Predictive Modelling of Financial Fraud Detection Using Big Data Analytics

Abstract

Financial fraud detection has become a critical challenge for institutions due to the increasing complexity and volume of financial transactions. With the rise of big data, financial organizations now have access to vast amounts of transactional data, which can be leveraged to identify patterns of fraud. Predictive modeling using big data analytics presents an innovative approach to detect fraudulent activities in real-time, reducing the risks associated with fraud. This paper explores the application of machine learning techniques, such as classification, clustering, and anomaly detection, to predict and identify fraudulent transactions in financial systems. The study examines various algorithms, such as decision trees, neural networks, and random forests, and discusses their performance in terms of accuracy, precision, recall, and F1 score. Additionally, the paper emphasizes the importance of data preprocessing, feature engineering, and model optimization in building effective predictive models. By using big data analytics, institutions can significantly improve fraud detection, minimize financial losses, and enhance the security of their systems.