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Using AI to Navigate Cross-Border Regulatory Complexities in FinTech

Author(s) Prashant Singh
Country United States
Abstract Globalization of financial technology services has also ratcheted up the need for agile, smart compliance solutions suitable for the complex of regulatory environments that cover cross-border transactions. Since FinTech businesses are crossing jurisdictional boundaries, they encounter a wide range of complicated regulations that differ in extent, interpretation and enforcement. These diverse variations are difficult for classic compliance tools to manage, and can lead to inefficiency, slower time-to-market, and increased legal exposure. In turn, artificial intelligence (AI) has become a critical catalyst for interpreting, standardizing, and automating regulatory workflows across global financial regimes.
This paper aims to conduct an in-depth study on the potential usage of AI-inspired approaches to address the fragmented regulatory landscape of cross-border FinTech. Building on these, by using NLP AI can extract and contextualize obligations from different regulatory texts and ML models can find patterns in enforcement actions and predict how much compliance risk exists. It also demonstrates how such solutions can help banks and other corporations interpret the rules more effectively, automate reporting processes and keep a current compliance posture in various legal jurisdictions.
A tiered architecture is suggested in which AI systems connect to regulatory databases, legal ontologies and internal compliance solutions to provide recent insights like on-the-fly mapping of jurisdiction-specific rules.” The paper shows how AI systems example, such as Anomodetect, help deliver standard compliance across regions with variable standards around anti-money laundering (AML), data at rest compliance, transaction authorization and customer KYC. Empirical evaluation of a prototype regulatory intelligence system demonstrates reduced manual overhead and error rates, and faster response times to regulatory changes.
Finally, this paper highlights the importance of transparency, explainability, and human-in-the-loop audits in AI-powered compliance models. It discusses how organizations can embed AI into their current governance systems, while maintaining accountability and ethical direction. Risk factors like model drift, data bias, and misalignment with the regulatory context are also covered, with proposed mitigation approaches. The study emphasises that while AI adds flexibility and agility to scale and respond, its implementation should be governed by stringent evidence-based vetting and cross-sector collaboration between those in compliance, legal and data science.
By positioning AI as an enhancer, rather than a usurper of human decision making in a compliance context, this article sets out a strategic guide for FinTechs to confidently conduct business across boundaries. They are instead described as concerns here that could well serve as actionable guidance on designing robust, interoperable, agile AI-driven RegTech platforms that can meet changing regulatory standards. With the continued redefinition of global finance by FinTech, AI emerges as a vital guarantor of compliance continuity, operational nimbleness, and regulatory confidence.
Field Engineering
Published In Volume 5, Issue 1, January 2024
Published On 2024-01-05
Cite This Using AI to Navigate Cross-Border Regulatory Complexities in FinTech - Prashant Singh - IJLRP Volume 5, Issue 1, January 2024. DOI 10.70528/IJLRP.v5.i1.1596
DOI https://doi.org/10.70528/IJLRP.v5.i1.1596
Short DOI https://doi.org/g9q4d8
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