ExpensifyAI: The AI Expense Tracker and Predictor

This project focuses on developing an automated expense processing system which utilizes OCR and LLMs for

invoice enhancement. Such system processes the necessary information contained in documents of various types and formats

automatically. This greatly decreases the time spent on data entry, without losing accuracy. Following extraction, data is

classified, and further processed to be more orderly with regard to finances. Users are also able to view and manage their

expenditure through an intuitive interface which allows setting filters or defining tracking criteria. One of the main features

of the system is the ability to incorporate analytics self-service – where users can analyze their spending habits based on

historical data and make predictive models, enabling them to spend less in the future. In particular, structured historical

expense data and spending for the upcoming month were evaluated using XGBoost, which also served as a financial prevision

tool due to its advanced machine learning capabilities. XGBoost does not rely on explicitly defined relationships, rather it

thrives on the existence of complex, non-linear relationships between features such as amount of expenses charged

previously, transaction frequency, category, seasonality, and trend indicators. It constructs an ensemble of decision trees

where each subsequent tree is built to increase precision, hence a greater ensemble will be more accurate. Tesseract OCR,

Django, OpenAI’s language models, and XGBoost were incorporated into a single cohesive and scalable framework that

serves the organizational and individual users personalized financial goals.

Ultimately, the result of this project is to improve spending accuracy and efficiency with respect to related spreadsheets

irrespective of the intended user. Moreover, it is scalable incorporating into pre-existing systems and thus maintaining

relevance and value over time.