interface needs improvement. Right now, it’s not very polished, and integrating something like Streamlit or asking Claude or Sonnet to generate a cleaner web app could help. Also, while the chat interface has potential, I’m not clear on how it’s better than using a regular ChatGPT. For example, I didn’t see math baked into your system, so it seems like a simple wrapper at the moment. You should clarify why this is a uniquely hard problem, rather than something ChatGPT can already do. Math-based reasoning with LLMs can be tricky, and incorporating safeguards for accuracy would improve the project. Overall, it’s a cool project with a lot of potential, but refining these details could take it to the next level."
Alex Reibman
CEO