The Expense Tracking System is a full-stack web application designed to help users manage personal and business expenses through a secure, intuitive interface. The platform allows users to record, categorize, and analyze financial transactions in real time, providing a clear overview of spending patterns. Built with a React frontend and a FastAPI backend, the system follows modern REST API design principles and emphasizes clean, maintainable architecture.
The backend leverages FastAPI for high-performance asynchronous request handling, JWT-based authentication for secure access control, and PostgreSQL for reliable transactional data storage. The system is containerized using Docker and designed with scalability in mind, enabling smooth deployment to cloud environments. Careful attention was given to API performance, data validation, and structured error handling to ensure production-ready reliability.
To enhance usability and automation, AI capabilities were integrated to assist users with expense categorization and financial insights using natural language inputs. This aligns with real-world use cases where users can interact with the system more intuitively rather than relying on manual data entry alone. The project demonstrates practical experience in combining full-stack engineering with AI-driven features, reflecting the intersection of software engineering, automation, and applied AI highlighted in my professional experience.