The past decade has shown a surge in the use and application of machine learning deep models across different domains. One such domain is credit scoring domain, where applicants are scored to assess their credit-worthiness for loan applications. During process, it key assure that there no biases or discriminations incurred. Despite proliferation (referred as black-box literature) scoring, still...

This research is aimed at the case of credit scoring in risk management and presents a novel machine learning method to be used for default prediction high-risk branches or customers. study uses Kruskal-Wallis non-parametric statistic form conservative credit-scoring model impact on modeling performance benefit provider. The findings show that new methodology represents reasonable coefficient d...

This study implements the multivariable logistic regression to develop a credit scoring model based on tenants’ characteristics. The history of tenant is not considered. Rental information tenants was collected from landlord company in Malaysia. Parameters were estimated by using penalized maximum likelihood estimation with ridge since separation training data detected. initial factors consider...

In the field of credit risk analysis, the problem that we often encountered is to increase the model accuracy as possible using the limited data. In this study, we discuss the use of supervised neural networks as a metalearning technique to design a credit scoring system to solve this problem. First of all, a bagging sampling technique is used to generate different training sets to overcome dat...