TrackEx: A Comprehensive Expense Tracker with Stock Recommendations and Tax Management

1. Introduction

At present, the rapid and tech-oriented environment has made personal finance management an indispensable trait for individuals in order to optimize their financial wellbeing. This is because many individuals are experiencing difficulty in managing their daily routines which include, among other things, keeping up with their expenses, taxes, and even making investments. Although it is true that many digital means are available to solve such problems, most of them do not work together. An expense tracker for example will be different from tax calculations and different again from investment observations requiring the user to manage their finances across several platforms. Such an approach ends up being time consuming, inconsistent and inaccurate for the user leading to less than favorable user experience.

To address the above problem, this paper suggests a radical and promising alternative – One application that will be mobile and will comprise of an expense tracker, an income tax calculator, a payment gateway and investment advice. Such a combination of financial tools into an application would allow managemen of finances to be done with no interruption and managing entire rather than individual spheres. The system aims to manage the user’s expenses by managing them, performing calculations on the user’s tax inputs and rules and providing an overview of the possible investments from the user’s spends and assets. Moreover, a payment gateway also enables users to pay for services via the site and minimizes the need for using other applications.The system proposed has an advantage where it can provide users with advice when it comes to investment. With the aid of machine learning algorithms, the system is able to study the spending behavior and the background of the financial data of a user in order to offer the user the necessary investment opportunity such as stocks, mutual funds, or even saving plans. This helps users receive an advice that is based on their financial standing, which allows them to make an investment decision that is not contrary to their objectives.

In addition to this, a payment gateway is also added making it easier to carry out payments and also handle transactions. Users can connect their bank account, credit cards, or any other e-wallet with the application enabling them to make straightforward and secure transactions without the need to leave the application.

This capability is beneficial in easing the payment process but also in this case helps in reducing the burden of an expense management system since there are no manual entries because every transaction made is recorded and tagged appropriately.

As a result of bringing these capabilities together, the proposed system does away with many financial management applications effectively offering a complete picture of a person’s finances in one place. In this case, they aim to provide users with ways in which they can better manage their financial resources and ensure that such resources are not only spent wisely but also used in tax planning strategies as well as in profitable investments. This combined system presents an effective response to the challenges of managing personal finances in a simple and convenient manner encouraging financial understanding and enhancing glad decision making.

2. Literature Review

Moghar et al. [1] proposed a stock market prediction model using Long Short-Term Memory (LSTM) networks, focusing on short-term trends. Their study applied Recurrent Neural Networks (RNN) to datasets from companies like Microsoft and Infosys, achieving high accuracy by increasing the number of training epochs. The authors found that their model effectively predicted stock prices for short periods, with a Mean Absolute Percentage Error (MAPE) of around 5%. This work is relevant to our project, as it demonstrates the effectiveness of RNN and LSTM techniques in stock market prediction, especially for short-term forecasting, which aligns with our aim of providing accurate predictions to assist investors.

Ray et al. [2] conducted a comprehensive study of stock market forecasting, which focused particularly on more complex techniques involving ANN and SVM. In such a study, the author draws attention to the fact that stock markets’ erratic nature along with being non-linear in character pose tremendous challenges to conventional statistical methods that often fail to handle them satisfactorily. The authors demonstrate that ANN and SVM can significantly perform better than the conventional ones in predicting stock price movements. Therefore, these are much more reliable alternatives. For patterns of this kind, ANN works well in handling complex, non-linear patterns, and SVM is robust in high-dimensional spaces and can control overfitting quite effectively to ensure generalization to unseen data. In general, this research falls within the overall direction found in financial studies toward adopting more advanced methodologies for stock market predictions. The use of these sophisticated techniques gives investors and analysts an edge in the ability to make better, more informed decisions on forecasting analytics. The outcomes enhance the body of knowledge for the scholarship on financial forecasting and are also a beneficial tool for practitioners who aim to update the effectiveness of investment strategies. Thus, the work of Ray et al. indicates the potentiality of innovative approaches toward further improvement in the stock market prediction accompanied by possibly more accurate results and better risk management practices..

Naveen Kumar et al. [3] developed the "Transfer Adaptive Boosting (TAB) algorithm," which focuses on enhancing tax prediction and management through machine learning techniques. The algorithm integrates multiple weak learners, such as decision trees and logistic regression, to accurately predict tax compliance outcomes. Their research addresses challenges in tax data, particularly the difficulties posed by unannotated data and varying attribute distributions across regions. The findings indicate that the TAB algorithm significantly improves the accuracy of tax default predictions, thereby aiding tax authorities in resource allocation and compliance management. This aligns with the objectives of the current project, which aims to create a comprehensive expense-tracking application that incorporates tax calculation and stock prediction features for better financial management.

Dedhia et al. [4] developed "TraceX," a tax-tracking application designed to enhance transparency in tax payments and government spending. The system allows users to pay taxes, track transactions, and monitor how their tax money is being utilized by various government departments. By converting tax payments into tokens, the system provides detailed tracking of funds from the point of payment to their allocation for public services. This approach ensures users are kept informed about the expenditure of their tax contributions, promoting trust and accountability in government financial practices. This concept aligns with the current project’s goal of improving transparency and user engagement in financial processes.

Using data mining techniques, Izzah et al[5]. created a mobile application for stock prediction that focuses on real-time forecasting using the Improved Multiple Linear Regression (IMLR) models. Accurate forecasting is difficult since a variety of economic and social factors impact stock markets. By reducing the impact of data outliers, the IMLR technique—a cross between multiple linear regression and moving average—improves prediction accuracy. Their client-server design, which was created for the Android platform, uses the Yahoo Finance API to get real-time stock data. The software offers a smooth and user-friendly tool for stock market analysis by giving users access to both historical and predictive stock information.

The Daily Expense Tracker System was created by Singla et al[6]. as a safe and intuitive platform to meet the changing needs of users for tracking daily expenses. The system includes customized reports to improve financial transparency and has an easy-to-use interface that delivers thorough insights into monthly spending trends. In the current digital era, where effective cost management is becoming increasingly important, the system bridges the gap between user expectations and available solutions. It has strong security features including Advanced Encryption Standard (AES) to protect user data, personalized dashboards, and advanced analytics. The solution enhances overall financial efficiency and assists users in making well-informed financial decisions by merging predictive insights with spending tracking.

Ravikant et al[7]. in their study on IPAY (An Intelligent Payment System) explore the development of an efficient mobile-based payment solution designed to streamline the checkout process at supermarkets. The literature surrounding online payment systems highlights the rapid adoption of digital transactions due to the increasing accessibility of mobile technology and e-commerce platforms. Several studies emphasize the need for more secure, user-friendly payment gateways to facilitate seamless transactions while reducing the risks associated with traditional payment methods, such as fraud and long waiting times in physical queues. Various digital payment platforms like Paytm, Google Pay, and PayPal have revolutionized the way consumers interact with financial systems by providing quick, secure, and cashless transaction methods. However, the authors identify existing limitations in these platforms, such as the use of physical receipts with toxic chemicals like BPA and BPS, and long queues in stores due to manual billing processes. IPAY addresses these issues by introducing an application that allows users to scan product barcodes, generate a digital receipt, and make payments directly through the app using various methods, such as UPI or card payments. By integrating modern payment technologies and focusing on sustainability through paperless transactions, IPAY offers an innovative and time-saving solution for consumers.

Gupta et al[8] developed the "Expense Tracker," an e-application meant to track daily spending for users. This application is developed using Java and MySQL. It has an interactive GUI that enables users to categorize and monitor income and expenses effectively. Unlike traditional methods where people need to input data manually, this system follows automation when tracking so saves time and effort for its users. It is multilingual, so the users can understand their day-to-day and category-wise expenditure analyses-which can be a perfect application for a wide user base. According to the findings, this app enhances the user’s skills in managing their finances much better and leads towards effective budgeting practices along with a reduction in unnecessary expenses. This is consistent with the principal objective of the initiative, which aims to equip individuals with resources that enhance financial literacy and autonomy, thereby facilitating better financial health and self-sufficiency in the management of personal finances..

Johri et al[9]. created an Expense Management System which is intended to ease personal financial management stress by automating the process of income and expenditure management by the use of SMS data. Most of the existing work points out the increasing concern of most people living in the digital age with systems that can assist them in managing their financial transactions most easily and quickly as possible. Some of the past research has focused on mobile device applications that involve technologies like Optical Character Recognition (OCR) for receipts imaging, and messages text content retrieving for automatic expense control. Over the decades, research targeting the area of personal finance management tools has shown that there is a huge need for these tools to enable users to track and classify their expenditures faithfully for the users to appreciate their spending habits. Other studies point out that even these systems can be made better by including AI and for instance, with predictive modeling, there is room for better recommendations based on the users’ actions. The introduction of AI-powered tools into a personal finance management system makes financial data dynamic, the budgeting process interactive, and decision-making effective. Incorporating these technologies, Johri et al.’s expense management system enable the user to track spending behavior and generate alerts on financial goal progress. This is a system that is of interest to people who wish to increase their savings and manage their outgoings through a simple and easy-to-use system.

3. Conclusion

Today’s survey puts forward growing importance to cross-functioning expense tracking applications with both functionality in tax calculation and stock forecasting to realize effective management of personal financial transactions. By including machine learning algorithms in the input for both stock forecasting and assessment, outstanding prospects for offering users practically relevant insights both in short-term financial planning and long-term investment strategies have been observed. These applications, through automated expense tracking, tax liability prediction, and stock trend forecasting, enable an individual to make reasonable decisions with regard to their finances. It fulfills a niche in today’s digital economy by combining features in one platform towards financial literacy and simplification of taxes and investments. Future advancements in predictive algorithms combined with better user experience will dramatically improve the effectiveness of these tools, making them indispensable to personal money management.

4. Future Scopes

Future innovations for the developed application should consider making it an integrated financial management tool with online payment facilities, automated expenditure tracking, support for tax filing, investment advice, and cross-platform compatibility.

Among others, including secure online payment mediums like UPI, credit cards, and digital wallets, integrating these would allow a seamless transaction experience for users. This would also ensure that payment becomes simpler and much easier to manage with automatic categorization of expenses so that there won’t be too much manual entry, thus optimizing the expenditure tracking process.

There could be an AI-driven expenditure calculator, driven and promoted for better budgeting. This system would analyze the expenditure patterns, automatically categorize transactions and provide insight into financial habits. This kind of automation would be able to create a clear overview of the expenditures that the user could then use for making educated financial decisions.

Besides tax calculations, this app will also support filing. It is going to generate pre-filled tax forms, keep documents for users, and remind them of the due date well in advance, thereby making compliance and tax management easier and more efficient for the users.

They would give the user investment advice or guidance through prediction of stocks and personalized investment strategies. Portfolio tracking features can also provide users with a real-time view of investments, and they can do careful asset management, including stocks and mutual funds.

Finally, cross-platform compatibility with Android, iOS, and web versions supported by cloud storage means that users will be able to take their financial information with them everywhere and have access from whatever device. Such flexibility would make for a great user experience as they’d easily move between the disparate devices while keeping their finances at their fingertips in one streamlined financial management system.