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Paper Title
Artificial Intelligence in Wealth Management: Applications, Empirical Evidence, and India Implementation Framework
Authors
Praveen Kumar Parambath
Keywords
Artificial Intelligence, Wealth Management, Robo-Advisors, Portfolio Optimization, SEBI Compliance, Fintech India, Agentic AI, Hybrid Advisory Models
Abstract
Artificial intelligence has transformed wealth management through robo-advisory platforms, portfolio optimization, risk management, and client behavior analytics[1][2]. Analysis of 2025 industry data reveals that AI-powered portfolio management outperformed traditional methods by 14%, operational costs decreased by 40%, and fraud detection accuracy improved to 97%[2]. Hybrid advisory models combining AI capabilities with human expertise achieved 32% higher client retention and 22% satisfaction gains[2]. Indian investment firms including ICICI Direct (iWISE platform), Kotak Securities (Neo AI trading platform), HDFC Securities (StockCase algorithmic baskets), and AXIS Securities initiatives have been documented through public sources and organizational websites[14][15][16][17]. Emerging 2026 trends include agentic AI adoption (71% of institutions planning 25-600% investment increases), tokenized wealth management, hyper-personalization platforms, and unified client intelligence architectures[23]. However, significant challenges persist: algorithmic bias, SEBI regulatory compliance, data security, and preserving human judgment for complex wealth decisions. This study synthesizes global benchmarks from 2025-2026, information sourced from organizational websites and public domain sources on Indian brokerages, and regulatory frameworks to provide evidence-based guidance for wealth management professionals, technologists, and regulators. Methodology combines systematic literature review (78 peer-reviewed sources), industry benchmarking analysis, and information sourced from public sources and organizational publications[1][2][22]. Findings validate AI's transformative potential while emphasizing strategic human-AI collaboration as the optimal path forward, where computational efficiency complements irreplaceable human judgment in personalized wealth guidance.
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IJRTI — journal style
"Artificial Intelligence in Wealth Management: Applications, Empirical Evidence, and India Implementation Framework", IJRTI - International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2456-3315, Vol.11, Issue 1, page no.a213-a225, January-2026, Available :https://ijrti.org/papers/IJRTI2601029.pdf
APA — 7th edition
Parambath, P. K. (2026). Artificial Intelligence in Wealth Management: Applications, Empirical Evidence, and India Implementation Framework. International Journal for Research Trends and Innovation, 11(1), a213-a225. https://ijrti.org/viewpaperforall.php?paper=IJRTI2601029
MLA — 9th edition
Parambath, Praveen Kumar. "Artificial Intelligence in Wealth Management: Applications, Empirical Evidence, and India Implementation Framework." International Journal for Research Trends and Innovation, vol. 11, no. 1, 2026, pp. a213-a225, https://ijrti.org/viewpaperforall.php?paper=IJRTI2601029.
Chicago — 17th, bibliography
Parambath, Praveen Kumar. "Artificial Intelligence in Wealth Management: Applications, Empirical Evidence, and India Implementation Framework." International Journal for Research Trends and Innovation 11, no. 1 (2026): a213-a225. https://ijrti.org/viewpaperforall.php?paper=IJRTI2601029.
Harvard — author–date
Parambath, P.K. (2026) 'Artificial Intelligence in Wealth Management: Applications, Empirical Evidence, and India Implementation Framework', International Journal for Research Trends and Innovation, 11(1), pp. a213-a225. Available at: https://ijrti.org/viewpaperforall.php?paper=IJRTI2601029
IEEE — numbered reference
P. K. Parambath, "Artificial Intelligence in Wealth Management: Applications, Empirical Evidence, and India Implementation Framework," IJRTI, vol. 11, no. 1, pp. a213-a225, Jan. 2026.
Vancouver — biomedical
Parambath PK. Artificial Intelligence in Wealth Management: Applications, Empirical Evidence, and India Implementation Framework. IJRTI. 2026 Jan;11(1):a213-a225.
AMA — 11th edition
Parambath PK. Artificial Intelligence in Wealth Management: Applications, Empirical Evidence, and India Implementation Framework. IJRTI. 2026;11(1):a213-a225. Accessed at https://ijrti.org/viewpaperforall.php?paper=IJRTI2601029
BibTeX — Zotero, Mendeley, LaTeX
@article{IJRTI2601029,
author = {Praveen Kumar Parambath},
title = {Artificial Intelligence in Wealth Management: Applications, Empirical Evidence, and India Implementation Framework},
journal = {International Journal for Research Trends and Innovation},
volume = {11},
number = {1},
pages = {a213-a225},
year = {2026},
month = {January},
issn = {2456-3315},
url = {https://ijrti.org/viewpaperforall.php?paper=IJRTI2601029}
}
RIS — EndNote, RefWorks
TY - JOUR
AU - Parambath, Praveen Kumar
TI - Artificial Intelligence in Wealth Management: Applications, Empirical Evidence, and India Implementation Framework
T2 - International Journal for Research Trends and Innovation
JA - IJRTI
VL - 11
IS - 1
PY - 2026
SN - 2456-3315
UR - https://ijrti.org/viewpaperforall.php?paper=IJRTI2601029
SP - a213-a225
ER -
Issue
Volume 11 Issue 1,
January-2026
Pages : a213-a225
Other Publication Details
Paper Reg. ID
IJRTI_209107
Published Paper ID
IJRTI2601029
Downloads
205,637
Research Area
Management
Country
Thiruvananthapuram, Kerala, India
Published
January 2026
Published Paper PDF
https://ijrti.org/papers/IJRTI2601029.pdf
Published Paper URL
https://ijrti.org/viewpaperforall.php?paper=IJRTI2601029
About Publisher
International Journal for Research Trends and Innovation
Published by IJRTI (JW Publication)
2456-3315
ISSN
10.57
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2016
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Open
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Declarations
Funding
No external funding was received for this study.
Conflict of Interest
The authors declare that they have no conflict of interest.
Acknowledgements
The authors would like to thank the reviewers and the editorial board of International Journal for Research Trends and Innovation for their careful reading and constructive comments, and all colleagues who supported the preparation of this manuscript.
