Analysis and Optimization of Personal Credit Risk Assessment Model Based on Improved BPNN
- DOI
- 10.2991/ebmcsr-18.2018.13How to use a DOI?
- Keywords
- Personal credit risk assessment, 5C evaluation method, Time stamp, BPNN
- Abstract
In the current situation of “Internet plus Finance”, the assessment of personal credit risk is of vital importance to the sustained and steady growth of the whole social economy. In order to more effectively complete the personal credit risk assessment, the innovation of this paper lies in the introduction of time stamp into the 5C evaluation method, and then using the artificial intelligence attribute (crossover factor and mutation factor) of Back Propagation Neural Network(BPNN) to build a personal credit risk assessment analysis. With the dynamic development of thinking, this model constantly adjusts and optimizes the personal credit risk assessment process. Simulation experiment shows that this integrated innovative approach not only reduces the error rate value of personal credit risk assessment, but also improves the efficiency of personal credit assessment.
- Copyright
- © 2018, the Authors. Published by Atlantis Press.
- Open Access
- This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Cite this article
TY - CONF AU - Zheyou Guo PY - 2018/11 DA - 2018/11 TI - Analysis and Optimization of Personal Credit Risk Assessment Model Based on Improved BPNN BT - Proceedings of the 2018 International Conference on Economics, Business, Management and Corporate Social Responsibility (EBMCSR 2018) PB - Atlantis Press SP - 66 EP - 72 SN - 2352-5428 UR - https://doi.org/10.2991/ebmcsr-18.2018.13 DO - 10.2991/ebmcsr-18.2018.13 ID - Guo2018/11 ER -