AI-Driven Personalization and Decision Support in Enterprise Advisor Discovery Platforms
DOI:
https://doi.org/10.15662/IJEETR.2024.0605013Keywords:
AI-driven personalization, advisor discovery platforms, enterprise decision support, regulated financial services, search and ranking systems, digital platform architecture, explainable AIAbstract
The financial services industry has witnessed significant growth in digital advisor discovery platforms, transforming how clients connect with financial professionals. Traditional directory-based search systems increasingly fail to meet customer expectations for relevance, speed, and contextual matching in advisor selection. This research examines AI-driven personalization as a decision support mechanism within enterprise advisor discovery platforms, with particular emphasis on governance, explainability, and platform architecture in regulated environments. Using a mixed-methods approach combining platform architecture analysis and user behavior data from a major financial services firm, we demonstrate how AI-assisted ranking systems improve matching outcomes while maintaining regulatory compliance and human oversight. Our findings show that properly architected personalization systems increase successful advisor-client connections by 37% compared to traditional search methods, while maintaining full explainability and audit trails. This study contributes a comprehensive framework for implementing AI-driven decision support in regulated financial contexts, emphasizing that advisor discovery represents a complex decision-support challenge rather than a simple search problem. The research provides practical guidance for enterprise architects implementing responsible AI systems in contexts requiring transparency and accountability.
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