A Review of Machine Learning Techniques for Financial Stress Testing: Emerging Trends, Tools, and Challenges
DOI:
https://doi.org/10.63282/3050-9262.IJAIDSML-V4I1P105Keywords:
Stress Testing, Scikit-learn, TensorFlow, Keras, SHAP, LIME, Apache Spark, Google Cloud PlatformAbstract
As the global economy becomes more uncertain and systemic vulnerabilities increase, financial institutions are growingly becoming resilient through the use of intelligent systems to assess resilience. Although it forms a basis of stress testing, traditional methods might not be sufficient to reflect the realities of contemporary financial settings. In this paper, a critical review of how the area of financial stress testing is changing towards machine learning (ML) technologies, such as supervised learning, unsupervised learning, reinforcement learning, and hybrid frameworks, is provided. In order to maximize model transparency and regulatory compliance, it goes into depth on how to include Explainable AI (XAI) approaches. Also, the paper provides the technological framework, including TensorFlow, Keras, and Apache Spark, which makes it possible to implement it on a large scale and in real-time. Important issues like data integrity, interpretability and model governance are also tackled. The paper also outlines the research gaps for the future and suggests a framework on how ML-based stress testing can be aligned with the global regulatory expectations. This review helps to orientate oneself in the future outlook of ML-based stress testing by mapping the major regulatory frameworks, practical challenges, and emerging tools through which ML-based stress testing can transform financial risk management and system-wide stability
References
[1] C. S. Hakkio, W. R. Keeton, and others, “Financial stress: What is it, how can it be measured, and why does it matter,” Econ. Rev., vol. 94, no. 2, pp. 5–50, 2009.
[2] W.-Y. Lin, Y.-H. Hu, and C.-F. Tsai, “Machine learning in financial crisis prediction: a survey,” IEEE Trans. Syst. Man, Cybern. Part C (Applications Rev., vol. 42, no. 4, pp. 421–436, 2011.
[3] V. Kolluri, “A Comprehensive Analysis On Explainable And Ethical Machine: Demystifying Advances In Artificial Intelligence,” TIJER - Int. Res. Journals, vol. 2, no. 7, 2015.
[4] M. Langemeier, “Stress Testing Financial Performance,” farmdoc Dly., vol. 6, 2016.
[5] M. Sorge, “Stress-testing Financial Systems: An Overview of Current Methodologies,” SSRN Electron. J., no. 165, 2011, doi: 10.2139/ssrn.759585.
[6] M. Quagliariello, Stress-Testing the Banking System. Cambridge University Press. Cambridge University Press, 2009. doi: 10.1017/CBO9780511635618.
[7] J. Henry and C. Kok, “A macro stress testing framework for assessing systemic risks in the banking sector,” 2013.
[8] M. S. Martinez Peria, G. Majnoni, M. T. Jones, and W. Blaschke, “Stress Testing of Financial Systems: An Overview of Issues, Methodologies, and FSAP Experiences,” IMF Work. Pap., vol. 01, no. 88, p. 1, 2001, doi: 10.5089/9781451851168.001.
[9] S. Mittnik and W. Semmler, “The real consequences of financial stress,” J. Econ. Dyn. Control, vol. 37, no. 8, pp. 1479–1499, 2013.
[10] M. Sorge and K. Virolainen, “A comparative analysis of macro stress-testing methodologies with application to Finland,” J. Financ. Stab., vol. 2, no. 2, pp. 113–151, 2006.
[11] R. Cardarelli, S. Elekdag, and S. Lall, “Financial stress and economic contractions,” J. Financ. Stab., vol. 7, no. 2, pp. 78–97, 2011.
[12] S. Garg, “Predictive Analytics and Auto Remediation using Artificial Inteligence and Machine learning in Cloud Computing Operations,” Int. J. Innov. Res. Eng. Multidiscip. Phys. Sci., vol. 7, no. 2, 2019.
[13] M. Leo, S. Sharma, and K. Maddulety, “Machine Learning in Banking Risk Management: A Literature Review,” Risks, vol. 7, no. 1, p. 29, Mar. 2019, doi: 10.3390/risks7010029.
[14] P. Gogas, T. Papadimitriou, and A. Agrapetidou, “Forecasting bank failures and stress testing: A machine learning approach,” Int. J. Forecast., vol. 34, no. 3, pp. 440–455, Jul. 2018, doi: 10.1016/j.ijforecast.2018.01.009.
[15] K. Hubrich and R. J. Tetlow, “Financial stress and economic dynamics: The transmission of crises,” J. Monet. Econ., vol. 70, Mar. 2015, doi: 10.1016/j.jmoneco.2014.09.005.
[16] A. J. Porcelli and M. R. Delgado, “Acute stress modulates risk taking in financial decision making,” Psychol. Sci., vol. 20, no. 3, pp. 278–283, 2009.
[17] D. Liang, C.-F. Tsai, A.-J. Dai, and W. Eberle, “A novel classifier ensemble approach for financial distress prediction,” Knowl. Inf. Syst., vol. 54, no. 2, Feb. 2018, doi: 10.1007/s10115-017-1061-1.
[18] S. Khademolqorani, A. Zeinal Hamadani, and F. Mokhatab Rafiei, “A hybrid analysis approach to improve financial distress forecasting: Empirical evidence from Iran,” Math. Probl. Eng., vol. 2015, no. 1, p. 178197, 2015.
[19] V. Kolluri, “An In-Depth Exploration of Unveiling Vulnerabilities: Exploring Risks in AI Models and Algorithms,” Int. J. Res. Anal. Rev., vol. 1, no. 3, pp. 910–913, 2014.
[20] B. J. Omopariola and V. Aboaba, “Comparative analysis of financial models: Assessing efficiency, risk, and sustainability,” Int J Comput Appl Technol Res, vol. 8, no. 5, pp. 217–231, 2019.
[21] M. Lo Duca and T. A. Peltonen, “Macro-financial vulnerabilities and future financial stress-Assessing systemic risks and predicting systemic events,” 2011.
[22] V. Acharya, R. Engle, and D. Pierret, “Testing macroprudential stress tests: The risk of regulatory risk weights,” J. Monet. Econ., vol. 65, pp. 36–53, 2014.
[23] C. Stamate, G. D. Magoulas, and M. S. C. Thomas, “Transfer learning approach for financial applications,” arXiv Prepr. arXiv1509.02807, 2015.
[24] C. Borio, M. Drehmann, and K. Tsatsaronis, “Stress-testing macro stress testing: does it live up to expectations?,” J. Financ. Stab., vol. 12, pp. 3–15, 2014.
[25] R. Brummelhuis and Z. Luo, “Bank net interest margin forecasting and capital adequacy stress testing by machine learning techniques,” Available SSRN 3282408, 2019.
[26] S. Finck, “Worst case search over a set of forecasting scenarios applied to financial stress-testing,” in Proceedings of the Genetic and Evolutionary Computation Conference Companion, New York, NY, USA: ACM, Jul. 2019. doi: 10.1145/3319619.3326835.
[27] D. Gramlich, “Sustainability stress testing the financial system: Challenges and approaches,” Des. a Sustain. Financ. Syst. Dev. goals socio-ecological Responsib., pp. 173–197, 2018.
[28] M. Jacobs Jr, “The validation of machine-learning models for the stress testing of credit risk,” J. Risk Manag. Financ. Institutions, vol. 11, no. 3, 2018.
[29] G. Gao, B. Mishra, and D. Ramazzotti, “Efficient Simulation of Financial Stress Testing Scenarios with Suppes-Bayes Causal Networks,” Procedia Comput. Sci., vol. 108, pp. 272–284, 2017, doi: 10.1016/j.procs.2017.05.167.
[30] A. Hemakom, T. Chanwimalueang, A. Carrion, L. Aufegger, A. G. Constantinides, and D. P. Mandic, “Financial Stress Through Complexity Science,” IEEE J. Sel. Top. Signal Process., vol. 10, no. 6, Sep. 2016, doi: 10.1109/JSTSP.2016.2581299.
[31] Polu, A. R., Buddula, D. V. K. R., Narra, B., Gupta, A., Vattikonda, N., & Patchipulusu, H. (2021). Evolution of AI in Software Development and Cybersecurity: Unifying Automation, Innovation, and Protection in the Digital Age. Available at SSRN 5266517.
[32] Chinta, P. C. R., Katnapally, N., Ja, K., Bodepudi, V., Babu, S., & Boppana, M. S. (2022). Exploring the role of neural networks in big data-driven ERP systems for proactive cybersecurity management. Kurdish Studies.
[33] Routhu, K., Bodepudi, V., Jha, K. M., & Chinta, P. C. R. (2020). A Deep Learning Architectures for Enhancing Cyber Security Protocols in Big Data Integrated ERP Systems. Available at SSRN 5102662.
[34] Chinta, P. C. R., & Katnapally, N. (2021). Neural Network-Based Risk Assessment for Cybersecurity in Big Data-Oriented ERP Infrastructures. Neural Network-Based Risk Assessment for Cybersecurity in Big Data-Oriented ERP Infrastructures.
[35] Katnapally, N., Chinta, P. C. R., Routhu, K. K., Velaga, V., Bodepudi, V., & Karaka, L. M. (2021). Leveraging Big Data Analytics and Machine Learning Techniques for Sentiment Analysis of Amazon Product Reviews in Business Insights. American Journal of Computing and Engineering, 4(2), 35-51.
[36] Kalla, D. (2022). AI-Powered Driver Behavior Analysis and Accident Prevention Systems for Advanced Driver Assistance. International Journal of Scientific Research and Modern Technology (IJSRMT) Volume, 1.
[37] Chinta, P. C. R. (2022). Enhancing Supply Chain Efficiency and Performance Through ERP Optimisation Strategies. Journal of Artificial Intelligence & Cloud Computing, 1(4), 10-47363.
[38] Kuraku, D. S., Kalla, D., & Samaah, F. (2022). Navigating the link between internet user attitudes and cybersecurity awareness in the era of phishing challenges. International Advanced Research Journal in Science, Engineering and Technology, 9(12).
[39] Sadaram, G., Sakuru, M., Karaka, L. M., Reddy, M. S., Bodepudi, V., Boppana, S. B., & Maka, S. R. (2022). Internet of Things (IoT) Cybersecurity Enhancement through Artificial Intelligence: A Study on Intrusion Detection Systems. Universal Library of Engineering Technology, (2022).
[40] Karaka, L. M. (2021). Optimising Product Enhancements Strategic Approaches to Managing Complexity. Available at SSRN 5147875.
[41] Polu, A. R., Vattikonda, N., Buddula, D. V. K. R., Narra, B., Patchipulusu, H., & Gupta, A. (2021). Integrating AI-Based Sentiment Analysis With Social Media Data For Enhanced Marketing Insights. Available at SSRN 5266555.
[42] Jha, K. M., Bodepudi, V., Boppana, S. B., Katnapally, N., Maka, S. R., & Sakuru, M. Deep Learning-Enabled Big Data Analytics for Cybersecurity Threat Detection in ERP Ecosystems.
[43] Kalla, D., Smith, N., Samaah, F., & Polimetla, K. (2022). Enhancing Early Diagnosis: Machine Learning Applications in Diabetes Prediction. Journal of Artificial Intelligence & Cloud Computing. SRC/JAICC-205. DOI: doi. org/10.47363/JAICC/2022 (1), 191, 2-7.
[44] Kalla, D., Kuraku, D. S., & Samaah, F. (2021). Enhancing cyber security by predicting malwares using supervised machine learning models. International Journal of Computing and Artificial Intelligence, 2(2), 55-62.
[45] Katari, A., & Kalla, D. (2021). Cost Optimization in Cloud-Based Financial Data Lakes: Techniques and Case Studies. ESP Journal of Engineering & Technology Advancements (ESP-JETA), 1(1), 150-157.
[46] Kalla, D., Smith, N., Samaah, F., & Polimetla, K. (2021). Facial Emotion and Sentiment Detection Using Convolutional Neural Network. Indian Journal of Artificial Intelligence Research (INDJAIR), 1(1), 1-13.
