Credit Card Fraud Detection using Machine learning algorithms and Artificial Neural Network

Credit card fraud detection is a crucial domain where the identification of complex patterns in transaction data is paramount. Deep learning, particularly Artificial Neural Networks (ANNs), offers advanced techniques to enhance the detection, prevention, and mitigation of fraudulent activities. However, existing methods for credit card fraud detection often fail to incorporate essential features that reflect realistic fraudulent behavior. This research focuses on the developing an ANN model and compares it with other popular machine learning models to detect credit card fraud and help users stay protected. The ANN model was optimized to handle the challenges of class imbalance and the sparse nature of fraud instances, ensuring robustness and reliability. When compared to other models in the domain of credit card fraud detection, our ANN model demonstrates a clear advantage in terms of accuracy and robustness with an accuracy of 99.99%.