A Structured Causal Framework for Operational Risk Quantification: Bridging Subjective and Objective Uncertainty in Advanced Risk Models

Evaluating risk in complex systems relies heavily on human auditors whose subjective assessments can be compromised by knowledge gaps and varying interpretations. This subjectivity often results in inconsistent risk evaluations, even among auditors examining identical systems, owing to differing pattern recognition processes. In this study, we propose a causality model that can improve the comprehension of risk levels by breaking down the risk factors and creating a layout of risk events and consequences in the system. To do so, the initial step is to define the risk event blocks, each comprising two distinct components: the agent and transfer mechanism. Next, we construct a causal map that outlines all risk event blocks and their logical connections, leading to the final consequential risk. Finally, we assess the overall risk based on the cause-and-effect structure. We conducted real-world illustrative examples comparing risk-level assessments with traditional experience-based auditor judgments to evaluate our proposed model. This new methodology offers several key benefits: it clarifies complex risk factors, reduces reliance on subjective judgment, and helps bridge the gap between subjective and objective uncertainty. The illustrative examples demonstrate the potential value of the model by revealing discrepancies in risk levels compared to traditional assessments.