School of Information Management and Engineering, Shanghai University of Finance and Economics, Shanghai, 200433, China; Key Laboratory of Financial Information Technology of Shanghai City, Shanghai, 200433, China

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Abstract—Operational risk has been great challenges of commercial banks and financial institutions since losses caused by operational risk have been significantly greater than ever before. In order to control and manage operational risk better，clear awareness of the operational risk conduction mechanism is helpful to further understand the whole dynamic process of the risks affecting the business. Traditional researches focus on the method to measure the risk more accurately, but few studies took into account the conduction mechanism of operational risk. This paper constructs operational risk network to simulate the contagion process of operational risk and put forward new measurement model based on complex network. First, properties of the operational risk network is analyzed, and risk spreading process in the network is described. Then, a load model is established to explain the conduction mechanism of operational risk factors. Next, a new measurement method of operational risk based on BA model is presented to make Monte Carlo simulation and calculate the amount of operational risk losses in the operational risk network. Also, this model is used to simulate the affecting factors in the operational risk network. The simulation results show that the degree of operational risk network as well as the selection of initial node plays an important role on losses, and thus some control recommendations based on the simulation results is given. Finally, a framework of operational risk management system is designed to apply this research result into practice.

Index Terms—Operational Risk, Scale-free Network, BA Model, Monte-Carlo Simulation

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Cite: Xiaoling Hao, Songqiao Han, "Measurement and Control of Operational Risk of Banking Industry based on Complex Network," Journal of Software vol. 9, no. 4, pp. 820-828, 2014.