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

Abstract

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 wellknown 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 metafeatures 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.

A recent review on optimisation methods applied to credit scoring models

“…, 2016). Thus, those customers whose CSMs present a high probability of being ‘ good ’ payers are accepted, and those with low probability are declined (Finlay, 2009; Breeden, 2021; Carta et al. , 2021).…”

“…Thus, the RF, ANN, SVM and LR with Ridge penalty were used for the learning and evaluation of the referred CSM. Carta et al. (2021) proposed an ensemble stochastic criterion that operates in a discretised feature space and is extended to some meta-features to build an efficient CSM.…”

“…Most CSMs aspire to identify the characteristics that influence behaviours that lead to either payment or default in a way that a customer might be classified as a 'good' or 'bad' payer (Louzada et al, 2016). Thus, those customers whose CSMs present a high probability of being 'good' payers are accepted, and those with low probability are declined (Finlay, 2009;Breeden, 2021;Carta et al, 2021). Furthermore, the latest CSMs have been employed for issues such as profitability, the use of Big Data (BD), Deep Learning (DL), equity in analysis, and sustainability (refer to Bastani et al, 2019;Kozodoi et al, 2019;Ashofteh and Bravo, 2021;Dastile and Celik, 2021;Djeundje et al, 2021;Kang et al, 2021 andKozodoi et al, 2022).…”

“…Thus, the RF, ANN, SVM and LR with Ridge penalty were used for the learning and evaluation of the referred CSM. Carta et al (2021) proposed an ensemble stochastic criterion that operates in a discretised feature space and is extended to some meta-features to build an efficient CSM. This approach uses a real-world data set with different data imbalance configurations to apply the following classification algorithms: RF, DT, Adaptive Boosting, Multilayer Perceptron and Gradient Boosting (GB).…”