Application of Operations Research in Financial Risk Management

Financial risk management has moved from a specialist function to a core part of financial strategy, and operations research (OR) supplies much of its mathematical backbone through optimization, probability modelling, simulation and algorithmic decision support. Existing reviews, however, are organized around a single risk category or industry sector rather than around OR as a methodology, and most stop before the 2024–2026 surge in AI-integrated work. This paper reviews the literature linking OR and financial risk management and complements it with a bibliometric analysis of 260 Scopus documents (227 articles and 33 reviews) published between 2010 and 2026, retrieved with the search string “Risk Management AND Investments AND Analytics” and screened using the PRISMA framework. Performance analysis, keyword co-occurrence and a Callon centrality–density thematic map were produced using Biblioshiny, VOSviewer, Excel and Tableau. The results show a rapidly expanding field, with an annual growth rate of 24.66% and 65.8% of documents published in the last three years. Growth is led by technology rather than by finance-specific themes: machine learning and Industry 4.0 are the motor themes, technology vocabulary accounts for 59.2% of keyword occurrences on the map, and no finance-specific cluster occupies the motor quadrant. The scholarly landscape is also fragmented, with 190 of 218 sources publishing a single document and no author contributing more than three. The paper closes with a future research agenda that calls for structured comparisons of machine-learning models against Value-at-Risk and Conditional Value-at-Risk benchmarks, greater emphasis on explainable AI, sustained interdisciplinary research groups and renewed attention to the OR foundations of risk measurement.