Identification of Credit Risk of Personal Loan in Commercial Bank Based on SVM

Zeng Lian Zhang

doi:10.4028/www.scientific.net/AMM.281.682

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vol. 281Identification of Credit Risk of Personal Loan in...

Identification of Credit Risk of Personal Loan in Commercial Bank Based on SVM

Abstract:

Guard against financial risks, reduce bad loans, increase the ability to identity risk of commercial banks, the key is risk warning. In view of the increasing proportion of personal loans in banking business, it is particularly important to warning personal loans credit risk. Commercial bank lending itself is a complex nonlinear system, using general linear theory is difficult to objectively reflect the laws of this, this paper uses SVM(support vactor machine). Personal loan credit index first constructed, and then use SVM to identify risk. Results showed that SVM plays an important role in identifying risk in personal loans.

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Periodical:

Applied Mechanics and Materials (Volume 281)

Pages:

682-687

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.281.682

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Online since:

January 2013

Authors:

Zeng Lian Zhang

Keywords:

Commercial Bank, Credit Risk Evaluation, Personal Loan, Support Vector Machine (SVM)

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References

[1] Altman E I, et al. Zeta analysis: Anew model to identify bankruptcy risk of corporations[J]. Journal of Banking and Finance, 1977, 1(1): 29-54.

Google Scholar

[2] Martin D. Early warning of bank failure: A logit regression approach[J]. Journal of Banking and Finance, 1977, 1(3): 249-276.

Google Scholar

[3] Ohlson J. Financial ratios and the probabilistic prediction of bankruptcy [J]. Journal of Accounting Research, 1980, 18(1): 109-131.

DOI: 10.2307/2490395

Google Scholar

[4] Altman E I, Marco G, Varettl F. Corporate distress diagnosis: Comparisons using linear discriminant analysis and neural networks [J]. Journal of Banking and Finance, 1994, 18(3): 505-529.

DOI: 10.1016/0378-4266(94)90007-8

Google Scholar

[5] Dan M C, Mark G R. A comparative analysis of current credit risk models [J]. Journal of Banking and Finance 2000, 24(1): 59-117.

Google Scholar

[6] Zhang Hua. On the personal loan business in the commercial banking risk prevention [J]. Financial Development, 2008 (8).

Google Scholar

[7] Tian Chunyan and so on. bank loan approval personal knowledge of the rule base refinement [J]. Management, 2006 (9).

Google Scholar

[8] Tan Chunhui. SVM commercial bank's credit risk assessment and control [J]. Information System Engineering, 2010(5).

Google Scholar