Internet loan business has received extensive attentions recently. How to provide lenders with accurate credit scoring profiles of borrowers becomes a challenge due to the tremendous amount of loan requests and the limited information of borrowers. However, existing approaches are not suitable to Internet loan business due to the unique features of individual credit data. In this paper, we propose a unified data mining framework consisting of feature transformation, feature selection and hybrid model to solve the above challenges. Extensive experiment results on realistic datasets show that our proposed framework is an effective solution.
|Title of host publication||Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST|
|Publisher||Springer-Verlag GmbH and Co. KG|
|Number of pages||11|
|Publication status||Published - Jul 2017|
|Name||Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST|
Bibliographical noteFunding Information:
The work described in this paper was supported by the National Key Research and Development Program (2016YFB1000101), the National Natural Science Foundation of China under (61472338), the Fundamental Research Funds for the Central Universities, and Macao Science and Technology Development Fund under Grant No. 096/2013/A3.
© 2017, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.
- Credit evaluation
- Data mining
- Internet finance