Utilizing a deep learning model for managing institutions income and expenses via a Web-Based Interface

Abstract

One of the reasons leading to poverty is less care to plan and know about income and expenses and carelessness about the expected result analysis. As a small institution, the families have not yet utilized the platform to supervise and manage their spending and income patterns. Instead, they manually carry out their financial transactions, which leads them to the loss of data, missing reports, and not being able to make self-control and analysis about their financial status. Income and expense system help to do the financial activities smartly where its users can be able to set their estimated budget, recoding their actual income and expenses, get the reports, and find the generated future budget. Hence, a deep learning-based income and expense model is developed to facilitate organizations, institutions, small businesses, cooperatives, families, and individuals in their finances. To conduct this study, Deep Neural Network (DNN) is a single model used to make predictions of future budget where we will have a website to facilitate users to insert their data in the database; from stored data, we will find a dataset that plays a role in deep learning Model training, testing and future budget prediction. A waterfall approach was used in the development of User interfaces to facilitate user model communication. Tools used to conduct this research are Python, Tensor flow, and Keras however, the experimental techniques used are Machine learning with a deep learning algorithm, for data collection technics, and we use observation, interviews, and documentation. As result, the users are able to find all financial information, financial reports, and future budgets are well predicted and displayed to them.