LOGICAL AND PROBABILISTIC MODELS FOR ESTIMATION OF BANKING RISKS
Logical and probabilistic models are widely applied for estimation the risk in technical systems. Logical and probabilistic method uses tree of events (failure tree) as risk scenario, logical and probabilistic functions and allows calculate exact numerical risk estimation, perform risk analysis and realize decision-making procedures. In this paper, authors analyze application of this method to estimate and analyze risk in banking. The large volume of statistical data makes the application of logical and probabilistic models easy but the algorithm of logical and probabil istic model identification is required. This is complex optimization of many-dimensional integer function with real arguments. Logical and probabilistic models allow calculate risk (probability of undesirable event) and contributions of initiating events in risk, i.e. perform risk analysis. Risk management is performed as decision-making procedures in accordance with contribution values. Integration of logical and probabilistic models, identification algorithm and method of randomized summarized indexes (to obtain probabilities if we have no statistical data) give us powerful analytical tool to manage risk and perform decision-making procedures in complex socioeconomic systems.