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

Akhil Chandra Shanivendra

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.

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

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.
Paper Structure (44 sections, 3 equations, 5 figures, 3 tables)

This paper contains 44 sections, 3 equations, 5 figures, 3 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Customer Segmentation in Financial Services
  4. Modeling Customer Intent and Behavioral Dynamics
  5. Personalization and Predictive Targeting
  6. Large Language Models for Personalization and Marketing
  7. Hybrid ML–LLM Systems
  8. Dataset Description
  9. Methodology
  10. Problem Formulation
  11. Latent Intent and Segment Modeling
  12. Temporal Representation Learning
  13. Dual-Granularity Feature Fusion
  14. Multi-Task Learning Objective
  15. Retrieval-Augmented Generation Layer
  16. ...and 29 more sections

Figures (5)

  • Figure 1: Architecture illustrating the fusion of static attributes and behavioral time series into the TL-ADGNPT Core Neural Network, feeding into the RAG Layer for content generation.
  • Figure 2: Comparison of Normal Sequence (Control) vs. Shuffled Sequence (Experimental) to validate temporal dependency.
  • Figure 3: Macro-F1 Score comparison between TL-ADGNPT (Full), Non-Temporal Baseline, and Temporal Shuffled.
  • Figure 4: Contribution of components across personalization dimensions.
  • Figure 5: Schematic representation of the Financial RAG Pipeline, illustrating the flow from inputs to the final cited explanation.