Predictive Analytics in Wealth Management and the Role of Machine Learning for Investment Professionals
subject: Data-Driven Decision Making; Financial Forecasting; Machine Learning; Predictive Analytics; Risk Assessment; Wealth Management
description: The paper focuses on the use of predictive analytics and machine learning (ML) to potentially transform the landscape of contemporary wealth management and provide financial decision-makers with cutting-edge tools that improve decision-making and portfolio management practices with clients. Conventional investment models are usually based on the past performance and unchanging risk evaluation, which is not flexible in a fluctuating market. As compared, the suggested ML-based framework would use the real-time data source dynamic, that is, market trends, behavioral financial indicators, and macroeconomic signals to capture the real-time forecast and provide individual investment advice. Multi-model ensemble comprising Random Forest, XGBoost, and LSTM networks were created to predict the asset performance and to evaluate investor risk profiles with high accuracy. Empirical analysis proved that the proposed system proved to be more accurate (95.3 %), have a higher precision (92 %), recall (94.1 %), and F1-score (92.1 %) as compared to current approaches. The strategy improves risk-adjusted returns, minimises human bias and decision lag. These results reinforced the useful application of ML in enhancing the strategic potential of investment professionals and establishing a more robust and flexible ecosystem of managing wealth. 2025 IEEE.
creator: Manoharan, Geetha; Kulshrestha, Nitin; Sugatha Kumari, B.; Sidhu, Kawerinder Singh; Sanghavi, Himanshu; Anix Joel Singh, J.
source: Proceedings - 2025 IEEE 1st International Conference on Smart Innovations in Systems, Infrastructure, Mechanical, Power, AI and Computing Technologies, SISIMPACT 2025;pp.1316-1321
publisher: Institute of Electrical and Electronics Engineers Inc.
date: 01-01-2025
rights: Restricted Access; Hardcopy may be available in the library
relation: ISBN: 979-833155787-4;
language: English
type: Conference paper
coverage: Manoharan G., School of Business, SR University, Telangana, Warangal, India; Kulshrestha N., Christ Deemed to Be University, India; Sugatha Kumari B., School of Commerce, Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology, Tamil Nadu, Chennai, India; Sidhu K.S., Uttaranchal Institute of Management, Uttaranchal University, Uttarakhand, India; Sanghavi H., C Z Patel College of Business and Management, The Charutar Vidya Mandal (CVM) University, Gujarat, Vallabh Vidyanagar, India; Anix Joel Singh J., St.Joseph University, Mechanical Engineering Department, Tanzania, India