CFRM-LLM: A Hybrid Framework for Cross-Border Financial Risk Management Using Large Language Models with Privacy-Preserving Mechanisms

Abstract

Cross-border financial transactions face unprecedented challenges in risk management due to increasing regulatory complexity, diverse jurisdictional requirements, and evolving money laundering techniques. This paper presents CFRM-LLM, a novel hybrid framework that integrates large language models with privacy-preserving mechanisms for enhanced cross-border financial risk management. The proposed framework combines domain-specific small language models with large-scale LLMs through a multi-layer architecture, incorporating differential privacy and secure multi-party computation to protect sensitive financial data. Our approach addresses critical limitations in traditional risk control systems by providing real-time transaction analysis, automated compliance checking, and interpretable risk assessment across multiple jurisdictions. The framework employs federated learning mechanisms to enable collaborative model training without compromising data sovereignty. Experimental evaluation demonstrates significant improvements in anomaly detection accuracy, achieving 94.3% precision in identifying suspicious cross-border transactions while maintaining sub-second latency for real-time processing. The CFRM-LLM framework reduces false positive rates by 47% compared to traditional rule-based systems and provides comprehensive audit trails for regulatory compliance. Our contributions include a novel hybrid architecture for financial LLM integration, privacy-preserving protocols for cross-border data processing, and an interpretable AI module for regulatory compliance verification.