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

TL;DR

This paper introduces a hybrid system combining machine learning and retrieval-augmented large language models to enhance personalized marketing in financial services, ensuring compliance, transparency, and improved accuracy.

Contribution

It presents a novel architecture integrating predictive customer modeling with RAG-based content generation for transparent and compliant financial marketing.

Findings

Temporal modeling improves personalization accuracy

Intent features enhance customer segmentation

Citation-based retrieval reduces unsupported content

Abstract

Personalized marketing in financial services requires models that can both predict customer behavior and generate compliant, context-appropriate content. This paper presents a hybrid architecture that integrates classical machine learning for segmentation, latent intent modeling, and personalization prediction with retrieval-augmented large language models for grounded content generation. A synthetic, reproducible dataset is constructed to reflect temporal customer behavior, product interactions, and marketing responses. The proposed framework incorporates temporal encoders, latent representations, and multi-task classification to estimate segment membership, customer intent, and product-channel recommendations. A retrieval-augmented generation layer then produces customer-facing messages constrained by retrieved domain documents. Experiments show that temporal modeling and intent features improve personalization accuracy, while citation-based retrieval reduces unsupported generation and supports auditability in regulated settings. The contribution is primarily architectural, demonstrating how predictive modeling and RAG-based generation can be combined into a transparent, explainable pipeline for financial services personalization.