Rethinking Risk Management in Robo-Advisory: Insights from Behavioural Finance and Algorithmic Transparency

Abstract

With the rapid development of financial technology, robo-advisors have become a pivotal tool in wealth management. However, the risk management mechanisms face dual challenges in practice: first, investor Behavioural biases lead to distorted model inputs; second, algorithmic opacity undermines user trust and regulatory transparency. To adress these two challenges, this article takes Behavioural finance and algorithmic transparency as entry points, systematically reviewing research progress in these areas and identifying key gaps in theoretical integration and practical application. Based on this, it proposes a dual-layer risk management framework of ‘Behavioural awareness—transparent feedback’, emphasising the crucial synergy between dynamic investor behaviour recognition and algorithmic decision-making explainability. This framework not only enhances platforms' responsiveness to irrational behaviour but also optimises risk control and regulatory alignment mechanisms under algorithmic transparency. At the same time, the establishment of this framework provides a new direction for the development of robot-advisors. The article holds significant theoretical and practical value for designing optimised robo-advisor systems, constructing trust mechanisms, and formulating regulatory strategies.