Research on Generative Artificial Intelligence for Virtual Financial Robo-Advisor


  • Zengyi Huang
  • Chang Che
  • Haotian Zheng
  • Chen Li



Artificial Intelligence (AI); Robo-advisers (RAs); Wealthfront; Investment Performance.


This research explores the intersection of artificial intelligence and finance, focusing on the emergence of intelligent investment advisers, commonly known as Robo-advisers (RAs). These RAs utilize robust computer models and artificial intelligence algorithms to deliver personalized asset management investment plans for users. Notably, Wealthfront is highlighted as a prominent platform in this field, offering automated investment management services aimed at optimizing investment returns. The study investigates the impact of users' past investment performance on their adoption of intelligent advisers, considering factors such as previous defaults and recent investment performance. It reveals that frequent adjustments to the use of intelligent advisers may hinder long-term investment objectives, emphasizing the importance of consistent usage to fully capitalize on their benefits. Furthermore, the research emphasizes the significance of transparency, user-friendly interaction design, and tailored financial services to foster user trust and enhance the optimization of intelligent advisers' design.


Download data is not yet available.


Gao, Longsen, et al. "Autonomous multi-robot servicing for spacecraft operation extension." 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2023.

Che, Chang, et al. "Enhancing Multimodal Understanding with CLIP-Based Image-to-Text Transformation." Proceedings of the 2023 6th International Conference on Big Data Technologies. 2023.

Zhang, Yufeng, et al. "Manipulator Control System Based on Machine Vision." International Conference on Applications and Techniques in Cyber Intelligence ATCI 2019: Applications and Techniques in Cyber Intelligence 7. Springer International Publishing, 2020.

K. Xu, X. Wang, Z. Hu and Z. Zhang, "3D Face Recognition Based on Twin Neural Network Combining Deep Map and Texture," 2019 IEEE 19th International Conference on Communication Technology (ICCT), Xi'an, China, 2019, pp. 1665-1668, doi: 10.1109/ICCT46805.2019.8947113.

Wang, G., Gong, Y., Zhu, M., Yuan, J., & Wei, K. (2023). Unveiling the future navigating next-generation ai frontiers and innovations in application. International Journal of Computer Science and Information Technology, 1(1), 147-156.

Che, Chang, et al. "Advancing Cancer Document Classification with R andom Forest." Academic Journal of Science and Technology 8.1 (2023): 278-280.

Zhu, Mengran, et al. "Utilizing GANs for Fraud Detection: Model Training with Synthetic Transaction Data." arXiv preprint arXiv:2402.09830 (2024).

Yu, Hanyi, et al. "Machine Learning-Based Vehicle Intention Trajectory Recognition and Prediction for Autonomous Driving." arXiv preprint arXiv:2402.16036 (2024).

Ji, Huan, et al. "Utilizing Machine Learning for Precise Audience Targeting in Data Science and Targeted Advertising." Academic Journal of Science and Technology 9.2 (2024): 215-220.

Che, Chang, et al. "Deep learning for precise robot position prediction in logistics." Journal of Theory and Practice of Engineering Science 3.10 (2023): 36-41.

Zhang, Y., Abdullah, S., Ullah, I., & Ghani, F. (2024). A new approach to neural network via double hierarchy linguistic information: Application in robot selection. Engineering Applications of Artificial Intelligence, 129, 107581.

Huo, Shuning, et al. "Deep Learning Approaches for Improving Question Answering Systems in Hepatocellular Carcinoma Research." arXiv preprint arXiv:2402.16038 (2024).

Xiang, Yafei, et al. "Text Understanding and Generation Using Transformer Models for Intelligent E-commerce Recommendations." arXiv preprint arXiv:2402.16035 (2024).

Wan, Weixiang, et al. "Progress in artificial intelligence applications based on the combination of self-driven sensors and deep learning." arXiv preprint arXiv:2402.09442 (2024).

W. Sun, W. Wan, L. Pan, J. Xu, and Q. Zeng, “The Integration of Large-Scale Language Models Into Intelligent Adjudication: Justification Rules and Implementation Pathways”, Journal of Industrial Engineering & Applied Science, vol. 2, no. 1, pp. 13–20, Feb. 2024.

Zeng, Q., Sun, W., Xu, J., Wan, W., & Pan, L. (2024). Machine Learning-Based Medical Imaging Detection and Diagnostic Assistance. International Journal of Computer Science and Information Technology, 2(1), 36-44.

Li, Chen, et al. "Enhancing Multi-Hop Knowledge Graph Reasoning through Reward Shaping Techniques." arXiv preprint arXiv:2403.05801 (2024).

Cai, Guoqing et al. “A deep learning-based algorithm for crop Disease identification positioning using computer vision.” International Journal of Computer Science and Information Technology (2023): n. pag.







How to Cite

Research on Generative Artificial Intelligence for Virtual Financial Robo-Advisor. (2024). Academic Journal of Science and Technology, 10(1), 74-80.

Similar Articles

1-10 of 465

You may also start an advanced similarity search for this article.