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International Conference on Emerging Internetworking, Data & Web Technologies

EIDWT 2017: Advances in Internetworking, Data & Web Technologies pp 22–33Cite as

Credit Risk Assessment of Peer-to-Peer Lending Borrower Utilizing BP Neural Network

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Part of the book series: Lecture Notes on Data Engineering and Communications Technologies ((LNDECT,volume 6))

Abstract

This paper proposes an innovated approach of risk assessment of borrowers based on the BP neutral network model. Specifically, firstly, referring to the empirical data published by the website ‘peer-to-peer lender’ and the indicators of personal credit risk assessment from commercial bank is an efficient method to pick several valid values through data processing, classification and quantification, then the final modeling indicators are selected by information gain technology. Secondly, the new credit risk assessment model is formed after training the modeling indicators. Meanwhile, several strings of collected testing data would be substituted to find out the default rates which are supposed to be compared with the practical ones on the website and the calculated ones from existing credit risk assessment evaluating models. Last but not the least, the effect of this new method is evaluated.

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References

  1. Chen, Y.F., Wu, C.J.: Influence of website design on consumer emotion and purchase intention in travel websites. Int. J. Technol. Hum. Interact. (IJTHI) 12(4), 15–29 (2016)

    Article  MathSciNet  Google Scholar 

  2. Sula, A., Spaho, E., Matsuo, K., et al.: A new system for supporting children with autism spectrum disorder based on IoT and P2P technology. Int. J. Space-Based Situated Comput. 4(1), 55–64 (2014)

    Article  Google Scholar 

  3. Di Stefano, A., Morana, G., Zito, D.: QoS-aware services composition in P2PGrid environments. Int. J. Grid Util. Comput. 2(2), 139–147 (2011)

    Article  Google Scholar 

  4. Sawamura, S., Barolli, A., Aikebaier, A., et al.: Design and evaluation of algorithms for obtaining objective trustworthiness on acquaintances in P2P overlay networks. Int. J. Grid Util. Comput. 2(3), 196–203 (2011)

    Article  Google Scholar 

  5. Takeda, A., Oide, T., Takahashi, A.: Simple dynamic load balancing mechanism for structured P2P network and its evaluation. Int. J. Grid Util. Comput. 3(2–3), 126–135 (2012)

    Article  Google Scholar 

  6. Eftychiou, A., Vrusias, B., Antonopoulos, N.: A dynamically semantic platform for efficient information retrieval in P2P networks. Int. J. Grid Util. Comput. 3(4), 271–283 (2012)

    Article  Google Scholar 

  7. Higashino, M., Hayakawa, T., Takahashi, K., et al.: Management of streaming multimedia content using mobile agent technology on pure P2P-based distributed e-learning system. Int. J. Grid Util. Comput. 5(3), 198–204 (2014)

    Article  Google Scholar 

  8. Holyoak, K.J.: Parallel distributed processing: explorations in the microstructure of cognition. Science 236, 992–997 (1987)

    Article  Google Scholar 

  9. Rochester, N., Holland, J., Haibt, L., et al.: Tests on a cell assembly theory of the action of the brain, using a large digital computer. IRE Trans. Inf. Theory 2(3), 80–93 (1956)

    Article  Google Scholar 

  10. Hoskins, J.C., Himmelblau, D.M.: Process control via artificial neural networks and reinforcement learning. Comput. Chem. Eng. 16(4), 241–251 (1992)

    Article  Google Scholar 

  11. Ciresan, D., Giusti, A., Gambardella, L.M., et al.: Deep neural networks segment neuronal membranes in electron microscopy images. In: Advances in Neural Information Processing Systems, pp. 2843–2851 (2012)

    Google Scholar 

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Acknowledgments

This work is financially supported by the National Natural Science Foundation of P.R. China (No. 61373017, No. 61572260, No. 61572261, No. 61672296, No. 61602261), the Natural Science Foundation of Jiangsu Province (No. BK20140886, No. BK20140888), Scientific & Technological Support Project of Jiangsu Province (No. BE2015702, BE2016185, No. BE2016777), Natural Science Key Fund for Colleges and Universities in Jiangsu Province (No. 12KJA520002), China Postdoctoral Science Foundation (No. 2014M551636, No. 2014M561696), Jiangsu Planned Projects for Postdoctoral Research Funds (No. 1302090B, No. 1401005B), Jiangsu Postgraduate Scientific Research and Innovation Projects (SJLX16_0326), Project of Jiangsu High Technology Research Key Laboratory for Wireless Sensor Networks (WSNLBZY201509), NUPTSF (Grant No. NY214060, No. NY214061) and the STITP projects of NUPT (No. XZD2016032 and No. XYB2016532).

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Authors and Affiliations

  1. Glasgow College, UESTC, University of Electronic Science and Technology of China, Chengdu, China

    Zhengnan Yuan

  2. School of Computer, Nanjing University of Posts and Telecommunications, Nanjing, China

    Zihao Wang & He Xu

  3. Jiangsu High Technology Research Key Laboratory for Wireless Sensor Networks, Nanjing, China

    He Xu

Authors
  1. Zhengnan Yuan
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  2. Zihao Wang
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  3. He Xu
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Corresponding author

Correspondence to He Xu .

Editor information

Editors and Affiliations

  1. Fukuoka Institute of Technology, Fukuoka, Japan

    Leonard Barolli

  2. School of Computer Sciences, Hubei University of Technology, Wuhan, China

    Mingwu Zhang

  3. Department of Electronic Technology, Key, Engineering University of CAPF, Xi’an, Xizang, China

    Xu An Wang

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Yuan, Z., Wang, Z., Xu, H. (2018). Credit Risk Assessment of Peer-to-Peer Lending Borrower Utilizing BP Neural Network. In: Barolli, L., Zhang, M., Wang, X. (eds) Advances in Internetworking, Data & Web Technologies. EIDWT 2017. Lecture Notes on Data Engineering and Communications Technologies, vol 6. Springer, Cham. https://doi.org/10.1007/978-3-319-59463-7_3

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  • DOI: https://doi.org/10.1007/978-3-319-59463-7_3

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-59462-0

  • Online ISBN: 978-3-319-59463-7

  • eBook Packages: EngineeringEngineering (R0)

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