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 Adak, M.; Yadav, Rashmi Akshay; Hiremath, Shashank M; Gorkhe, Monika; Gopukumar, S.T.; Sankari, V. Title A name given to the resource Integrating Machine Learning with Financial Risk Modeling for Portfolio Management 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 International Conference on Communication, Computer and Information Technology, IC3IT 2025; Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/IC3IT66137.2025.11340922" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/IC3IT66137.2025.11340922</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105033366493?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105033366493?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 Adak M., Yeshwantrao Chavan College of Engineering, Department of Applied Mathematics, Nagpur, India; Yadav R.A., Christ University, Department: School of Business and Management-MBAk, Karnataka, Bangalore, India; Hiremath S.M., Presidency Business School, Presidency College, Department of Mba, Karnataka, Bengaluru, India; Gorkhe M., Symbiosis Skills and Professional University, Department: School of Bfsi, Pune, India; Gopukumar S.T., Saveetha Medical College and Hospital, Saveetha Institute of Medical and Technical Sciences (SIMATS), Saveetha University, Department of General Surgery, Tamil Nadu, Chennai, India; Sankari V., K.Ramakrishnan College of Engineering, Department of Ai&amp;ds, Trichy, India Description An account of the resource Financial markets may be unpredictable and volatile; the ability to perform proper risk forecasting and effectiveness in performing an efficient portfolio is of primary importance when making wise investment choices. The nonlinear trends, and time dependence applied in financial data are usually not captured in conventional predictive models. The research is suggesting a new hybrid architecture LSTXplain that combines with and is afforded capabilities of SHAP, and exogenized with LSTM networks as well as Experimental learning. The aim of this paper, which is entitled Integrating Machine Learning with Financial Risk Modeling to Portfolio Management is to combine sequential learning with interpretability in an attempt to deepen financial risk prediction and portfolio optimization. The model is intended to forecast various measurements of financial risk, such as volatility and Value-at-Risk, and is also likely to establish the causes of each of these estimates. LSTXplain uses historical stock prices, technical features and optionally, sentiment scores designed using financial news to train a robust deep learner. Model outputs are then fed through SHAP that allocates a value of importance of a feature and discover that this allows analysts to know and trust what the model does. In order to compare the framework, Yahoo Finance data was applied, and the findings were compared to the traditional models ARIMA, SVM, Random Forest, and MLP. It has a prediction accuracy of over 98 percent which does not just complement the risk forecasting but enables a portfolio management to act. The analysis is a bridge between the performance of DL and explainable AI in the financial risk prediction. Statistical significance were applied to prove that such improvements are significant, and it is established that results are significant at p&lt;0.05. 2025 IEEE. Subject The topic of the resource Explainable AI; Financial Risk Forecasting; LSTM; Portfolio Management; SHAP Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Relation A related resource ISBN: 979-833152483-8; 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