Credit Scoring Models Using Soft Computing Methods: A Survey

被引：1

作者：

Lahsasna, Adel ^{[1
]}

Ainon, Raja Noor ^{[1
]}

Teh, Ying Wah ^{[1
]}

机构：

[1] Univ Malaya, Fac Comp Sci & Informat Technol, Kuala Lumpur, Malaysia

来源：

INTERNATIONAL ARAB JOURNAL OF INFORMATION TECHNOLOGY | 2010年 / 7卷 / 02期

关键词：

Credit scoring; credit risk; soft computing; data mining; SUPPORT VECTOR MACHINES; NEURAL-NETWORKS; RULE EXTRACTION; RISK EVALUATION; MINING APPROACH; FUZZY; ALGORITHMS; DESIGN;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

During the last fifteen years, soft computing methods have been successfully applied in building powerful and flexible credit scoring models and have been suggested to be a possible alternative to statistical methods. In this survey, the main soft computing methods applied in credit scoring models are presented and the advantages as well as the limitations of each method are outlined. The main modelling issues are discussed especially from the data mining point of view. The study concludes with a series of suggestions of other methods to be investigated for credit scoring modelling.

引用

页码：115 / 123

页数：9

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