Generative AI-driven robo-advisors: Technological Evolution, Risk Mechanisms and Collaborative Governance Pathways

Shichao Zhang

doi:10.47297/taposatWSP2633-456928.20260704

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Generative AI-driven robo-advisors: Technological Evolution, Risk Mechanisms and Collaborative Governance Pathways

Volume 7, Issue 4

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Generative AI-driven robo-advisors: Technological Evolution, Risk Mechanisms and Collaborative Governance Pathways
Generative AI-driven robo-advisors: Technological Evolution, Risk Mechanisms and Collaborative Governance Pathways
School of Economics, Hefei University of Technology, Hefei, Anhui, 230601, China
Vol. 7, Issue 4, Pages: 159-167(2026)
Published：30 April 2026
DOI：10.47297/taposatWSP2633-456928.20260704
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Zhang Shichao.Generative AI-driven robo-advisors: Technological Evolution, Risk Mechanisms and Collaborative Governance Pathways[J].Theory and Practice of Science and Technology,2026,07(04):159-167.
DOI：

Zhang Shichao.Generative AI-driven robo-advisors: Technological Evolution, Risk Mechanisms and Collaborative Governance Pathways[J].Theory and Practice of Science and Technology,2026,07(04):159-167. DOI： 10.47297/taposatWSP2633-456928.20260704.

摘要

Abstract

Artificial intelligence models have transformed the delivery of robo-advisory services

shifting the industry's operational paradigm from automated computer programmes to AI-supported technical services. These models can exchange and organise information

enabling timely adjustments to recommendations. This resolves previous issues of poor communication and difficulty in explanation

thereby facilitating personal financial management. However

the application of AI in high-risk sectors such as finance gives rise to issues including misinformation

opaque decision-making processes

unclear allocation of responsibility

and data breaches

thereby increasing the complexity of financial risk management. This article describes the transition of robo-advisory services from single-programme control to intelligent services

outlines risk models across technological

user

institutional and regulatory dimensions

and analyses issues of information bias and regulatory failure caused by technical flaws. It proposes a governance approach combining technological safeguards and regulatory frameworks

establishing a management system based on three pillars: technical protection

regulatory constraints and inter-departmental collaboration. This research addresses current methodological gaps in the study of AI applications within the financial sector

providing a reference for institutional management

policy formulation by regulatory authorities and industry development.

关键词

Keywords

references

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