Fraud Detection Using Machine Learning and Community Detection Algorithm

Online fraud has indeed been a growing problem in recent years, causing significant financial losses for individuals, merchants, and banks. Machine learning has demonstrated its effectiveness in detecting and mitigating credit card fraud. This paper aims to review various online credit card techniques for fraud detection utilizing Machine Learning algorithms and evaluate them based on performance metrics such as precision, accuracy, and specificity. Additionally, the paper proposes a Fraud Detection System (FDS) that employs a supervised Random Forest algorithm to enhance fraud detection accuracy. The suggested system employs a learning-to-rank strategy to efficiently prioritize alerts and tackles the issue of concept drift in fraud detection. This ensures that the system stays efficient and current in identifying changing fraud patterns.