Name
Abstract | ||
|---|---|---|
At present, with continuously expanding of Chinese credit market, thus large amounts of P2P (person-to-person borrow or lend money in Internet Finance) platform were born and have been in development. Most of P2P platform in China carries out the credit risk evaluation of loan applicant by data mining method. As an emerging data mining tool, the artificial neural network has better classification capability. The improvement of risk assessment capabilities of applicant can effectively reduce the overdue rate of analysis, thus in this paper, a kind of credit risk evaluation model based on the Long Short-Term Memory (LSTM) model is presented. The sample data of overdue and non-overdue credits are provided by Hengxin Investment Consulting Co., Ltd. in Jinan, by which the model is established. After the trial, this model is applied to the aspect of overdue classification of credit evaluation with higher accuracy. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1007/978-3-319-63312-1_62 | INTELLIGENT COMPUTING THEORIES AND APPLICATION, ICIC 2017, PT II |
Keywords | Field | DocType |
Artificial neural network, Credit risk assessment, Long Short-Term memory | Credit risk assessment,Loan,Actuarial science,Computer science,Long short term memory,Risk assessment,Bond market,Artificial neural network,Credit risk,The Internet | Conference |
Volume | ISSN | Citations |
10362 | 0302-9743 | 1 |
PageRank | References | Authors |
0.37 | 8 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yishen Zhang | 1 | 1 | 0.71 |
| Dong Wang | 2 | 1 | 2.40 |
| Yuehui Chen | 3 | 1167 | 106.13 |
| Huijie Shang | 4 | 1 | 0.71 |
| Qi Tian | 5 | 1 | 0.37 |