The main goal of this project is to build a credit card fraud detection system that uses neural networks to recognize fraudulent transaction patterns and protect users from financial loss. With the rise of digital banking and e-commerce, cyberattacks involving credit card misuse have also increased. Traditional systems often fail to detect sophisticated or new types of fraud. This project leverages the learning capabilities of neural networks to understand transaction behaviors, detect anomalies, and flag suspicious activities. Additionally, the system aims to improve detection accuracy through ensemble techniques and potentially explore real-time classification. The final deliverable will be a predictive model that serves as a cybersecurity tool for detecting financial fraud, while ensuring that legitimate transactions are not misclassified.
The project is designed over a twelve-week timeline. Initially, students will learn the foundational concepts of neural networks, install required environments (Python, Anaconda, or Google Colab), and collect transactional datasets for model training. Preprocessing will involve cleaning the data and formatting it for input into machine learning models.
In the middle phase, students will design and train a classification model using neural networks, focusing on identifying whether transactions are legitimate or fraudulent. The system will be tested against new data to evaluate accuracy, followed by improvements through hyperparameter tuning or feature engineering. In the final stages, students will integrate the components into a complete fraud detection pipeline, document the project thoroughly, and present it as a team. The system will include a basic user interface or output reporting mechanism to display classification results. However, limitations such as potential misclassifications must be acknowledged, as no model can guarantee perfect accuracy in fraud detection.
