Hybrid Intent-Aware Personalization with Machine Learning and RAG-Enabled Large Language Models for Financial Services Marketing

Researchers developed a hybrid AI architecture combining machine learning and retrieval-augmented generation (RAG) for personalized financial services marketing. The system uses temporal modeling and intent prediction to create compliant, auditable customer communications while improving personalization accuracy.

• →A new hybrid architecture integrates classical ML with RAG-enabled large language models for financial marketing personalization.
• →The system uses temporal encoders and latent intent modeling to predict customer behavior and segment membership.
• →Citation-based retrieval reduces unsupported AI generation and supports regulatory compliance requirements.
• →Experiments demonstrate improved personalization accuracy through temporal modeling and intent features.
• →The architecture provides transparency and explainability needed for regulated financial services environments.