This research is aimed at the case of credit scoring in risk management and presents a novel machine learning method to be used for the default prediction of high-risk branches or customers. This study uses the Kruskal-Wallis non-parametric statistic to form a conservative credit-scoring model and to study the impact on modeling performance on the benefit of the credit provider. The findings show that the new credit scoring methodology represents a reasonable coefficient of determination and a very low false-negative rate. It is computationally less expensive with high accuracy with around 18% improvement in Recall/Sensitivity. Because of the recent perspective of continued credit/behavior scoring, our study suggests using this credit score for non-traditional data sources for online loan providers to allow them to study and reveal changes in client behavior over time and choose the reliable unbanked customers, based on their application data. This is the first study that develops an online non-parametric credit scoring system, which is able to reselect effective features automatically for continued credit evaluation and weigh them out by their level of contribution with a good diagnostic ability.

--

Risk analysis,Online credit scoring,Big Data,Kruskal_Wallis statistic,Open banking,Machine learning

• Computer and Information Sciences - Natural Sciences
• Civil Engineering - Engineering and Technology
• Electrical Engineering, Electronic Engineering, Information Engineering - Engineering and Technology
• Economics and Business - Social Sciences