Credit scoring by leveraging an ensemble stochastic criterion in a transformed feature space

The credit scoring models are aimed to assess the capability of refunding a loan by assessing user reliability in several financial contexts, representing a crucial instrument for a large number of financial operators such as banks. Literature solutions offer many approaches designed to evaluate users' reliability on the basis of information about them, but they share some well-known problems that reduce their performance, such as data imbalance and heterogeneity. In order to face these problems, this paper introduces an ensemble stochastic criterion that operates in a discretized feature space, extended with some meta-features in order to perform efficient credit scoring. Such an approach uses several classification algorithms in such a way that the final classification is obtained by a stochastic criterion applied to a new feature space, obtained by a twofold preprocessing technique. We validated the proposed approach by using real-world datasets with different data imbalance configurations, and the obtained results show that it outperforms some state-of-the-art solutions.