A Multi-Layer Complex Adaptive System Framework for AI-Driven Robo-Advisory Services
subject: 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
description: 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.
creator: Philip, Jesty Mariam; Boralkar, Mohit; Joseph, Alwin
source: Northeast Journal of Complex Systems;Volume;8;Issue;1;Article No.;5;
publisher: Binghamton University Libraries
date: 01-01-2026
rights: All Open Access; Gold Open Access
relation: ISSN: 25778439;
format: online
language: English
type: Article
coverage: Philip J.M., CHRIST University, India; Boralkar M., CHRIST University, India; Joseph A., CHRIST University, India