A BP-Neural Network Predictor Model for Operational Risk Losses of Commercial Bank

With financial globalization, the rapid development of financial derivatives and the complexity of banks management, operational risk measurement and management in commercial bank management is becoming increasingly important. How to effectively predict, control and prevent operational risk in commercial banks have become an important issue. Using BP neural network model to predict the risk has its unique advantages. In recent years, there have been more successful applications in the financial field. In this article, a BP neural network prediction model is built with Matlab, which overcomes ambiguity of its definition and diversity in the traditional analysis of operational risks using BP neural network self-learning, nonlinear mapping, adaptability and strong fault tolerance. The result of experiments shows that the results of this forecast is useful for the measure of losses and the model is valid for a given sample and appropriate algorithm with appropriate nodes.