ItemBook Chapter

Advancements in Computational Modeling for Enhancing Financial Risk Management: Applications, Challenges, and Future Directions

Title
Advancements in Computational Modeling for Enhancing Financial Risk Management: Applications, Challenges, and Future Directions
Creator
Arjun, B.S.; Ravinagarajan, Janani; Pachiyappan, Sathish; Thomas, Binoy
Description
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.
Source
Computational Modelling Approaches to FinTech Innovation;pp.191-215
Date
01-01-2025
Publisher
Bentham Science Publishers
Subject
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
Coverage
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
Rights
Restricted Access; Hardcopy may be available in the library
Relation
ISBN: 979-889881081-8; 979-889881083-2;
Format
online
Language
English
Type
Book chapter

Citation

Arjun, B.S.; Ravinagarajan, Janani; Pachiyappan, Sathish; Thomas, Binoy, “Advancements in Computational Modeling for Enhancing Financial Risk Management: Applications, Challenges, and Future Directions,” CHRIST (Deemed To Be University) Institutional Repository, accessed June 18, 2026, https://archives.christuniversity.in/items/show/24491.

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