Design and development of a fintech-based algorithmic framework for detecting and preventing cross-border financial terrorism

The increasing complexity of financial transactions and the sophistication of cybercriminals have made the detection and prevention of cross-border financial terrorism a critical challenge. This study presents the design and development of a fintech-based algorithmic framework leveraging big data analytics, machine learning, blockchain technology, and natural language processing to enhance fraud detection and prevention in financial institutions. We applied various supervised and unsupervised learning algorithms to extensive transaction datasets, achieving high accuracy in detecting fraudulent activities. Notably, the XGBoost model demonstrated superior performance with a precision of 0.94, recall of 0.92, and an AUC-ROC of 0.96. The Random Forest algorithm also showed strong results, with a precision of 0.93 and recall of 0.91. Unsupervised learning methods, such as K-means clustering, effectively identified new fraud patterns, achieving a precision of 0.96 in anomaly detection. The integration of blockchain technology ensured transaction security and transparency, with zero tampered transactions recorded. Natural language processing techniques, including sentiment analysis and entity recognition, successfully detected linguistic cues indicative of fraud, with negative sentiments correlating strongly with fraudulent activities. Real-time analytics capabilities were validated with high accuracy and low latency, enabling timely detection and response to fraudulent transactions. Geographic distribution analysis identified high-risk regions, providing insights for targeted fraud prevention strategies. These advancements significantly improve the capabilities of U.S. financial institutions to combat financial terrorism, ensuring greater financial stability and compliance with regulatory requirements.