A Review of Big Data Analysis for Financial Fraud Identification Based on Machine Learning Approaches in Credit Card Systems
Keywords:
Apache Hadoop, Big data techniques, Credit card fraud, Financial fraud detection, Machine LearningAbstract
The amount of monetary transactions has expanded exponentially, and with it, the challenge of identifying credit card fraud. Traditional methods are struggling to keep pace with the evolving sophistication of fraudulent activities. This study delves into the use of big data platforms like Hadoop and Spark to enhance the detection and prevention of credit card theft through the incorporation of ML. The goal is to address this increasing concern. That demonstrate how ML algorithms can search through massive amounts of transaction data for anomalies and intricate fraud patterns that might otherwise be missed by more conventional methods using these state-of-the-art technologies. The capacity of several machine learning methods, such as supervised and unsupervised learning, to improve detection accuracy while reducing false positives is explored in this research.
Additionally, the paper examines key challenges, including data privacy concerns, scalability issues, and the need for adaptive models that can evolve with emerging fraud tactics. The results indicate that fraud detection systems can be made much more efficient and successful by using big data analytics in combination with ML. Future work in this domain could explore the development of hybrid models that combine ML with other emerging technologies, like blockchain, to provide even more robust security. Furthermore, privacy-preserving techniques and real-time fraud detection models remain important areas for future research, as it holds the potential to further strengthen the security of financial transactions and protect consumers from financial losses.
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