AI-Driven Compliance Automation in Banking: A Hybrid Model Integrating Natural Language Processing and Knowledge Graphs

Abstract

Maintaining regulatory compliance while detecting ever more complex fraud patterns via conventional rules-based systems presents unmatched difficulties for the financial services sector. The incorporation of understandable artificial intelligence approaches with hybrid architectures integrating knowledge graphs and natural language processing to automate compliance and fraud detection in banking is discussed in this article. Machine learning models show superior performance to conventional detection methods, but their black-box character goes against transparency and explainability regulations. Using transformer-based language models and heterogeneous graph neural networks, the hybrid design extracts semantic patterns from textual transaction data while encoding domain knowledge via structured knowledge representations. Using SHAP and attentional mechanisms, human-interpretable explanations that satisfy legislative obligations can be created while keeping identification accuracy. Regulatory compliance frameworks, including the GDPR and Basel Committee guidelines, provide openness requirements, yet execution issues with regard to clarity, specificity, adversarial robustness, and computational overhead persist. Deploying reliable artificial intelligence systems for financial compliance calls for balancing the conflicting needs of stakeholder trust, traceability performance, and operational efficiency by means of well-thought-out governance systems and multi-modal explainability strategies.