APPLICATIONS OF OPERATIONS RESEARCH IN FINANCIAL RISK MANAGEMENT: A BIBLIOMETRIC REVIEW
This bibliometric review examines the application of operations research in financial risk management, tracing the field's growth, structure, and emerging directions. A total of 423 peer-reviewed articles retrieved from ScienceDirect using seven targeted Boolean keyword searches were screened, spanning the period 2010–2026, and were analyzed using Biblioshiny (Bibliometrix) and VOSviewer to identify publication trends, keyword co-occurrence, and thematic clusters. The results reveal that research output has grown at an accelerating, near-exponential rate, with more than half of the sampled articles published in the last three years alone. The literature clusters around five core streams: credit risk modelling, systemic and network-based risk, portfolio and Value-at-Risk/Conditional-VaR optimization, artificial intelligence and machine learning in risk analytics, and climate/ESG-linked risk. Keyword co-occurrence analysis shows us that systemic risk as the most central and frequently connected node, linking nearly all the other themes and indicating that the field functions as a single, unified body of research rather than a set of fragmented sub-disciplines. A persistent tension between predictive accuracy and model interpretability emerges as a defining challenge, particularly as machine learning and graph-based methods increasingly converge with traditional credit and network risk frameworks. Building on these findings, the study outlines a forward-looking research agenda centered on artificial intelligence, graph neural networks, quantum computing, blockchain and distributed ledger technology, climate risk integration, and regulatory technology. Overall, the findings offer a structured view of the current state of operations research in financial risk management and highlight where deeper integration across methods and topics is most needed.
Keywords: Financial Risk Management; Operations Research; Bibliometric Analysis; Systemic Risk; Credit Risk; Portfolio Optimization; Artificial Intelligence; Climate Risk.
Saxena, D. R. K., Mehta, A., Gadh, A. S., Agrawal, A., Valiulla, A. & Gada, A. (2026). Applications of Operations Research in Financial Risk Management: A Bibliometric Review. International Journal of Science, Strategic Management and Technology, 02(10), 1-9. https://doi.org/10.55041/ijsmt.v2i10.053
Saxena, Dr, et al.. "Applications of Operations Research in Financial Risk Management: A Bibliometric Review." International Journal of Science, Strategic Management and Technology, vol. 02, no. 10, 2026, pp. 1-9. doi:https://doi.org/10.55041/ijsmt.v2i10.053.
Saxena, Dr,Ananya Mehta,Arshraj Gadh,Aryan Agrawal,Ayaan Valiulla, and Ayushi Gada. "Applications of Operations Research in Financial Risk Management: A Bibliometric Review." International Journal of Science, Strategic Management and Technology 02, no. 10 (2026): 1-9. https://doi.org/https://doi.org/10.55041/ijsmt.v2i10.053.
Al Janabi, M. A. M. (2021). Multivariate portfolio optimization under illiquid market prospects: a review of theoretical algorithms and practical techniques for liquidity risk management. Journal of Modelling in Management, 16(1), 288–309. https://doi.org/10.1108/JM2-07-2019-0178
Borri, N., & Giorgio, G. di. (2022). Systemic risk and the COVID challenge in the european banking sector. Journal of Banking and Finance, 140, 106073. https://doi.org/10.1016/j.jbankfin.2021.106073
Chockalingam, A., Dabadghao, S., & Soetekouw, R. (2018). Strategic risk, banks, and Basel III: estimating economic capital requirements. Journal of Risk Finance, 19(3), 225–246. https://doi.org/10.1108/JRF-11-2016-0142
Chowdhry, B. (2026). Why efficient systems fail spectacularly: A unified theory of systemic risk. Economics Letters, 268, 112984. https://doi.org/10.1016/j.econlet.2026.112984
Faryal, M., Tang, Y., Chui, C. M., & Khan, I. (2026). The rise of FinTech as a systemic risk transmitter: A time-frequency analysis of spillovers to energy and macroeconomic markets. International Review of Economics and Finance, 107, 105112. https://doi.org/10.1016/j.iref.2026.105112
García, V., Marqués, A. I., & Sánchez, J. S. (2019). Exploring the synergetic effects of sample types on the performance of ensembles for credit risk and corporate bankruptcy prediction. Information Fusion, 47, 88–101. https://doi.org/10.1016/j.inffus.2018.07.004
Ghaemi Asl, M. (2026). An AI-optimized strategy for intelligent risk mapping of Islamic and conventional sustainable markets: Assessing the enduring dynamics of technological risk spillovers. Expert Systems with Applications, 296, 128945. https://doi.org/10.1016/j.eswa.2025.128945
Guo, Y. (2025). Research on Investment Bank Risk Monitoring and Early Warning Model Combining Factor Analysis and Artificial Neural Network. Procedia Computer Science, 262, 878–886. https://doi.org/10.1016/j.procs.2025.05.121
Hammoudeh, S., & McAleer, M. (2015). Advances in financial risk management and economic policy uncertainty: An overview. International Review of Economics and Finance, 40, 1–7. https://doi.org/10.1016/j.iref.2015.02.001
Han, C., Li, A., Chi, Y., Wei, Q., & Shi, Y. (2026). LANR-GNN: Label-aware neighborhood resampling graph neural network for credit bond default prediction. Applied Soft Computing, 194, 114993. https://doi.org/10.1016/j.asoc.2026.114993