Cross-Border Fraud Detection, Compliance, and Operational Efficiency Systems for Multinational Businesses
DOI:
https://doi.org/10.63084/econova.v1i2.95Keywords:
Cross-border fraud detection, compliance systems, multinational corporations, artificial intelligence, blockchain, operational efficiency, anti-money laundering, regulatory technologyAbstract
The proliferation of digital commerce and globalized business operations has intensified the complexity of fraud detection, regulatory compliance, and operational efficiency for multinational corporations. This paper systematically examines contemporary systems for cross-border fraud detection, compliance management, and operational optimization in multinational business environments. This study identifies key technological approaches, including artificial intelligence, machine learning, blockchain, and robotic process automation, and evaluates their effectiveness in detecting fraudulent activities, ensuring regulatory compliance, and enhancing operational efficiency across jurisdictions. The findings reveal that integrated AI-driven frameworks demonstrate superior performance in detecting trade-based money laundering, payment fraud, and compliance violations, while blockchain-based systems offer enhanced transparency and auditability for cross-border transactions. However, significant challenges persist, including data residency regulations, jurisdictional conflicts, interoperability limitations, and the absence of standardized international compliance frameworks. The analysis further identifies critical gaps in real-time detection capabilities, explainability of AI models, and scalability for small and medium-sized enterprises. This paper contributes to the academic discourse by synthesizing fragmented research streams, providing a structured taxonomy of fraud detection and compliance systems, and offering evidence-based recommendations for practitioners and policymakers. The study concludes that successful implementation of cross-border fraud detection and compliance systems requires a multi-layered approach combining advanced analytics, regulatory harmonization, and organizational commitment to ethical data governance.
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