During the last fifteen years, soft computing methods have been successfully applied in building powerful and flexible credit scoring models and have been suggested to be a possible alternative to statistical methods. In this survey, the main soft computing methods applied in credit scoring models are presented and the advantages as well as the limitations of each method are outlined. The main ...

In this paper, we will evaluate the power and usefulness of Bayesian network classifiers for credit scoring. Various types of Bayesian network classifiers will be evaluated and contrasted including unrestricted Bayesian network classifiers learnt using Markov Chain Monte Carlo (MCMC) search. The experiments will be carried out on three real life credit scoring data sets. It will be shown that M...

In this study, we use least square support vector machines (LSSVM) to construct a credit scoring model and introduce conjoint analysis technique to analyze the relative importance of each input feature for making the decision in the model. A test based on a real-world credit dataset shows that the proposed model has good classification accuracy and can help explain the decision. Hence, it is an...

For most Americans, access to credit is an essential requirement for upward mobility and financial success. A favorable credit rating is necessary to purchase a home or car, to start a new business, to seek higher education, or to pursue other important goals. For many consumers, strong credit is also necessary to gain access to employment, rental housing, and essential services such as insuran...

Multi-Layer Perceptron (MLP) neural networks are widely used in automatic credit scoring systems with high accuracy and efficiency. This paper presents a higher accuracy credit scoring model based on MLP neural networks that have been trained with the back propagation algorithm. Our work focuses on enhancing credit scoring models in three aspects: (i) to optimise the data distribution in datase...

Credit scoring is one of the most successful applications in banking and finance. However, most studies do not explain the whole process of scorecard development, probably due to the difficulty in obtaining credit scoring data. This study addresses some of the gaps that are present in the existing literature in that it explains in detail the processes, as performed in practice, of scorecard dev...

This paper applies a hybrid classification approach combining rough set and genetic programming (GP) to construct the credit scoring model. Comparing with the previous credit scoring model only based on GP, the hybrid method not only makes an improvement in the average classification accuracy, but also saves the required computational effort.