Data Processing in Model of the Bank Credit Risk Assessment Based on Asset Assessment

Abstract:

Asset assessment theory research lagging behind the industry development is an important problem existing in current assessment industry, and the construction of theoretical framework system is particularly urgent. Asset appraisal theory frame system is divided into the logical starting point and the theoretical framework. According to the connotation of asset assessment and the concept of the logic starting point in philosophy, the logic starting point of the asset assessment is thought to be the value discovery. According to the relationship to the logical starting point, establish a theoretical framework with four-layers, which takes the value found as the qualitative, nine evaluation factors as the kernel, takes the legal system, exam training, industry, rules and member acknowledges as the outer core, takes the institutional environment, economic environment, social and cultural environment etc. as the evaluating environment.

• Periodical: Applied Mechanics and Materials (Volume 707)
• Pages: 493-496
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.707.493
• Online since: December 2014
• Authors: Lv Rong Wang*
• Keywords: Asset Assessment, Bank Credit, Risk Assessment

References

[1] Zhao Xiaocui, Wang Laisheng. The commercial bank credit risk assessment based on principal component analysis and support vector machine. Journal of statistics and decision making. 2012 (13).

[2] Zhen Tong, Fan Yanfeng. Research of enterprise credit risk assessment based on support vector machine. Journal of microelectronics and computer. 2013 (05).

[3] Huang Yong, Zheng Chunying, Song Zhonghu. Summary of the multiclass support vector machine algorithm. Journal of computing and automation. 2012 (04).

[4] Liu Xuewei, He Changzheng. The listed company credit evaluation based on Bayesian regularization BP neural network. Journal of software science, 2011 (05).

[5] Shi Qingyan. A neural network based-Logistic regression hybrid two-phase personal credit scoring model study. Journal of statistical research. 2012 (05).