Domain-Aware Conversational AI for Personalized Financial Advisory and Context-Driven Information Recommendation
Keywords:
conversational AI, financial advisory, personalization, context-aware recommendation, domain ontology, fairness, governance, system architectureAbstract
The proliferation of digital financial services has created an urgent need for conversational artificial intelligence systems capable of delivering personalized, domain-aware guidance and context-sensitive information recommendations. This paper presents a comprehensive system-level analysis of the architectures, design trade-offs, and governance frameworks required to build such systems at scale. We examine the structural interplay between domain knowledge integration, user modeling, and context-driven retrieval, arguing that strictly modular architectures with layered domain ontologies offer significant advantages in maintainability, compliance, and adaptability over end-to-end black-box models. The discussion extends to personalization mechanisms that balance collaborative filtering signals with individual behavioral patterns while addressing cold-start challenges through progressive profiling. A central contribution is the analysis of context-aware recommendation pipelines that fuse financial transaction semantics, temporal dynamics, and user intent into a unified retrieval-augmented generation framework. Furthermore, we investigate the governance, fairness, and sustainability dimensions, highlighting how differential privacy, explainability mandates, and carbon-aware deployment strategies must be treated as first-class design constraints rather than afterthoughts. By synthesizing lessons from dialogue systems, recommender architectures, and regulatory technology, the paper outlines a principled roadmap for next-generation financial advisory agents that are trustworthy, robust, and equitable. The analysis underscores that sustainable deployment in this high-stakes domain requires a careful orchestration of technical infrastructure, policy alignment, and continuous oversight.
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This article is published under the Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
