The primary aim of the present research project is to design and develop a personal finance management mobile application that utilizes advanced features to enhance the efficiency of money management, thereby assisting users in achieving financial success without requiring any substantial knowledge of personal finance management. The mobile application will be characterized by its smart and engaging interface, which will incorporate advanced functionalities, including the ability to automatically detect bank SMS and extract details of expenses, as well as identify bill images and extract relevant expense information. The application will also feature automated expense classification and insights generation, which will be presented to users in a visually appealing manner, as well as automated budget planner components that analyze user spending patterns. Additionally, the application will include an informative chatbot feature to provide answers to users’ finance related queries. Finally, the mobile application will also incorporate a future expense prediction feature based on events planned in users' personal digital calendars.

After the bill image reading and SMS extraction processes, unclassified unstructured expense data that was recorded will be used as inputs for expense classification and accurately classifies the expenses into the corresponding matching expense categories. Based on the expense data classified systems will display automated graphical representations, such as line graphs, to illustrate current variations in expense categories and how expenses affect the user's cash flow because visual representations make it simple, quick, and convenient for people with low financial literacy and language proficiency to understand how expenses behave. Furthermore, Insights will be generated based on the expense classified into expense categories. And as the user updates the application with new expenses, the amounts in expense categories will also change. Based on those variation analyses expense insights will be generated.

Aims to provide an effective way to plan next month's budget for the user by developing a system that analyses users’ past expenses, to forecast the expenses so that the system can allocate the appropriate 41 amount of money to each spending category (Food, Grocery, Bills, Personal Care, Transportation, Medicine). The system will create an effective, personalized budget plan for users. When there is a budget for each month it will be a great help because at first glance users can understand how much they are supposed to spend for each of their needs, and it will prevent users from spending money on unnecessary items as well. Also, it will provide financial assistance to users by implementing a chatbot. This chatbot is capable of answering questions that users have related to the financial landscape of Sri Lanka. The availability of reliable sources of information will help users to stay informed about financial situations.

Aims to help users manage their finances by predicting expenses for upcoming events. It will collect real calendar event data using Google App Script and Google Calendar API, which will be triggered monthly. A survey will be conducted to gather demographic data and analyze how expenses vary across different event categories. Preprocessing techniques will be used to cleanse the natural language event data, removing noise and irrelevant information. A classification model will be built to filter monetary events from users' calendars. Expense amounts for these events will be predicted based on user demographic data, monthly income using a trained regression model, and users will be notified of the estimated budget. Users can confirm, update or ignore the predicted amount, which will be added to their budget plan. A mobile app ensuring privacy and usability will be developed, granting users permission to access calendar events and displaying predictions in an easily understandable manner with simple user interfaces.