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Export Reference (APA) 
Ashofteh, A. & Bravo, J. (2021). A conservative approach for online credit scoring. Expert Systems with Applications. 176
Export Reference (IEEE) 
A. Ashofteh and J. M. Bravo,  "A conservative approach for online credit scoring", in Expert Systems with Applications, vol. 176, 2021
Export BibTeX 
@article{ashofteh2021_1766474045612,
	author = "Ashofteh, A. and Bravo, J.",
	title = "A conservative approach for online credit scoring",
	journal = "Expert Systems with Applications",
	year = "2021",
	volume = "176",
	number = "",
	doi = "10.1016/j.eswa.2021.114835",
	url = "https://www.sciencedirect.com/journal/expert-systems-with-applications"
}
Export RIS 
TY  - JOUR
TI  - A conservative approach for online credit scoring
T2  - Expert Systems with Applications
VL  - 176
AU  - Ashofteh, A.
AU  - Bravo, J.
PY  - 2021
SN  - 0957-4174
DO  - 10.1016/j.eswa.2021.114835
UR  - https://www.sciencedirect.com/journal/expert-systems-with-applications
AB  - 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.
ER  -