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The Role of AI and Big Data in Enhancing Financial Risk Assessment Models

Book Title: Multidisciplinary Research Nexus: Ideas for the Modern World

Chief Editors:  Mr. Irshadullah Asim Mohammed, Dr. Yogesh Mohan Gosavi, and Prof. (Dr.) Vineeta Kaur Saluja

Associate Editor: Mrs. Sruthi S

Co-Editors: Dr. S. Rajeswari, Dr. Nikhil Saini, and Ms. Atreyee Banerjee

ISBN:  978-81-985805-1-1

Chapter: 4

DOI: https://doi.org/10.59646/mrnc4/321

Author: Arun Adwani

Abstract

The integration of Artificial Intelligence (AI) and Big Data in financial risk assessment has revolutionized traditional risk modeling approaches by enhancing accuracy, efficiency, and predictive capabilities. AI-driven techniques, such as machine learning and natural language processing (NLP), enable financial institutions to analyze vast amounts of structured and unstructured data, improving the identification of emerging risks and anomalies. Meanwhile, Big Data technologies facilitate real-time risk monitoring, allowing financial organizations to make data-driven decisions and mitigate potential financial crises proactively. Additionally, AI-powered automation reduces human biases in risk evaluations, ensuring more objective and consistent assessments. The fusion of these technologies enhances credit risk analysis, fraud detection, and market risk forecasting, contributing to a more resilient financial ecosystem. However, challenges such as data privacy concerns, regulatory compliance, and model interpretability must be addressed to maximize the benefits of AI and Big Data in financial risk assessment. Future advancements in explainable AI and ethical AI frameworks are crucial for fostering trust and ensuring responsible AI deployment in the financial sector.

References

  1. Alexander, C. (2008). Market Risk Analysis, Volume I: Quantitative Methods in Finance. John Wiley & Sons.
  2. Anderson, J. (2023). The Impact of AI on Credit Scoring Models. Journal of Financial Studies, 48(3), 175-190.
  3. Bishop, C. M. (2006). Pattern Recognition and Machine Learning. Springer.
  4. Brown, T. & Green, M. (2022). Machine Learning in Credit Risk Assessment. Financial Analytics Review, 30(2), 89-104.
  5. Brynjolfsson, E., & McAfee, A. (2014). The Second Machine Age: Work, Progress, and Prosperity in a Time of Brilliant Technologies. W.W. Norton & Company.
  6. Carter, J. (2023). AI in Fraud Prevention Strategies. Cybersecurity & Finance Journal, 16(3), 205-220.
  7. Chen, M., Mao, S., & Liu, Y. (2012). Big Data: A Survey. Mobile Networks and Applications, 19(2), 171-209.
  8. Davis, H. (2022). Ethical Challenges in AI-Based Risk Assessment. Finance & Ethics Quarterly, 17(2), 95-110.
  9. Doshi-Velez, F., & Kim, B. (2017). Towards a Rigorous Science of Interpretable Machine Learning. arXiv preprint arXiv:1702.08608.
  10. Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press.
  11. Gupta, J., & Tham, T. (2019). Artificial Intelligence in Finance: The Road Ahead. Springer.
  12. Hastie, T., Tibshirani, R., & Friedman, J. (2009). The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Springer.
  13. Hull, J. C. (2018). Options, Futures, and Other Derivatives (10th ed.). Pearson.
  14. Jobst, A. A., & Gray, D. F. (2013). Systemic Contingent Claims Analysis: Estimating Market-Implied Systemic Risk. International Monetary Fund.
  15. Johnson, P. (2021). AI-Driven Market Risk Models. Global Finance Review, 35(1), 67-82.