Abstract:
The Smart AI-powered Expense Tracking System is a modernized personal financial
management tool aimed at overcoming limitations associated with classical budget
applications. The first main purpose of this innovative application is to get rid of
problems connected with excessive reliance on human intervention and outdated
ways of processing information. To this end, the Smart AI Expense Tracking System
incorporates modern internet-based tools, rule-based financial calculations, and an
LLM model of conversation with access to external APIs.
The most distinctive characteristic of the proposed system is its artificial intelligence
component. The AI-powered financial advisor makes it possible for people to
communicate with the software using regular language. It means that any person
can use natural language to find out more about his/her expenditure habits, saving
practices, budgeting methods, and financial behaviors in general. Based on the
provided context, the artificial intelligence processes all queries and provides
answers to them.
Geolocation features are integrated into the app's functionality to improve its ability
to understand context from the point of view of user behavior. Through recognizing
the presence of the user at certain places (such as retail shops, restaurants, or
transportation points), it becomes possible to deduce the spending activities
associated with being in these locations, which helps to ensure that expenses are
logged promptly. Therefore, the need for manual data entry is significantly reduced
and data accuracy increased.
FastAPI serves as the foundation for the backend architecture of the application,
ensuring excellent speed and performance capabilities of the server.
All functions are performed by this framework, which includes user authentication,
spending and budget tracking, requests related to AI algorithms and input
validation.
As for data storage, a light and effective SQL database named SQLite is used to
store user data, including user profiles, budgets, and spending locations.
In addition to that, the system is built with a significant focus on data visualization
and UX. Data concerning finances of the users is provided by means of interactive
dashboards, graphs, and chart representations, which make it easier for the users
to get insights into their spending. Thus, this system makes visualizations out of
complicated data in order to help users analyze their spending patterns.
