WealthGuardian-Smart Expense Management using Machine Learning

International Journal of Nature and Science Advance Research , 2025

Expense Analyzer is a mobile-based financial management system designed to help individuals and institutions monitor, categorize, and analyze their expenses efficiently. Financial mismanagement is a challenge faced by many due to the lack of real-time expense tracking tools. We adopted a structured software engineering methodology (SSADM) and implemented the system using React Native, Firebase, and Firestore to support Android and iOS platforms. The system allows users to log transactions, generate reports, receive budget alerts, and visualize spending patterns. Expense Analyzer simplifies personal and institutional budgeting, enabling better financial decisions, accountability, and organization.

International Journal of Recent Technology and Engineering, 2020

The need for skilled financial advisors is more than ever in the current scenario when there are in-numerous moneymaking strategies and at the same time, the global economy might be on the verge of collapse. Also there is a pure lack of good financial advisors and even if you find one, you will end up paying a hefty amount. The current proposed application fulfils the above-stated demand in a cost-effective and reliable way. The proposed system automates the job of a financial advisor using Artificial Intelligence. It provides the user with a simple and easy to use interface where every individual will have their own account handled by Google’s firebase platform. The application uses 'Plaid' API which allows app to send a request to the corresponding bank server and fetch the account details of an individual. Logged in user is shown a very comprehensive representation of their account details which also includes category-wise expenditure, their investment, and the savings. O...

TENCON 2019 - 2019 IEEE Region 10 Conference (TENCON)

As this meticulous journey comes to end, we have come across many well-wishers whose invaluable contribution and assistance made this project successful. Our beloved Professor, Mr. Hossain Arif is one such person. His continuous support and guidance has kept us motivated and enabled us to present our best in all cases. We would also like to thank Mr. Samiul Islam and Dr. Iftekharul Mobin for their continuous encouragement and expertise in this field. Last but certainly not the least; we would like to show our appreciation towards our families without whose unconditional support none of this would have been possible.

Banber Hayastani petakan tntesagitakan hamalsarani, 2022

There are many problems in each credit institution. The most important of them is the risk of possible losses in lending. Within the framework of the topic, the studies conducted by other researchers were investigated, from which it was concluded that machine learning tools are often used to optimally solve the above-mentioned problem. Real data on credits were used as a basis for modeling in the work. In this work, based on the available data, several machine learning models were developed, from which the best one was selected, which can contribute to the improvement of the credit risk management process. During the work, the logical connections between data and their interaction with each other were revealed. Then, based on the work done, the appropriate models were built, the quality of which was checked using various tools. The obtained models were compared and the best one was selected. The obtained results are practically applicable and show that each bank and credit organization can develop a better solution based on the large databases they have, which will contribute to curbing credit risk and reducing costs.

IEEE Access

The COVID-19 pandemic has adversely affected households' lives in terms of social and economic factors across the world. The Malaysian government has devised a number of stimulus packages to combat the pandemic's effects. Stimulus packages would be insufficient to alleviate household financial burdens if they did not target those most affected by lockdowns. As a result, assessing household financial vigilance in the case of crisis like the COVID-19 pandemic is crucial. This study aimed to develop machine learning models for predicting and profiling financially vigilant households. The Special Survey on the Economic Effects of Covid-19 and Individual Round 1 provided secondary data for this study. As a research methodology, a cross-industry standard process for data mining is followed. Five machine learning algorithms were used to build predictive models. Among all, Gradient Boosted Tree was identified as the best predictive model based on F-score measure. The findings showed machine learning approach can provide a robust model to predict households' financial vigilances, and this information might be used to build appropriate and effective economic stimulus packages in the future. Researchers, academics and policymakers in the field of household finance can use these recommendations to help them leverage machine learning.

2020 Turkish National Software Engineering Symposium (UYMS), 2020

The aim of this study is to develop an effective and interactive application that helps users regulate their expenditures and encourage them to save. In this application; it is aimed to estimate the forward cash flow using personal regular and irregular historical financial data and future full or partial specific financial data and the dynamic date estimation of when a person will reach a target for accumulation after a long term is aimed with the information obtained in this process. This prediction will benefit the user by supporting cash savings or in a loan plan. In the first stage of the proposed system, a fast microservice based architectural system that collects instant data to be used in forward cash flow estimation is designed. The second part of the study is the estimation of the nearest possible date to reach an accumulation amount target by adapting the most frequently used neural network algorithms and applications in the literature.

Mathematics, 2024

This paper describes the results from a study designed to illustrate the use of machine learning analytical techniques from a household consumer perspective. The outcome of interest in this study is a household's degree of financial preparedness as indicated by the presence of an emergency fund. In this study, six machine learning algorithms were evaluated and then compared to predictions made using a conventional regression technique. The selected ML algorithms showed better prediction performance. Among the six ML algorithms, Gradient Boosting, kNN, and SVM were found to provide the most robust degree of prediction and classification. This paper contributes to the methodological literature in consumer studies as it relates to household financial behavior by showing that when prediction is the main purpose of a study, machine learning techniques provide detailed yet nuanced insights into behavior beyond traditional analytic methods.

Granthaalayah Publications and Printers, 2023

An increasing number of customers are applying for personal loans for their purchases. Personal loans help the consumer to meet any shortfall they experience in buying a needful item. fulfill their needs consumer apply for loans. As many loan application increases, day by day, bank needs to detect and analyze which customer is eligible for a personal loan. The term banking can be defined as receiving and protection of money that is deposited by the individual or the entities. The primary objective of the bank is to provide their wealth in safer hands. In recent times, banks approve the loan after verifying and validating the documents provided by the customer. Yet there is no guarantee whether the applicant is deserving or not. This paper classifies customers based on certain criteria. In this paper, the main focus is to identify and analyze the risk of giving a loan in the banking sector. The Machine Learning techniques are used to analyze risk giving loan, it helps in summarize into a piece of valuable information. This will improve the quality of banking system thus improving customer retention. Applying it on a dataset of the customer and predicting the risk percentage for an individual to give loan. These techniques facilitate useful data analysis and can help to get better outcomes into the processes behind the data. The banks and many investment companies are pioneers in taking advantage of Machine Learning. The main objective is to predict whether assigning the loan to a particular person will be safe or not.

IJRCAR, 2022

The present global economic crisis makes it difficult for banks to attract customers. Therefore, marketing is seen to be a useful technique for the banking industry to get clients interested in a term deposit. In banks, telemarketing is a commonly used kind of direct marketing. Customers seldom react favorably, therefore data prediction models may assist in identifying the most probable potential clients. Data mining helps direct marketing efforts succeed by foretelling which leads will sign up for term deposits. In this study, we used machine learning to the benchmark dataset of banking institutions' direct marketing campaigns to create an accurate classifier to forecast which consumer would accept a long-term deposit offer. Our research reveals the remarkable influence that machine learning methods may have on the outcome of a telemarketing campaign. Data preparation and model assessment are the two main phases. In the first phase, data must be cleaned by removing duplicate records and determining if missing values should be kept or removed, data visualization, and utilizing the response coding approach to encode category characteristics using label and one-hot encoding. The dataset is originally split into training and testing but the dataset is unbalanced so we needed to consider that while training so we used the balanced class weight approach and 10-fold cross-validation to solve the imbalanced class problem. The Random Forest algorithm is used for training and testing and a perfect classifier is achieved. The proposed system outperformed all the state-of-the-art techniques and achieved perfect classification.

Artificial Intelligence, 2021