AI-Powered Cross-Border Financial Data Integration

International financial institutions face different challenges in cross-border integration of financial data owing to diverse legal frameworks, inconsistent data formatting, and security concerns. The goal of this paper is to try to find the role of AI in mitigating such problems by correctly enabling compliance with the law and the efficient integration of cross-border financial data. We discuss how things were initially done and their associated problems, and how they are done now. This research proposes an architecture based on AI and the rule of law, with techniques for natural language processing, machine learning, and deep learning, in order to integrate data from multiple sources and real-time data validation. Real-life case studies depict both the beneficial and not-so-beneficial sides of AI in the concerned domain. Finally, we discuss some potential avenues for future research that address newer AI systems and ethical issues arising when the financial data from various countries is integrated.

Cross-Border Financial Data Data Integration Artificial Intelligence Machine Learning Natural Language Processing FinTech Regulatory Compliance Data Privacy Data Harmonization Deep Learning

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