A Novel Method for Credit Scoring Based on Cost-Sensitive Neural Network Ensemble

Abstract

Most existing studies on credit scoring adapted a concept of classifier ensemble for solving an imbalanced dataset. They apply resampling methods to generate multiple training subsets for constructing multiple base classifiers. However, this approach leads to several problems that degrade the classification performance, such as problems of information loss, model overfitting, and computational cost. Thus, we propose a novel ensemble approach for developing a credit scoring model based on a cost-sensitive neural network, called Cost-sensitive Neural Network Ensemble (CS-NNE). In the proposed approach, multiple class weights are adapted to original training data, enabling the multiple base neural networks to consider imbalanced classes. Following this approach, a high diversity of multiple base classifiers without consequent problems can be achieved. The approach's effectiveness is evaluated on five real-world credit datasets. Among them is a loan-requesting dataset provided by a financial institution in Thailand. The remaining datasets are publicly available and widely used by several existing studies. The experimental results showed that the proposed CS-NNE approach improves the predictive performance over a single neural network based on imbalanced credit datasets, e.g., Thai credit dataset, by achieving 1.36%, 15.67%, and 6.11% Area under the ROC Curve (AUC), Default Detection Rate (DDR), and G-Mean (GM), respectively, and achieving the best Misclassification Cost (MC). The proposed CS-NNE approach can effectively solve a class of imbalance problems and outperform many existing models. The prediction model can well compromise between classes of default (bad credit applicants) and non-default (good credit applicants), whereas existing approaches preferred a class of non-default over default loans (having high specificity and low DDR), resulting in NPL.

Intelligent credit scoring using deep learning methods

“…Some of the newer studies also applied novel machine learning techniques to German credit scoring models: step‐wise multi‐grained augmented gradient boosting decision trees, 33 dynamic 1‐nearest neighbor 34 and Cost‐sensitive Neural Network Ensemble 35 …”

Cost-Sensitive Metaheuristic Optimization-Based Neural Network with Ensemble Learning for Financial Distress Prediction

“…On the other hand, ensemble classifiers have been effectively employed in credit scoring and the forecasting of company insolvency in recent years. For example, a costsensitive neural network ensemble for credit scoring was proposed in [29]. The suggested method outperformed the benchmark individual and ensemble methods, as evidenced by the comparative results.…”

The Advantage of Case-Tailored Information Metrics for the Development of Predictive Models, Calculated Profit in Credit Scoring

“…Thus, misclassification costs are not fully taken into account, and it has been proven that an additional advantage can be gained by using metrics considering misclassification costs throughout the whole optimization stage [ 26 ]. Some authors already addressed this issue in the area of credit scoring [ 27 , 28 ]. There were also proposals for new metrics incorporating cost aspects.…”