Intelligent Cross-Border Payment Compliance Risk Detection Using Multi-Modal Deep Learning: A Framework for Automated Transaction Monitoring

Abstract

Cross-border payment systems face escalating challenges in compliance monitoring due to increasing transaction volumes, sophisticated money laundering techniques, and evolving regulatory requirements across multiple jurisdictions. Traditional rule-based compliance systems demonstrate significant limitations through excessive false positive rates and inability to detect complex financial crime patterns that exploit emerging digital payment channels. This paper presents an intelligent multi-modal deep learning framework for automated cross-border payment compliance risk detection that integrates structured transaction data, unstructured textual information, and behavioral pattern analysis. The proposed framework employs attention-based neural architectures with multi-modal fusion techniques to process heterogeneous data streams simultaneously, enabling real-time risk assessment with enhanced accuracy and reduced false positive rates. The system incorporates advanced feature extraction mechanisms for transaction amounts, geographical patterns, entity descriptions, and temporal sequences through transformer-based encodings and graph neural network representations. Experimental evaluation demonstrates substantial performance improvements over conventional approaches, achieving 94.7% precision and 92.3% recall in compliance violation detection while reducing false positive rates by 67% compared to traditional rule-based systems. The framework maintains processing latencies below 50 milliseconds for real-time transaction evaluation and demonstrates linear scalability up to 100,000 transactions per second. Case studies reveal successful identification of sophisticated money laundering patterns involving layered transactions and coordinated timing sequences across multiple jurisdictions, validating the practical effectiveness of multi-modal integration for financial compliance applications.