Vol. 4 No. 2 (2024): Journal of AI-Assisted Scientific Discovery
Articles

AI-Powered Financial Advisory Services

PDF
  • Dr. Minh Nguyen
Dr. Minh Nguyen
Professor of Information Technology, Hanoi University of Science and Technology, Vietnam
Cover

Published 26-10-2024

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

How to Cite

[1]
D. M. Nguyen, “AI-Powered Financial Advisory Services”, Journal of AI-Assisted Scientific Discovery, vol. 4, no. 2, pp. 115–125, Oct. 2024, Accessed: Nov. 13, 2024. [Online]. Available: https://scienceacadpress.com/index.php/jaasd/article/view/196

Abstract

Nowadays, a growing trend in an increasingly competitive environment for investment service companies is to provide personalized financial advice. The latest technological advances allow them to know each client’s characteristics in depth and offer them services tailored to their needs. The demand for personalized financial advice has experienced growth in parallel with technological advances, making this a widely treated topic. Above all, advances in artificial intelligence have changed the way investment is conceived, reinventing traditional methods and skills and bringing about a range of changes that were unthinkable a few years ago. AI-powered financial advisory services try to close the gap between the needs and expectations of investors and investment company capabilities.

The increasing heterogeneity of the population, especially in the field of wealth management, makes it impossible for a single model to work effectively for all clients. A financial advisory model based on technology can therefore be an added value and a reason to justify customers staying with one company or moving to another. Currently, personalized advice is offered through risk capacity questionnaires, models based on rules, or advanced portfolio optimization models that try to personalize the advice and avoid, as much as possible, boilerplate. Financial advisory technology, or digital investment advice, has enormous potential for fundamentally changing the way investment advice is now being conducted. Automation, reduction of margins, volumes of customers, and automatic advice seem to be the disruptive components. On the other hand, this technology can streamline the service, deepen the relationship with the client, and bring added value to the advice given through sophisticated decision-making that would not be possible with traditional processes.

PDF

Downloads

Download data is not yet available.

References

  1. Tamanampudi, Venkata Mohit. "Automating CI/CD Pipelines with Machine Learning Algorithms: Optimizing Build and Deployment Processes in DevOps Ecosystems." Distributed Learning and Broad Applications in Scientific Research 5 (2019): 810-849.
  2. Pasupuleti, Vikram, et al. "Enhancing supply chain agility and sustainability through machine learning: Optimization techniques for logistics and inventory management." Logistics 8.3 (2024): 73.
  3. Thota, Shashi, et al. "Federated Learning: Privacy-Preserving Collaborative Machine Learning." Distributed Learning and Broad Applications in Scientific Research 5 (2019): 168-190.
  4. J. Singh, “Advancements in AI-Driven Autonomous Robotics: Leveraging Deep Learning for Real-Time Decision Making and Object Recognition”, J. of Artificial Int. Research and App., vol. 3, no. 1, pp. 657–697, Apr. 2023
  5. Alluri, Venkat Rama Raju, et al. "Serverless Computing for DevOps: Practical Use Cases and Performance Analysis." Distributed Learning and Broad Applications in Scientific Research 4 (2018): 158-180.
  6. Machireddy, Jeshwanth Reddy. "Assessing the Impact of Medicare Broker Commissions on Enrollment Trends and Consumer Costs: A Data-Driven Analysis." Journal of AI in Healthcare and Medicine 2.1 (2022): 501-518.
  7. S. Chitta, S. Thota, S. Manoj Yellepeddi, A. Kumar Reddy, and A. K. P. Venkata, “Multimodal Deep Learning: Integrating Vision and Language for Real-World Applications”, Asian J. Multi. Res. Rev., vol. 1, no. 2, pp. 262–282, Nov. 2020
  8. Ahmad, Tanzeem, et al. "Hybrid Project Management: Combining Agile and Traditional Approaches." Distributed Learning and Broad Applications in Scientific Research 4 (2018): 122-145.
  9. Tamanampudi, Venkata Mohit. "CoWPE: Adaptive Context Window Adjustment in LLMs for Complex Input Queries." Journal of Artificial Intelligence General science (JAIGS) ISSN: 3006-4023 5.1 (2024): 438-450.
  10. Thota, Shashi, et al. "Few-Shot Learning in Computer Vision: Practical Applications and Techniques." Human-Computer Interaction Perspectives 3.1 (2023): 29-59.
  11. Tamanampudi, Venkata Mohit. "Leveraging Machine Learning for Dynamic Resource Allocation in DevOps: A Scalable Approach to Managing Microservices Architectures." Journal of Science & Technology 1.1 (2020): 709-748.
  12. J. Singh, “Autonomous Vehicle Swarm Robotics: Real-Time Coordination Using AI for Urban Traffic and Fleet Management”, Journal of AI-Assisted Scientific Discovery, vol. 3, no. 2, pp. 1–44, Aug. 2023
  13. S. Kumari, “Cloud Transformation for Mobile Products: Leveraging AI to Automate Infrastructure Management, Scalability, and Cost Efficiency”, J. Computational Intel. & Robotics, vol. 4, no. 1, pp. 130–151, Jan. 2024.