A non-linear analysis of operational risk and operational risk management in banking industry

The analysis of operational risk events in banks is complicated as the financial institutions themselves, as well as the external environment in which financial institutions operate, are complex adaptive systems. As a complex adaptive system, it is not possible to analyse the outcomes of the system from a reductionist perspective as it is the interaction of the agents in the system that drive the system outcome. Most analysis of operational risk in banks has involved statistical analysis of the frequency and severity of historical events, but this analysis does not identify the drivers of the events that is necessary to assist management in managing the future events to an acceptable level. This study aims at providing insights into operational risk management from a complex adaptive systems view. An empirical study with AU, US and EU data will use cladistics analysis, networks analysis and complexity theory for the validation of the methodology. The result of the empirical study shows relatively stable important drivers of operational risk events. The findings present the feasibility of applying the theories from other fields, i.e. cladistics analysis and physics theory into analysing operational risk. Further, it reveals the management environment and management style in different markets is relatively stable after the GFC.