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 The credit risk evaluation models: An application of data mining techniques
Tác giả hoặc Nhóm tác giả: Cuong Nguyen
Nơi đăng: Proc. SAIS 2019; Số: 2019;Từ->đến trang: 1-6;Năm: 2019
Lĩnh vực: Công nghệ thông tin; Loại: Báo cáo; Thể loại: Quốc tế
TÓM TẮT
In the banking sector, credit risk assessment is an important operation in ensuring that loans could be paid on time, and banks could maintain their credit performance effectively; despite restless business efforts allocated to credit scoring yearly, high percentage of loan defaulting remains a major issue. With the availability of tremendous banking data and advanced analytics tools, classification data mining algorithms can be applied to develop a platform of credit scoring, and to resolve the loan defaulting problem. With the dataset of 5,960 observations representing information about characteristics of underlyingcollateral loans, the paper sets out a data mining process to compare four classification algorithms, including logistic regression, decision tree, neural network, and XGboost in performance. Via the confusion matrix and Monte Carlo simulation benchmarks, the XGboost outperforms as the most accurate and …
ABSTRACT
In the banking sector, credit risk assessment is an important operation in ensuring that loans could be paid on time, and banks could maintain their credit performance effectively; despite restless business efforts allocated to credit scoring yearly, high percentage of loan defaulting remains a major issue. With the availability of tremendous banking data and advanced analytics tools, classification data mining algorithms can be applied to develop a platform of credit scoring, and to resolve the loan defaulting problem. With the dataset of 5,960 observations representing information about characteristics of underlyingcollateral loans, the paper sets out a data mining process to compare four classification algorithms, including logistic regression, decision tree, neural network, and XGboost in performance. Via the confusion matrix and Monte Carlo simulation benchmarks, the XGboost outperforms as the most accurate and …
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