Adaptive Intelligence in Robo-Advisory: A Framework for Risk Control and Return Optimization

Abstract

The rapid integration of artificial intelligence (AI) into financial services has transformed investment management. However, existing robo-advisory systems remain limited by static optimization, constrained risk adaptability, and opaque decision-making processes. To overcome these challenges, this study introduces an Adaptive Intelligence Framework (AIF) that integrates reinforcement learning, modern portfolio theory, and explainable AI (XAI) to enable dynamic risk control and transparent return optimization. Employing a mixed-method approach that combines theoretical modeling, comparative case studies (BlackRock Aladdin and Betterment), and empirical simulations on verified global market data from 2018 to 2024, the framework demonstrated superior performance, achieving an 11.6% increase in cumulative return, a 17.3% reduction in volatility, and a high interpretability score of 0.82. These findings indicate that adaptive algorithms can simultaneously enhance stability and transparency under non-stationary market conditions. The study advances financial AI research by linking quantitative finance with algorithmic accountability and provides a practical blueprint for developing trustworthy, regulation-aligned robo-advisory systems capable of balancing efficiency, explainability, and resilience in capital markets.