Data Driven Approaches to Mitigating Systemic Financial Risks in the U.S. Banking and Investment Sectors

Md Iqbal Hossain¹*, Mitu Akter², Md. Rezaul Haque³, Proggo Choudhury⁴ 

The method of combining sentiment data with supervisory information creates an affordable system which detects systemic risks in financial institutions at earlier stages.

Abstract

Background: Systemic risk within the U.S banking and investment sectors generates ongoing problems for both regulatory bodies and financial institutions. Traditional ratio-based monitoring systems lack real-time capabilities which prevents them from detecting new risks that appear. The identification of problems at an early stage remains essential although standard measures show limited power to predict system-wide stress.

Methods: The study gathered information about present-day methods through 189 risk managers and analysts who work at banks and investment firms. The study examined predictive models which combined supervisory metrics with alternative data sources that included market sentiment information. The study used Pearson correlation coefficients (r) and chi-square (χ²) tests and paired t-tests to evaluate statistical relationships and model performance differences.

Results:  The industry relied on traditional assessment tools which included capital adequacy measures and liquidity ratios at rates of 82 % and 78 % respectively. The model achieved better results through sentiment data integration because random forest AUC moved from 0.84 to 0.88 (p = 0.001, r = 0.47) while mean time-to-detection dropped from 14.3 ± 3.1 to 9.6 ± 2.7 days (p = 0.001, r = 0.52) and high-risk detection rates increased from 62 % to 80 % (p = 0.001, r = 0.45). Main obstacles to adoption stemmed from financial barriers which affected 68 % of participants together with regulatory restrictions that impacted 54 % of participants.

Conclusion:  The combination of supervisory metrics with sentiment data creates a superior method for predicting systemic risk in U.S. financial institutions.

Keywords: Systemic risk, banking, investment, sentiment analysis, machine learning

References

Alowais, S. A., Alghamdi, S. S., Alsuhebany, N., Alqahtani, T., Alshaya, A. I., Almohareb, S. N., Aldairem, A., Alrashed, M., Saleh, K. B., Badreldin, H. A., Yami, M. S. A., Harbi, S. A., & Albekairy, A. M. (2023b). Revolutionizing healthcare: the role of artificial intelligence in clinical practice. BMC Medical Education, 23(1). https://doi.org/10.1186/s12909-023-04698-z

Amann, J., Blasimme, A., Vayena, E., Frey, D., & Madai, V. I. (2020b). Explainability for artificial intelligence in healthcare: a multidisciplinary perspective. BMC Medical Informatics and Decision Making, 20(1). https://doi.org/10.1186/s12911-020-01332-6

Amrani, Y. A., Lazaar, M., & Kadiri, K. E. E. (2018). Random Forest and Support Vector Machine based Hybrid Approach to Sentiment Analysis. Procedia Computer Science, 127, 511–520. https://doi.org/10.1016/j.procs.2018.01.150

Borges, A., & Carvalho, M. (2025). Short- and long-term financial distress prediction in SMEs: a survival model comparison. Journal of Applied Statistics, 1–30. https://doi.org/10.1080/02664763.2025.2501166

Boyer, K. K., Olson, J. R., Calantone, R. J., & Jackson, E. C. (2002). Print versus electronic surveys: a comparison of two data collection methodologies. Journal of Operations Management, 20(4), 357–373. https://doi.org/10.1016/s0272-6963(02)00004-9

Brown, C. L., Cavusgil, S. T., & Lord, A. W. (2014). Country-risk measurement and analysis: A new conceptualization and managerial tool. International Business Review, 24(2), 246–265. https://doi.org/10.1016/j.ibusrev.2014.07.012

Dong, W., Liao, S., & Zhang, Z. (2018). Leveraging financial social media data for corporate fraud detection. Journal of Management Information Systems, 35(2), 461–487. https://doi.org/10.1080/07421222.2018.1451954

Ellis, L., Haldane, A., & Moshirian, F. (2014). Systemic risk, governance and global financial stability. Journal of Banking & Finance, 45, 175–181. https://doi.org/10.1016/j.jbankfin.2014.04.012

Fang, B., & Zhang, P. (2016). Big data in finance. In Springer eBooks (pp. 391–412). https://doi.org/10.1007/978-3-319-27763-9_11

Gomber, P., Kauffman, R. J., Parker, C., & Weber, B. W. (2018). On the Fintech Revolution: Interpreting the Forces of Innovation, Disruption, and Transformation in Financial Services. Journal of Management Information Systems, 35(1), 220–265. https://doi.org/10.1080/07421222.2018.1440766

Hassija, V., Chamola, V., Mahapatra, A., Singal, A., Goel, D., Huang, K., Scardapane, S., Spinelli, I., Mahmud, M., & Hussain, A. (2023). Interpreting Black-Box Models: A review on Explainable Artificial intelligence. Cognitive Computation, 16(1), 45–74. https://doi.org/10.1007/s12559-023-10179-8

Huang, X., Zhou, H., & Zhu, H. (2009). A framework for assessing the systemic risk of major financial institutions. Journal of Banking & Finance, 33(11), 2036–2049. https://doi.org/10.1016/j.jbankfin.2009.05.017

Huang, X., Zhou, H., & Zhu, H. (2009b). A framework for assessing the systemic risk of major financial institutions. Journal of Banking & Finance, 33(11), 2036–2049. https://doi.org/10.1016/j.jbankfin.2009.05.017

Kapoor, K. K., Tamilmani, K., Rana, N. P., Patil, P., Dwivedi, Y. K., & Nerur, S. (2017). Advances in social Media Research: past, present and future. Information Systems Frontiers, 20(3), 531–558. https://doi.org/10.1007/s10796-017-9810-y

Kovac, N., Ratkovic, K., Farahani, H., & Watson, P. (2024). A practical applications guide to machine learning regression models in psychology with Python. Methods in Psychology, 11, 100156. https://doi.org/10.1016/j.metip.2024.100156

Lee, I., & Shin, Y. J. (2019). Machine learning for enterprises: Applications, algorithm selection, and challenges. Business Horizons, 63(2), 157–170. https://doi.org/10.1016/j.bushor.2019.10.005

Li, L., Goh, T., & Jin, D. (2018). How textual quality of online reviews affect classification performance: a case of deep learning sentiment analysis. Neural Computing and Applications, 32(9), 4387–4415. https://doi.org/10.1007/s00521-018-3865-7

M, V. S., & Prashar, A. (2022). State and citizen responsiveness in fighting a pandemic crisis: A systems thinking perspective. Systems Research and Behavioral Science, 40(1), 170–193. https://doi.org/10.1002/sres.2849

Nassirtoussi, A. K., Aghabozorgi, S., Wah, T. Y., & Ngo, D. C. L. (2014). Text mining for market prediction: A systematic review. Expert Systems With Applications, 41(16), 7653–7670. https://doi.org/10.1016/j.eswa.2014.06.009

Ravi, V., & Kamaruddin, S. (2017). Big data analytics enabled smart financial services: Opportunities and challenges. In Lecture notes in computer science (pp. 15–39). https://doi.org/10.1007/978-3-319-72413-3_2

Robisco, A. A., & Martínez, J. M. C. (2022). Measuring the model risk-adjusted performance of machine learning algorithms in credit default prediction. Financial Innovation, 8(1). https://doi.org/10.1186/s40854-022-00366-1

Rodriguez-Galiano, V., Sanchez-Castillo, M., Chica-Olmo, M., & Chica-Rivas, M. (2015). Machine learning predictive models for mineral prospectivity: An evaluation of neural networks, random forest, regression trees and support vector machines. Ore Geology Reviews, 71, 804–818. https://doi.org/10.1016/j.oregeorev.2015.01.001

Solinska-Nowak, A., Magnuszewski, P., Curl, M., French, A., Keating, A., Mochizuki, J., Liu, W., Mechler, R., Kulakowska, M., & Jarzabek, L. (2018). An overview of serious games for disaster risk management – Prospects and limitations for informing actions to arrest increasing risk. International Journal of Disaster Risk Reduction, 31, 1013–1029. https://doi.org/10.1016/j.ijdrr.2018.09.001

Sun, Y., Liu, L., Xu, Y., Zeng, X., Shi, Y., Hu, H., Jiang, J., & Abraham, A. (2024). Alternative data in finance and business: emerging applications and theory analysis (review). Financial Innovation, 10(1). https://doi.org/10.1186/s40854-024-00652-0

Thysen, S. M., Tawiah, C., Blencowe, H., Manu, G., Akuze, J., Haider, M. M., Alam, N., Yitayew, T. A., Baschieri, A., Biks, G. A., Dzabeng, F., Fisker, A. B., Imam, M. A., Martins, J. S. D., Natukwatsa, D., Lawn, J. E., & Gordeev, V. S. (2021). Electronic data collection in a multi-site population-based survey: EN-INDEPTH study. Population Health Metrics, 19(S1). https://doi.org/10.1186/s12963-020-00226-z

Wang, Z., Cai, Y., Liu, D., Qiu, F., Sun, F., & Zhou, Y. (2023). Intelligent classification of coal structure using multinomial logistic regression, random forest and fully connected neural network with multisource geophysical logging data. International Journal of Coal Geology, 268, 104208. https://doi.org/10.1016/j.coal.2023.104208

Weber, R. F. (2012). Structural regulation as antidote to complexity capture. American Business Law Journal, 49(3), 643–738. https://doi.org/10.1111/j.1744-1714.2012.01140.x

Wu, L., Chen, S., Guo, L., Shpyleva, S., Harris, K., Fahmi, T., Flanigan, T., Tong, W., Xu, J., & Ren, Z. (2022). Development of benchmark datasets for text mining and sentiment analysis to accelerate regulatory literature review. Regulatory Toxicology and Pharmacology, 137, 105287. https://doi.org/10.1016/j.yrtph.2022.105287

Xing, F. Z., Cambria, E., & Welsch, R. E. (2017). Natural language based financial forecasting: a survey. Artificial Intelligence Review, 50(1), 49–73. https://doi.org/10.1007/s10462-017-9588-9

Zheludev, I., Smith, R., & Aste, T. (2014). When can social media lead financial markets? Scientific Reports, 4(1). https://doi.org/10.1038/srep04213