Risk insolvency predictive model maximum expected utility

This paper presents a new approach to develop the probability of default for private firms. This work provides a global perspective regarding the credit risk prediction, starting from the work of the Basel Committee on Banking Supervision, with a deep study of the more predictive variables for default prediction and, finally, building a new mathematical model based on machine learning. The used method is called Maximum Expected Utility (MEU) and represents the most promising methodology for the default prediction. The main idea is to use the interaction between variables to improve the final model efficiency. The development model has been tested on complex analytical function (in which the classical models fault) and finally has been developed to assess the distress of industrial companies according to Basel II guidelines. The evidence was related to Italian industrial enterprises and took into consideration, the situation of the Italian economy both from a micro and macro perspective.