Mobile application for analyzing and forecasting personal expenses

In the modern world, the volume of financial transactions is increasing daily, and the variety of payment methods and platforms for conducting financial operations is expanding. People are increasingly using electronic payment systems, mobile apps for payments, and digital banking services, creating a demand for more accurate and convenient tools for managing personal expenses. The ability to analyze spending and forecast future financial needs is crucial for ensuring budget stability. This research presents the development of an intelligent system for analyzing and predicting personal expenses, integrated into a cross-platform mobile application. The study includes an analysis of recent research and publications, which confirmed that the implementation of analytical and intelligent systems for expense management significantly improves the efficiency of financial control and allows for timely, informed decision-making. The literature review also showed that the integration of such systems with mobile apps is relevant, in demand, and ensures convenience and accessibility in financial management. To develop a mobile app for analyzing and forecasting personal expenses based on historical user data, forecasting methods such as Exponentially Weighted Moving Average (EWMA) and linear regression were examined. The evaluation of these methods by expense categories (e.g., food, transportation, leisure) demonstrated that EWMA consistently achieved higher accuracy, measured by WAPE and MAPE. However, forecasting accuracy depends on the availability of sufficient historical data. The prototype of the mobile app was created that uses historical transaction data obtained via an open API from Monobank, classifies expenses by category, and applies the chosen method to provide users with personalized forecasts. To ensure cross-platform functionality, the mobile app was developed using Dart and Flutter technologies, enabling solutions for both iOS and Android. Firebase Firestore and Firebase Authentication were used for data processing and secure information storage. The developed prototype features an intuitive interface with graphical visualizations of analysis and forecast results in the form of charts and graphs. The proposed approach promotes cost rationalization, improves financial planning efficiency, and enhances users' financial literacy