Knowledge level modeling for systemic risk management in financial institutions

被引:16
|
作者
Ye, Kang [1 ]
Yan, Jiaqi [2 ]
Wang, Shanshan [3 ]
Wang, Huaiqing [2 ]
Miao, Baiqi [4 ]
机构
[1] Res Ctr, 528 Pudong Rd, Shanghai 200120, Peoples R China
[2] City Univ Hong Kong, Dept Informat Syst, Kowloon, Hong Kong, Peoples R China
[3] Inner Mongolia Univ, Coll Comp Sci, Inner Mongolia, Peoples R China
[4] Univ Sci & Technol China, Dept Finance & Stat, Hefei 230026, Peoples R China
关键词
Systemic risk; Knowledge level modeling; Ontology; Problem solving model; Subprime crisis; QUALITY; CRISIS;
D O I
10.1016/j.eswa.2010.08.141
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
The current subprime mortgage crisis is a typical case for systemic risk in financial institutions. In order to further our understanding and communication about systemic risk management (SRM) in financial institutions, this paper proposes a knowledge level model (KLM) for systemic risk management in financial institutions. There are two parts considered in the proposed KLM: ontologies and problem solving method (PSM). Ontologies are adopted to represent a knowledge base of KLM, which integrates top level ontology and domain level ontologies. And then the problem solving method is given to show the reasoning process of this knowledge. The symbol level of KLM is also discussed which integrates OWL, SWRL and JESS. Further, the discussion about Lehman Brother's Minibonds case in 2008 is provided to illustrate how proposed KLM is used in practice. With these, first, they will enhance the interchange of information and knowledge sharing for SRM within a financial institution. Second, they will assist knowledge base development for SRM design, for which a prototype of financial systemic risk management decision support system is given in this study. Third, they will support coordination among different institutions by using standardized vocabularies. And finally, from the design science perspective, the whole proposed framework could be meaningful to models in other domains. (C) 2010 Elsevier Ltd. All rights reserved.
引用
收藏
页码:3528 / 3538
页数:11
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