A Survey of Applying Machine Learning Techniques for Credit Rating: Existing Models and Open Issues

被引：11

作者：

Wang, Xiang ^{[1
]}

Xu, Min ^{[1
]}

Pusatli, Ozgur Tolga ^{[2
]}

机构：

[1] Univ Technol Sydney, Global Big Data Technol Ctr, Sydney, NSW 2007, Australia

[2] Cankaya Univ, Dept Math & Comp Sci, Ankara, Turkey

来源：

NEURAL INFORMATION PROCESSING, PT II | 2015年 / 9490卷

关键词：

Credit rating; Single classifier models; Hybrid learning models; Literature survey; SUPPORT VECTOR MACHINES; NEURAL-NETWORKS; BANKRUPTCY PREDICTION; DISCRIMINANT-ANALYSIS; BOND RATINGS; RISK; CLASSIFIER; ENSEMBLES;

D O I：

10.1007/978-3-319-26535-3_15

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In recent years, machine learning techniques have been widely applied for credit rating. To make a rational comparison of performance of different learning-based credit rating models, we focused on those models that are constructed and validated on the two mostly used Australian and German credit approval data sets. Based on a systematic review of literatures, we further compare and discuss about the performance of existing models. In addition, we identified and illustrated the limitations of existing works and discuss about some open issues that could benefit future research in this area.

引用

页码：122 / 132

页数：11

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