The Emerging Financial Pre-warning Systems

The exact prediction of financial crises is an essential research task for decision makers. In recent years, data mining techniques have been used to tackle the related problems and perform a satisfactory job in various domains. However, in the information age, utilizing straightforward data mining techniques to predict financial crises has many shortcomings and limitations. Thus, this investigation utilized the random forest (RF) technique as a pre-processing procedure to determine the most representative features. Then, the selected features were fed into rough set theory to yield interpretable information for decision makers, who can use it to make suitable judgments in a turbulent economic climate. The proposed model is a promising alternative for predicting financial crisis, and it can assist in regard to both taxation and financial institutions.