Data Science Applications in Financial Risk Assessment

  • Authors

    • Dr. Daniel Rodríguez Department of Social Sciences, Central American University, San José, Costa Rica. Author

    DOI:

    https://doi.org/10.67228/30713498/IJADSMC-2022PII8D6N

    Published 11-04-2022

  • Financial risk assessment, data science, machine learning, credit risk, market risk, big data analytics, predictive modeling, explainable AI

    Issue

    Vol. 5 No. 2 (2022)

    Section

    Articles

    How to Cite

    [1]
    D. Rodríguez, “Data Science Applications in Financial Risk Assessment”, IJADSMC, vol. 5, no. 2, pp. 01–15, Nov. 2022, doi: 10.67228/30713498/IJADSMC-2022PII8D6N.

  • Abstract

    Financial risk assessment is essential in handling uncertainties and complexities in modern financial systems. Traditional statistical models often struggle with nonlinear relationships and dynamic market conditions. This paper explores the application of data science techniques—such as machine learning, deep learning, and big data analytics—in evaluating various financial risks, including credit, market, operational, and systemic risks. The proposed framework integrates data preprocessing, feature engineering, predictive modeling, and explainability to enhance decision-making and regulatory compliance. Experimental insights show that models like random forests, gradient boosting, SVMs, and neural networks provide better predictive accuracy and early warning capabilities compared to traditional methods. However, challenges related to data quality, interpretability, and ethical concerns remain. Overall, data science is identified as a transformative approach to financial risk assessment when supported by proper governance and validation practices.

  • References

    [1] Lessmann, S., Baesens, B., Seow, H. V., & Thomas, L. C. (2015). Benchmarking state-of-the-art classification algorithms for credit scoring: An update of research.European Journal of Operational Research, 247(1), 124–136.

    [2] Hu, L., Chen, J., Vaughan, J., Yang, H., Wang, K., Sudjianto, A., & Nair, V. N. (2020).

    Supervised Machine Learning Techniques: An Overview with Applications to Banking.arXiv:2008.04059.

    [3] Xia, Y., Liu, C., Li, Y., & Liu, N. (2017). A boosted decision tree approach using Bayesian hyper-parameter optimization for credit risk modeling. Expert Systems with Applications, 78, 225–241.

    [4] Hochreiter, S., & Schmidhuber, J. (1997). Long Short-Term Memory. Neural Computation, 9(8), 1735–1780.

    [5] Fischer, T., & Krauss, C. (2018).Deep learning with long short-term memory networks for financial market predictions.European Journal of Operational Research, 270(2), 654–669.

    [6] Niu, X., Wang, L., & Yang, X. (2019). A Comparison Study of Credit Card Fraud Detection: Supervised versus Unsupervised. arXiv:1904.10604.

    [7] Dal Pozzolo, A., Bontempi, G., Snoeck, M., & Snoeck, C. (2015). Adaptive Machine Learning for Credit Card Fraud Detection. IEEE Intelligent Systems, 30(4), 50–54.

    [8] Ribeiro, M. T., Singh, S., & Guestrin, C. (2016)."Why Should I Trust You?": Explaining the Predictions of Any Classifier. Proceedings of the 22nd ACM SIGKDD Conference.

    [9] Lundberg, S. M., & Lee, S. I. (2017). *A Unified Approach to Inter

  • Downloads