Modernising operational risk management in financial institutions via data-driven causal factors analysis: A pre-registered study

Abstract

In an effort to contribute a quantitative, objective and real-time tool to proactively and precisely manage the factors underlying and exacerbating operational risks, this pre-registered study executes the empirical methodology approved in the associated pre-registered report (Cornwell et al., 2023). The application of the Bayesian network-based approach to an Australian insurance company shows that integrating a financial institution's loss and operational data in this way can effectively model the probability of an operational loss event within its interconnected operational risk environment. Further insights and efficiencies are gained by modelling multiple operational loss events together, rather than in isolation. A novel two-module framework derived specifically for causal factors analysis from the resulting operational risk model helps to highlight the relative importance of causal factors, their collective effects and critical thresholds requiring proactivity. These insights derived from the framework are expected to be strategically valuable in helping an organisation design intentional and targeted controls for and monitoring of operational risks. Given existing knowledge of the improvements quantitative risk management tools make to risk management effectiveness and subsequently firm value, the enhanced risk management and the operational efficiencies this tool seeks to afford should ultimately contribute to driving financial performance and firm value.