Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Manoharan, Geetha; Kulshrestha, Nitin; Sugatha Kumari, B.; Sidhu, Kawerinder Singh; Sanghavi, Himanshu; Anix Joel Singh, J. Title A name given to the resource Predictive Analytics in Wealth Management and the Role of Machine Learning for Investment Professionals Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived Proceedings - 2025 IEEE 1st International Conference on Smart Innovations in Systems, Infrastructure, Mechanical, Power, AI and Computing Technologies, SISIMPACT 2025;pp.1316-1321 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/SISIMPACT67725.2025.11439363" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/SISIMPACT67725.2025.11439363</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105037465455?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105037465455?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant 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&amp;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 Description An account of the resource 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. Subject The topic of the resource Data-Driven Decision Making; Financial Forecasting; Machine Learning; Predictive Analytics; Risk Assessment; Wealth Management Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Relation A related resource ISBN: 979-833155787-4; Language A language of the resource English Type The nature or genre of the resource Conference paper Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online