Machine-Learning-Based Enterprise Risk Classification and Mitigation Using Predictive Analytics

Shourya Gupta

Vol. 9, Issue 1, Jan-Dec 2023

Page Number: 94 - 102

Abstract:

Business risks are increasingly shaped by fast-changing markets, complex supply chains, digital operations, and evolving regulation. Traditional risk management approaches (workshops, qualitative scoring, periodic audits) remain essential, but they often struggle with early detection, real-time monitoring, and scaling across many business units. Machine learning (ML) can strengthen risk management by (1) identifying weak signals of emerging risks, (2) estimating likelihood and impact with data-driven models, (3) improving detection of anomalies and fraud, and (4) supporting better, faster mitigation decisions. This paper proposes an end-to-end ML risk management framework that connects risk identification, quantification, explainability, and control selection. We review common business risk categories (operational, supply chain, cyber, compliance/fraud, and financial/credit), map them to ML problem types, and outline model development choices (supervised, unsupervised, NLP, time series, causal and probabilistic models). We also present a comparative analysis of model families (logistic regression, random forest, gradient boosting, deep learning, Bayesian networks, and anomaly detection methods) across accuracy, interpretability, data needs, and deployment complexity. Practical issues including data quality, concept drift, fairness, governance, and integration into Enterprise Risk Management (ERM) processes are discussed. Finally, we provide implementation guidance and metrics aligned with risk outcomes, not only predictive performance.

References

• Agyemang, E. F. (2024). Anomaly detection using unsupervised machine learning algorithms: A simulation study. Scientific African, 26, e02386. https://doi.org/10.1016/j.sciaf.2024.e02386
• Aziz, S., & Dowling, M. (2018). Machine learning and AI for risk management. In T. Lynn, J. Mooney, P. Rosati, & M. Cummins (Eds.), Disrupting finance: FinTech and strategy in the 21st century (pp. 33–50). Palgrave Macmillan. https://doi.org/10.1007/978-3-030-02330-0_3
• Baryannis, G., Dani, S., & Antoniou, G. (2019). Predicting supply chain risks using machine learning: The trade-off between performance and interpretability. Future Generation Computer Systems, 101, 993– 1004. https://doi.org/10.1016/j.future.2019.07.059
• Cornwell, N., Bilson, C., Gepp, A., Stern, S., & Vanstone, B. J. (2023). Modernising operational risk management in financial institutions via data-driven causal factors analysis: A pre-registered report. *Pacific-Basin Finance Journal, 77*, 101906. https://doi.org/10.1016/j.pacfin.2022.101906
• Cremer, F., Sheehan, B., Fortmann, M., Kia, A. N., Mullins, M., Murphy, F., & Materne, S. (2022). Cyber risk and cybersecurity: A systematic review of data availability. *The Geneva Papers on Risk and Insurance - Issues and Practice, 47*(3), 698–736. https://doi.org/10.1057/s41288-022-00266-6
• Cremer, F., Sheehan, B., Fortmann, M., Kia, A. N., Mullins, M., Murphy, F., & Materne, S. (2022). Cyber risk and cybersecurity: A systematic review of data availability. *The Geneva Papers on Risk and Insurance - Issues and Practice, 47*(3), 698–736. https://doi.org/10.1057/s41288-022-00266-6
• Kia, A. N., Fortmann, M., Mullins, M., Murphy, F., Cremer, F., Sheehan, B., & Materne, S. (2024). A cyber risk prediction model using common vulnerabilities and exposures. Expert Systems with Applications, 238(Part B), 121599. https://doi.org/10.1016/j.eswa.2023.121599
• Kozodoi, N., Jacob, J., & Lessmann, S. (2022). Fairness in credit scoring: Assessment, implementation and profit implications. European Journal of Operational Research, 297(3), 1083– 1094. https://doi.org/10.1016/j.ejor.2021.06.023
• Lundberg, S. M., Erion, G., Chen, H., DeGrave, A., Prutkin, J. M., Nair, B., Katz, R., Himmelfarb, J., Bansal, N., & Lee, S.-I. (2020). From local explanations to global understanding with explainable AI for trees. Nature Machine Intelligence, 2(1), 56–67. https://doi.org/10.1038/s42256-019-0138-9
• Schroeder, M., & Lodemann, S. (2021). A systematic investigation of the integration of machine learning into supply chain risk management. Logistics, 5(3), 62. https://doi.org/10.3390/logistics5030062
• Wang, L., Zhang, Y., & Zhang, J. (2025). Risk management based on machine learning: A review of applications and frameworks. ASME Open Engineering, 3(1), 020001. https://doi.org/10.1115/1.4069023

Back Download