Message Integrity Verification in SWIFT Flows Using AI-Assisted Rule Extraction and Python-based Constraint Engines

While ensuring the validity of SWIFT messages is vital for secure and compliant financial undertakings, legacy validation approaches based on static and manually crafted rules struggle with evolving formats as well as inter-field dependencies. This work proposes a hybrid architecture combining AI-based rule extraction with a constraint-based engine developed in Python to identify field-level and semantic inconsistencies in real-time SWIFT streams. The architecture derives structural and relational rules from a corpus of 2.5 million historical messages, translating them into executable constraints that enable dynamic, scalable SWIFT flow message validation. Evaluation results demonstrate that the AI model achieved 94% rule coverage, while the constraint engine maintained sub-30-millisecond inference times under high-load conditions, outperforming rule-based systems in terms of precision and redundancy reduction. Furthermore, the architecture revealed cross-message governance flagging using static rules, providing new perspectives on compliance that those rules could not offer. These results demonstrate the benefits of integrating machine learning-based rule extraction with real-time constraint application to improve message integrity checks in complex financial systems.