This study addresses the critical issue of real-time credit card fraud detection using machine learning. The primary goal is to develop a model that promptly identifies fraudulent transactions and alerts users. Two algorithms—Random Forest and Decision Tree Classifier were used, alongside various sampling techniques to balance the dataset and enhance performance. Six models were created, each with different accuracy levels in fraud detection. Key findings include a higher incidence of fraud among individuals over 75 years, likely due to less familiarity with modern transaction methods. Additionally, a majority of transactions involved females, indicating a potential higher fraud risk in these transactions. The Random Forest -SMOTE [Hyperparameter Tuned] model was the most effective, achieving a 97% accuracy rate, 95% F1 score, and 98% precision rate. For practical application, this model was integrated with Twilio for real-time fraud alerts, proving successful in sending timely, accurate notifications. The study highlights valuable insights and a robust solution for real-time fraud detection and response. Regular performance evaluations of the model are recommended to maintain its effectiveness against evolving fraud patterns.
Keywords: Algorithm, credit card fraud, machine learning, real-time detection, twilo integration
