As financial criminal methods become increasingly sophisticated, traditional anti-money laundering and fraud detection approaches face significant challenges. This study focuses on the application technologies and cha...As financial criminal methods become increasingly sophisticated, traditional anti-money laundering and fraud detection approaches face significant challenges. This study focuses on the application technologies and challenges of big data analytics in anti-money laundering and financial fraud detection. The research begins by outlining the evolutionary trends of financial crimes and highlighting the new characteristics of the big data era. Subsequently, it systematically analyzes the application of big data analytics technologies in this field, including machine learning, network analysis, and real-time stream processing. Through case studies, the research demonstrates how these technologies enhance the accuracy and efficiency of anomalous transaction detection. However, the study also identifies challenges faced by big data analytics, such as data quality issues, algorithmic bias, and privacy protection concerns. To address these challenges, the research proposes solutions from both technological and managerial perspectives, including the application of privacy-preserving technologies like federated learning. Finally, the study discusses the development prospects of Regulatory Technology (RegTech), emphasizing the importance of synergy between technological innovation and regulatory policies. This research provides guidance for financial institutions and regulatory bodies in optimizing their anti-money laundering and fraud detection strategies.展开更多
文摘As financial criminal methods become increasingly sophisticated, traditional anti-money laundering and fraud detection approaches face significant challenges. This study focuses on the application technologies and challenges of big data analytics in anti-money laundering and financial fraud detection. The research begins by outlining the evolutionary trends of financial crimes and highlighting the new characteristics of the big data era. Subsequently, it systematically analyzes the application of big data analytics technologies in this field, including machine learning, network analysis, and real-time stream processing. Through case studies, the research demonstrates how these technologies enhance the accuracy and efficiency of anomalous transaction detection. However, the study also identifies challenges faced by big data analytics, such as data quality issues, algorithmic bias, and privacy protection concerns. To address these challenges, the research proposes solutions from both technological and managerial perspectives, including the application of privacy-preserving technologies like federated learning. Finally, the study discusses the development prospects of Regulatory Technology (RegTech), emphasizing the importance of synergy between technological innovation and regulatory policies. This research provides guidance for financial institutions and regulatory bodies in optimizing their anti-money laundering and fraud detection strategies.
