Risk management is one of the most important branches of business and finance. Classification models are the most popular and widely used analytical group of data mining approaches that can greatly help financial decision makers and managers to tackle credit risk problems. However, the...

Knowledge generated using data mining techniques is of great interest for organizations, as it facilitates tactical and strategic decision making, generating a competitive advantage. In the special case of credit granting organizations, it is important to clearly define rejection/approval...

This paper proposes and examines a new structural risk of default model for banks in frictional and fuzzy financial markets. It is motivated by the need to fill the shortcomings of probability-based credit risk metric models that are characterised by unrealistic assumptions such as crisply...