A conservative approach for online credit scoring
نویسندگان
چکیده
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 determination very low false-negative rate. It computationally less expensive with high accuracy around 18% improvement Recall/Sensitivity. Because recent perspective continued credit/behavior scoring, our suggests using this score non-traditional data sources online loan providers allow them reveal changes client behavior over time choose reliable unbanked customers, based their application data. first develops an system, which able reselect effective features automatically evaluation weigh out by level contribution good diagnostic ability.
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ژورنال
عنوان ژورنال: Expert Systems With Applications
سال: 2021
ISSN: ['1873-6793', '0957-4174']
DOI: https://doi.org/10.1016/j.eswa.2021.114835