APPROACHES TO OPERATIONAL RISK ASSESSMENT IN FINANCIAL INSTITUTIONS

Abstract

The article examines approaches to assessing operational risk in financial institutions, with an emphasis on quantitative, qualitative and hybrid methods in the context of rising uncertainty driven by military, geopolitical, technological and regulatory factors. Theoretical foundations of traditional metrics (the product of probability and loss severity), contemporary statistical approaches (loss distribution modeling, Value at Risk) and a set of qualitative instruments (expert judgment, scenario analysis, root‑cause analysis, etc.) are considered, as well as their combinations within hybrid approaches for constructing a comprehensive risk profile of a financial institution. It is argued that the quantitative approach provides measurability and comparability of results but its applicability is constrained by the quality and sufficiency of historical data; the qualitative approach enables identification and assessment of specific risk events including non‑financial ones, but is inherently subjective; the hybrid approach combines the strengths of both, striking a balance between statistical precision and expert interpretation of results. The advantages and limitations of modern methods (LDA, VaR) are demonstrated, notably their capacity to quantify aggregate risk while offering limited direct support for decision‑making at the level of individual processes. The study concludes that effective operational risk management requires integration of quantitative, qualitative and hybrid approaches into the operational risk management framework: quantitative models provide a basis for measuring financial losses using historical data, qualitative methods fill gaps in historical records and reveal emerging risks, and hybrid approaches deliver consolidated, stakeholder‑comprehensible outputs that contextualize quantitative estimates within defined ranges. The paper recommends implementing integrated assessment practices, including data normalization and aggregation, model validation, sensitivity analysis and result visualization, to improve the quality of managerial decisions and enhance the resilience of financial institutions. Future research of operational risk assessment approaches in financial institutions should advance standardized approaches to data normalization and benchmarking, refine hybrid methodologies that balance statistical precision with expert interpretation, and conduct longitudinal studies on emerging risks and institutional resilience under geopolitical and technological transformation.