INTERPRETABLE TRANSFORMER MODELS FOR RELATIONSHIP ANALYSIS IN FINANCIAL DATA

The increasing complexity of financial transaction networks has necessitated the development of sophisticated analytical tools capable of uncovering intricate relationships within heterogeneous financial data while maintaining interpretability for regulatory compliance and fraud detection purposes. This paper presents a novel framework for interpretable transformer models specifically designed for relationship analysis in financial transaction networks. Our approach builds upon the foundational attention mechanisms developed for sequence-to-sequence tasks and extends them through graph attention networks to handle complex multi-entity financial relationships. The framework demonstrates how attention-based architectures can effectively analyze heterogeneous networks comprising card numbers, transaction identifiers, email domains, and card types to identify suspicious patterns and fraudulent activities. We develop specialized visualization techniques that reveal temporal dependencies in transaction sequences and cross-entity correlations in financial networks. Experimental evaluation on real-world financial transaction datasets demonstrates that our interpretable transformer models achieve superior performance in fraud detection while providing actionable insights for financial analysts. The framework successfully identifies complex fraud patterns including coordinated attacks across multiple entity types, suspicious email-card associations, and abnormal transaction behaviors, with interpretability metrics showing high alignment with expert fraud analyst assessments.