Big Data Analytics for Detecting Financial Fraud in Multinational Corporations
Keywords:
Big Data Analytics, Financial Fraud, Multinational Corporations, Corporate Governance, Socio-Technical Infrastructures, Enterprise ArchitectureAbstract
Financial fraud within multinational corporations presents an escalating challenge to global economic stability, regulatory compliance, and corporate governance. As corporate structures become more decentralized and transactions span diverse geopolitical boundaries, traditional auditing methods fail to capture complex, multi-layered fraudulent schemes. This paper provides a comprehensive, system-level investigation into the deployment of big data analytics frameworks designed to detect and prevent financial fraud in multinational environments. By examining the structural trade-offs between centralized data lakes and decentralized mesh architectures, we analyze how modern enterprise infrastructures ingest, process, and analyze heterogeneous financial streams in real time. The study delves deeply into the technical and operational challenges of data integration across incompatible legacy enterprise resource planning platforms, the preservation of privacy under conflicting regional regulations such as the General Data Protection Regulation and cross-border data transfer restrictions, and the algorithmic trade-offs between model interpretability and predictive power. Furthermore, we evaluate the socio-technical dimensions of these systems, focusing on algorithmic fairness, the mitigation of automation bias among corporate auditors, and the long-term infrastructure sustainability of high-throughput computational frameworks. Through conceptual analysis and systemic evaluations, this research establishes a robust governance model that balances regulatory compliance, technical scalability, and ethical responsibility, ultimately offering a blueprint for next-generation corporate oversight infrastructures.
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