Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Article Faculty Publications -Articles Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Philip, Jesty Mariam; Boralkar, Mohit; Joseph, Alwin Title A name given to the resource A Multi-Layer Complex Adaptive System Framework for AI-Driven Robo-Advisory Services Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2026 Source A related resource from which the described resource is derived Northeast Journal of Complex Systems;Volume;8;Issue;1;Article No.;5; Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.63562/2577-8439.1142" target="_blank" rel="noreferrer noopener">https://doi.org/10.63562/2577-8439.1142</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105035144483?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105035144483?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Philip J.M., CHRIST University, India; Boralkar M., CHRIST University, India; Joseph A., CHRIST University, India Description An account of the resource The rapid integration of Artificial Intelligence (AI) into investment advisory services has changed financial decision-making, giving rise to adaptive robo-advisory systems capable of real-time analysis, personal recommendations, and autonomous portfolio optimization. Existing research evaluates these systems primarily through technological performance or investor adoption, overlooking the complex feedback-driven interactions that emerge when AI analytics, data environments, and human behavior operate together. This study addresses this gap by conceptualizing AI-enabled robo-advisors as a multi-layered Complex Adaptive System comprising historical data, real-time data, AI analytics, investor perception, and decision-making layers. A simulation model grounded in machine learning dynamics, behavioral finance, and complexity theory is developed to capture nonlinear interactions, adaptive learning, and emergent investor responses. Results show that historical data acts as a stabilizing memory, real-time data amplifies short-term volatility, AI analytics self-organize toward performance equilibrium, and investor perception evolves through nonlinear trust thresholds that ultimately drive decision lock-in. Complexity measures reveal that adaptive intelligence is concentrated in the historical and perception layers, while the decision layer becomes increasingly deterministic as feedback loops strengthen. The findings provide a unified system-level understanding of robo-advisory ecosystems and highlight the need for governance structures that incorporate transparency, behavioral dynamics, and adaptive model monitoring. This framework offers a foundation for designing more resilient, trustworthy, and sustainable AI-driven financial advisory systems. 2026 Binghamton University Libraries. All rights reserved. Subject The topic of the resource Adaptive decision-making; Complex adaptive systems; FinTech algorithms; Investor behavior; Machine learning analytics; Nonlinear dynamics; Real-time financial data; Reinforcement learning; Robo-advisory systems; Simulation modeling Publisher An entity responsible for making the resource available Binghamton University Libraries Relation A related resource ISSN: 25778439; Language A language of the resource English Type The nature or genre of the resource Article Rights Information about rights held in and over the resource All Open Access; Gold Open Access Format The file format, physical medium, or dimensions of the resource online