TransactGuard: A mobile-based Credit Card Fraud Detection System integrated with a Machine Learning Model
The project focuses on the development of a sophisticated machine learning-powered credit card fraud detection system tailored for integration into a mobile application, specifically Android. The primary objective is to empower banks to proactively identify and prevent fraudulent transactions, enhancing the overall user experience. Leveraging state-of-the-art machine learning algorithms, the system analyzes real-time transaction data to swiftly recognize irregular patterns indicative of potential fraud. Continuous learning and adaptability are pivotal aspects of the system, ensuring preemptive fraud mitigation. Through regular algorithm updates, the system remains well-informed about emerging fraud trends, fortifying its efficacy. The application incorporates reporting and analytics features, providing valuable insights to banks for fine-tuning their fraud detection strategies. The ultimate goal is to provide a comprehensive mobile application solution that benefits both financial institutions and customers, fostering a more secure credit card ecosystem. The project's success relies on the guidance and expertise of the project supervisor in shaping the trajectory, defining data requisites, crafting robust machine learning models, and devising optimal deployment strategies.
The Android application presents users with options to get started, register for an account, or log in if they already have one. After authentication, users access the dashboard, featuring three main cards:
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Profile Card: Allows users to view and edit their personal information.
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Credit Card Transaction Card:
- Normal Users: Initiate credit card transactions by entering details (credit card number, amount, and merchant ID). Transaction data is sent to the integrated model for analysis.
- Admin Users: Receive transaction requests from normal users, conduct analysis, and provide feedback regarding the transaction's status.
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Message Card: Reflects transaction history and actions taken by both normal and admin users.
The integrated model analyzes transaction data from normal users, and results are sent back to admin users for appropriate actions. Admin users can approve or flag transactions based on insights from the model. Real-time notifications are sent to normal users about the approval or flagging of their transactions.
The application ensures the security and privacy of user data throughout registration, login, and transactions, employing secure communication channels. With a user-friendly interface and intuitive design, the application offers a seamless experience for users engaging in credit card transactions while incorporating real-time analysis to detect and respond to potential fraud.