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

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.

Citation-Enforced RAG for Fiscal Document Intelligence: Cited, Explainable Knowledge Retrieval in Tax Compliance

Tax authorities and public-sector financial agencies rely on large volumes of unstructured and semi-structured fiscal documents - including tax forms, instructions, publications, and jurisdiction-specific guidance - to support compliance analysis and audit workflows. While recent advances in generative AI and retrieval-augmented generation (RAG) have shown promise for document-centric question answering, existing approaches often lack the transparency, citation fidelity, and conservative behaviour required in high-stakes regulatory domains. This paper presents a multimodal, citation-enforced RAG framework for fiscal document intelligence that prioritises explainability and auditability. The framework adopts a source-first ingestion strategy, preserves page-level provenance, enforces citations during generation, and supports abstention when evidence is insufficient. Evaluation on real IRS and state tax documents demonstrates improved citation fidelity, reduced hallucination, and analyst-usable explanations, illustrating a pathway toward trustworthy AI for tax compliance.