The paper researchs on the intelligent problem of guarantee system. For traditional guarantee system, because of low efficiency in risk control and mining customer value, increased risk of Guarantee Corporation oans and too long approval cycle results in serious loss of customers. To solve the above problem, put forward a new risk control method based on rough set neural network mode, and use Analytic Hierarchy Process and Activity Based Classification model to achieve customer segmentation. To do simulation with the sample data of 2005-2015 offered by Shenzhen Surety Association. The rough set and Back Propagation to be used in control risk, and the credit approval time is significantly reduce, and with the Analytic Hierarchy Process and Activity Based Classification model is to be achieved customer segmentation, that result in the company profit significantly increases. So the technique presented effectively in this paper.
Risk Control, Rough Set, Neural Network, Analytic Hierarchy Process.
1. Lina Wu. Design and Implementation of Credit Guarantee Comprehensive Management System Based on Java. JILIN, School of Computer Science and Technology. vol. 23, no. 2, pp. 2-13, 2016.
2. C. Feng, J. Liang, Solve the more general travelling salesman problem [J]. AMSE JOURNALS –2014-Series: Modelling D. vol. 35, no. 1 pp 19-23, 2014.
3. C. Feng, J. Liang, The solution of the more general traveling salesman [J]. AMSE JOURNALS –2014-Series: Advances A, vol. 51, no. 1, pp 27-40, 2014.
4. Hongyuan Wang. Design and implementation of experimental system of logistics information management platform based on B/S structure [C]. Changchun China, 2016.
5. Yang Zhao, Lin Wang, Nan Gao. The improvement of CRM in social networking: A case study of Alibaba & Sina. International Society for information and Engineering [C], Tianjin China, 2015.
6. Jing Yang, Chao Lei. Research of Business Intelligence Technology Applications on CRM[J]. Journal of Beijing Union University, Vol 29, 2 PP47-50, 2015.
7. Lihua Zhan. An AHP Based Evaluation Index System of FRBR Applied to Network Information Organization[J]. INFORMATION SCIENCE, vol. 34, no. 4, pp. 55-58, 2016.
8. Wei Zhang；Application on Real Estate Price Prediction Based on RS and PBNN[J]. Computer simulation, vol. 28, no. 7, PP365-368, 2011.
9. Jie Duan, Qinghua Hu, Lingjun Zhang. Feature Selection for Multi-Label Classification Based on Neighborhood Rough Sets[J]. Journal of Computer Research and Development, vol. 52, no. 1, pp. 56-65, 2015.
10. Wensheng Yu. Aircraft Hydraulic Fluids Soft Measurement Based on Improved[J].Computer Measurement & Control, vol. 24, no. 3, PP21-24, 2016.
11. Yan Wang, YucHun Pan, Hui Wang. Based on voronoi and information entropy spatial outliers detection algorithms[J]. Computer Engineering and Design. vol. 31, no. 18, pp. 135-143, 2010.
12. QingHua Zhang. YuBin Xue, GuoYin Wang. Optimal Approximation Sets of Rough Sets[J]. Journal of Software. vol. 27, no. 1 pp. 295-308, 2016.
13. Qing hua Zhang, Yubin XUE, Feng U. Research on Uncertainty of Approximation Set of Rough Set [J], Actaelect Ronica Sinca. vol. 44, no. 7, pp. 1574-1579, 2016.