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 Book Chapter Faculty Publications- Book Chapter 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 Arjun, B.S.; Ravinagarajan, Janani; Pachiyappan, Sathish; Thomas, Binoy Title A name given to the resource Advancements in Computational Modeling for Enhancing Financial Risk Management: Applications, Challenges, and Future Directions 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 Computational Modelling Approaches to FinTech Innovation;pp.191-215 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.2174/9798898810818125010013" target="_blank" rel="noreferrer noopener">https://doi.org/10.2174/9798898810818125010013</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105025272544?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105025272544?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 Arjun B.S., School of Business and Management, Christ University, Bangalore, India; Ravinagarajan J., Department of Commerce and Management, Shiv Nadar University, Chennai, India; Pachiyappan S., School of Business and Management, Christ University, Bangalore, India; Thomas B., Department of Business Management, Sahrdaya Institute of Management Studies, Kerala, Thrissur, India Description An account of the resource The advancement in risk management with deeper insights and more accurate predictions amidst complex data landscapes is attributed to computational modeling. It offers sophisticated tools to analyze, forecast, and mitigate risks in the dynamic financial market. This research article discusses integrating machine learning, network analysis, and other techniques to enhance risk identification, scenario analysis, and decision support in financial institutions. This article also addresses the importance of data quality, model validation, and transparency in ensuring the reliability and effectiveness of computational models. The application of machine learning techniques in credit risk assessment, market risk analysis, stress testing, scenario analysis, sensitivity analysis, portfolio management, and optimization is discussed. The study has demonstrated the conceptual model where identifying the type of risks is the first step, followed by sourcing the data internally and externally, considering the accuracyand reflection of current market conditions. Choosing the right computational techniques occupies an important stage due to the availability of both traditional and modern techniques. Traditional techniques are equally important to modern techniques, but this comes with challenges. Further risk management processes can be initiated to address the identified risks proactively and reduce potential financial losses. Finally, the study outlines future trends and technological advancements that promise to shape the future of computational modeling in financial risk management. 2025, Bentham Books imprint. Subject The topic of the resource Agent-based modelling; Anomaly detection; Computational modelling; Credit risk; Credit risk assessment; Ensemble methods; Ethical issues; Financial system; Fraud detection; Liquidity risk; Machine learning; Market risk; Monte carlo simulation; Natural language processing; Operational risk; Optimization; Risk control; Risk management; Risk management process; Stochastic modelling; Stochastic modelling and supervised learning Publisher An entity responsible for making the resource available Bentham Science Publishers Relation A related resource ISBN: 979-889881081-8; 979-889881083-2; Language A language of the resource English Type The nature or genre of the resource Book chapter 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