Design and Implementation of Credit Fraud Detection System

Abstract Credit card fraud represents a significant threat to financial institutions and consumers, necessitating robust and efficient detection systems to mitigate associated risks. This study focuses on the design and implementation of an advanced Credit Card Fraud Detection System (CCFDS) that leverages state-of-the-art machine learning techniques and data analytics to identify and prevent fraudulent activities in real-time. The system made use of HTML coding (XAMPP). The proposed system integrates multiple detection algorithms, including supervised learning models such as Random Forest, Gradient Boosting, and neural networks, as well as unsupervised learning methods like clustering and anomaly detection, to enhance detection accuracy and minimize false positives. Key components of the system include a comprehensive data preprocessing module to handle the high volume and variety of transaction data, feature engineering techniques to extract relevant patterns indicative of fraud, and a model training and evaluation framework to ensure the system's robustness and adaptability to evolving fraud tactics. The implementation also emphasizes scalability and real-time processing capabilities, employing cloud-based infrastructure and parallel processing techniques to handle large datasets and high transaction speeds. To ensure consistent performance over time, the system also includes continuous learning algorithms that update models depending on fresh data. In order to help fraud analysts make better decisions, future work will concentrate on improving the system's functionality, investigating the integration of new data sources, and improving the interpretability of model predictions.