This research proposal investigates the application of predictive models utilizing big data analytics for credit card fraud detection. It addresses critical research questions, including the nature of credit card fraud, the importance of its detection, and the potential of big data in fraud prevention. The proposal outlines a synthesis matrix, exploring relevant literature on data mining techniques, network-based extensions, and ensemble classifiers. It details data collection methods, including primary and secondary data sources, and considers ethical implications. The research aims to determine if predictive models using big data can effectively prevent credit card fraud by leveraging techniques like psoaan-based one-class classification and network-based extensions. The study also explores the application of various data mining techniques and their applications in the banking sector, with the ultimate goal of improving fraud detection capabilities. The proposal also addresses the importance of ethical considerations throughout the research process, ensuring that the study adheres to all relevant regulations and norms.
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