Operational Risk Assessment: A Multi- Objective, Adaptive Framework Enhanced by Advanced Data Analytics

Abstract

In an era characterized by geopolitical upheaval, economic instability, and escalating ecological threats, operational risks have become increasingly interconnected and dynamic, necessitating a multi-objective and adaptive Operational Risk Assessment (ORA) approach to address these risks effectively and systematically. In addition, the analysis of these multi-objective, interrelated, and dynamic operational risks often requires processing massive, multivariate, and rapidly changing data in real time. Consequently, there is a pressing need for the development of innovative ORA frameworks and methodologies that can harness the power of advanced data analytics to enable more accurate, timely, and actionable risk assessments. This thesis addresses the aforementioned challenges by developing ORA framework that leverages Multi-Criteria Decision Analysis (MCDA) techniques to systematically balance competing priorities across various operational objectives, thereby creating a comprehensive and balanced risk profile. By incorporating multiple criteria and stakeholder perspectives, the MCDA approach ensures that the risk assessment process is inclusive, transparent, and aligned with organizational goals. Furthermore, the MCDA based ORA framework integrates advanced data analysis techniques, such as Structured Time Series (STS) analytics, to capture the temporal dynamics of operational risks by modeling the time-dependent relationships among risk factors and their impact on the overall risk profile. By continuously updating the risk profiles based on real-time data and Insights derived from these advanced data analytics, the proposed ORA framework enables multi-objective and adaptive risk assessment strategies. This innovative approach to ORA not only enhances the accuracy and timeliness of risk assessments but also facilitates dynamic decision-making and resource allocation in response to emerging threats and opportunities. The research is divided into two parts. The first part, detailed in Paper I, II and III, includes a field study and comparative survey that establish a foundational understanding of the current challenges and opportunities in ORA. The second part, covered in Paper IV to V, focuses on the development and validation of the multi-objective and adaptive ORA framework.