AI-Based Credit Card Fraud Detection Using Machine Learning

Financial fraud has emerged as one of the most critical threats to global economic stability in the digital era. With the exponential proliferation of online banking, mobile payments, e-commerce transactions, and digital financial services, malicious actors have developed increasingly sophisticated strategies to exploit vulnerabilities in existing fraud detection mechanisms. Traditional rule-based systems, while foundational, are demonstrably inadequate in detecting novel, adaptive, and complex fraud patterns, necessitating the development of intelligent, data-driven solutions capable of evolving in tandem with emerging threats. This paper presents FinSecure AI, a comprehensive, machine learning–powered fraud detection system engineered to address the limitations of conventional approaches. The proposed system leverages three well-established supervised classification algorithms—Logistic Regression, Random Forest, and Gradient Boosting—applied to the publicly available Kaggle Credit Card Fraud Detection Dataset comprising 284,807 transactions. Critical methodological challenges, including severe class imbalance (fraudulent transactions representing only 0.17% of the dataset), are addressed through the application of the Synthetic Minority Over-sampling Technique (SMOTE). The system architecture encompasses an end-to-end pipeline from raw transaction ingestion and feature engineering through model training, evaluation, and deployment via a Flask-based RESTful prediction API supported by an interactive web dashboard. Experimental results demonstrate that the Random Forest classifier achieves superior predictive performance, attaining an accuracy of 99.96%, a precision of 98.7%, a recall of 96.2%, an F1-score of 97.4%, and a ROC-AUC score of 0.9985. These results underscore the capacity of ensemble machine learning methods to deliver robust, scalable, and operationally viable fraud detection in real-world financial environments. The FinSecure AI framework represents a significant advancement in applied artificial intelligence for financial security.