Bank Risk Management

Banking risks are phenomena that occur, more or less, in unforeseen or predictable situations. Controlling and preventing the effects of bank risks is based on rigorous management in the banking system. Knowing bank risks involves activities and involves going through several stages.The first is that it is necessary to know and forecast the banking risk. The second is the identification of bank risk, which depends on the diversity of business lines and banking products / services offered to customers. Furthermore, the quantitative and qualitative risk analysis involves the use of techniques and tools, but above all the skills of workers studying the risks, analyzes their effects and thus provides for measures accordingly.Establishing risk strategies is a very important issue that aims to minimize risk-related expenses but at the same time requires banking supervision to avoid supervision and monitoring of the National Bank. The authors point out that the National Bank plays a decisive role in establishing and analyzing the banking system. Last but not least, risk monitoring and control means that once identified these risks must be monitored and, as far as possible, the necessary measures are taken to ensure that they are rigorously controlled.It is necessary, and the authors have dealt with the setting of risk management models, identifying two methods (models) for banking risk management. Thus, three authors, Williams, Smith and Young, consider that for risk management is an equally important, if not the most important component, and it is stated that risk management is an organizational level that must be well established, known and applied . Then, Glyn Holton believes that risk management must be part of the organizational culture of management. The authors focus on quantitative risk analysis using modeling problems, from risk modeling to the Monte Carlo simulation method. Analysis based on the decision tree is one of the aspects to which the authors have attached them and, schematically, show that these are tools that describe the key interactions between decisions and the random elements as perceived by the decision-makers.An interesting approach is made to the Monte Carlo simulation method that addresses input variables, defining the distributions of „random variables”, analyzing output variables, applying simulation, and ultimately applying analysis and interpretation of simulation results.