Cross Border Predictive Analytics: A Multinational Framework for Preempting Financial Crime in Real Time Ecosystems

Abstract

Considering that today is characterized by the high pace of financial globalization and digitalization, the frequent detection and prevention of financial crimes using traditional techniques have become inadequate. This paper investigates the opportunities that cross-border predictive analysis has as a disruptive technology to real-time incident analysis typified by financial crimes. It has a conceptual review style and looks at the current literature, framework, and the interrelation of technology, regulation, and international cooperation. This paper offers a multinational system by including real-time data ingestion, risk scoring with Artificial Intelligence risks, common surveillance dashboards, and privacy-preserving technologies like blockchain and federated learning. The framework focuses on the significance of strategic cooperation between banks, fintech firms, regulators, and law enforcement agencies to have a synchronized global outlook. The paper also critically assesses the pros and cons of predictive surveillance, such as legal and ethical issues regarding data privacy, algorithm bias, and nations' sovereignty. In solving these challenges, the study provides specific proposals to harmonize the law, invest in infrastructure and have transparent algorithm control. After all, the study concludes that although the potential of predictive analytics is large, the chances of its success in any cross-border ecosystem rely on a moderate balance between technology, regulatory vision, and foreign affairs. This research also adds to the emergence of discussion on modernizing the global financial crime prevention system into a more effective, transparent, and ethically accountable model.