Design of an intelligent financial surveillance system using big data analytics for enhanced fraud detection and prevention in financial institutions

The increasing complexity and volume of financial transactions have heightened the vulnerability of financial institutions to fraudulent activities. Traditional fraud detection methods are often insufficient to address the sophisticated tactics used by modern cybercriminals. This study presents the design and implementation of an intelligent financial surveillance system utilizing big data analytics to enhance fraud detection and prevention in financial institutions. By integrating advanced machine learning algorithms, natural language processing, and network analysis, the system processes vast amounts of transaction data in real-time, enabling the identification of anomalous patterns indicative of fraud. The results demonstrate that the Random Forest algorithm achieved the highest performance metrics, with a precision of 0.92, recall of 0.89, F1-score of 0.90, and AUC-ROC of 0.95. The sentiment analysis model also showed high accuracy in classifying transaction descriptions, with negative sentiments correlating strongly with fraudulent activities. Network analysis further identified significant relationships between entities involved in suspicious transactions, providing insights into potential money laundering schemes. The developed system's ability to process and analyze diverse data sources in real-time significantly enhances the detection and prevention capabilities of financial institutions. On a national and global scale, this system can help mitigate financial losses, reduce the incidence of fraud, and enhance the overall security and integrity of the financial ecosystem. These advancements support regulatory compliance and provide a robust framework for future research and development in financial fraud detection.