Big Data Financial Fraud Detection with Machine Learning

This paper presents a financial fraud detection framework grounded in big data technology and cutting-edge machine learning algorithms. The framework uses a distributed computing platform to process massive financial transaction data, and extracts key indicators that can reflect abnormal behavior through feature engineering. Grounded on this premise, we integrate a comprehensive array of machine learning models, which encompass, yet are not limited to, Random Forests, Support Vector Machines, and Deep Neural Networks, to devise efficient fraud detection models. The outcomes of our experiments demonstrate that this proposed framework is not only capable of substantially enhancing the fraud recognition rate while maintaining a low false positive rate, but also exhibits robust scalability, enabling it to adapt to the continually evolving data volumes and fraud tactics.