Models Of Risk Management Based on Soft Computing Methods
Preventing risk through analysis tools means having an advantage over it.
Preventing risk through analysis tools means having an advantage over it.
Managing risk means having an advantage over it, and this advantage is the difference where the unexpected is greater. The risk is closely connected to the modification, over time, of a variable. This variable can cause positive or negative aspects to take risks, a hope of success against the risk of failure.
The core of risk management is that of identifying the possible risk, the determination and its meaning, analysis and modeling. The ultimate goal is to provide support.
The methods used in the past, such as expert systems based on the classic risk matrices, have proved unsuitable. Today by means of Soft Computing it is possible to manage risk with tools that manage to model the physical processes of risk.
Fuzzy Logic, Neural Networks and Genetic Algorithms are now essential tools in risk management in all its facets. The Fuzzy logic allows you to work in terms of the vague and inaccurate; identification and determination of risk.
Neural networks represent the thought model of the human brain; risk analysis and modeling. Genetic algorithms simulate evolution in terms of selection, intersection and mutation; risk evolution and forecasting.
The end result of the risk management model is therefore to provide support to the decision process.
The correct decision is a fundamental step to translate a risk into an advantage. The methodologies for the construction of risk management models implemented by EthosLab fully merge with Soft Computing and enhance every aspect thanks to highly innovative tools.