Leveraging AI for Personalized Financial Client Advisory Services: A Modular, Human-Centric Framework Based on Markowitz Portfolio Theory

Kaushik Khare; Manisha Dhage; Eshan Deosatwar; Yash Bankar; Sahil Rodge

doi:10.2991/978-94-6239-797-2_34

<Previous Article In Volume

Next Article In Volume>

Leveraging AI for Personalized Financial Client Advisory Services: A Modular, Human-Centric Framework Based on Markowitz Portfolio Theory

Authors

Kaushik Khare¹^{, *}, Manisha Dhage², Eshan Deosatwar³, Yash Bankar⁴, Sahil Rodge⁵

¹Marathwada Mitra Mandal’s College of Engineering, Karvenagar, Pune, Maharashtra, India, 41105

²Head of AI & Data Science Department, Marathwada Mitra Mandal’s College of Engineering, Karvenagar, Pune, Maharashtra, India, 41105

³Marathwada Mitra Mandal’s College of Engineering, Karvenagar, Pune, Maharashtra, India, 41105

⁴Marathwada Mitra Mandal’s College of Engineering, Karvenagar, Pune, Maharashtra, India, 41105

⁵Marathwada Mitra Mandal’s College of Engineering, Karvenagar, Pune, Maharashtra, India, 41105

^*Corresponding author. Email: kaushikkhare26@gmail.com

Corresponding Author

Kaushik Khare

Available Online 14 October 2026.

DOI: 10.2991/978-94-6239-797-2_34 How to use a DOI?
Keywords: Artificial Intelligence; Financial Advisory System; Portfolio Optimization; Risk Management; Asset Allocation
Abstract: This research presents an AI-powered financial advisory system that delivers personalized portfolio recommendations from individual investor risk profiles. The system comprises three interconnected modules. The first identifies top-performing Nifty50 stocks through financial modelling; the second allocates capital across five asset classes; stocks, mutual funds, gold, cryptocurrency, and fixed deposits; guided by risk tolerance captured in a registration questionnaire; and the third manages risk by simulating market scenarios and applying trend-based strategies to limit losses. Outputs are presented in an interactive dashboard showing asset allocation, expected performance, and risk measures. The work demonstrates how combining AI with established financial principles yields personalized, accessible investment tools for novice and experienced investors alike.
Open Access: Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Download article (PDF)

<Previous Article In Volume

Next Article In Volume>

Volume Title: Proceedings of the 2nd International Conference on Lifespan Innovation (ICLI 2026) national Conference on Lifespan Innovation
Series: Advances in Social Science, Education and Humanities Research
Publication Date: 14 October 2026
ISBN: 978-94-6239-797-2
ISSN: 2352-5398
DOI: 10.2991/978-94-6239-797-2_34 How to use a DOI?
Open Access: Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

ris enw bib

TY  - CONF
AU  - Kaushik Khare
AU  - Manisha Dhage
AU  - Eshan Deosatwar
AU  - Yash Bankar
AU  - Sahil Rodge
PY  - 2026
DA  - 2026/10/14
TI  - Leveraging AI for Personalized Financial Client Advisory Services: A Modular, Human-Centric Framework Based on Markowitz Portfolio Theory
BT  - Proceedings of the 2nd International Conference on Lifespan Innovation (ICLI 2026) national Conference on Lifespan Innovation
PB  - Atlantis Press
SP  - 345
EP  - 355
SN  - 2352-5398
UR  - https://doi.org/10.2991/978-94-6239-797-2_34
DO  - 10.2991/978-94-6239-797-2_34
ID  - Khare2026
ER  -

download .riscopy to clipboard