TY - GEN
T1 - An Effective Model Between Mobile Phone Usage and P2P Default Behavior
AU - Liu, Huan
AU - Ma, Lin
AU - Zhao, Xi
AU - Zou, Jianhua
N1 - Publisher Copyright:
© Springer International Publishing AG, part of Springer Nature 2018.
PY - 2018
Y1 - 2018
N2 - P2P online lending platforms have become increasingly developed. However, these platforms may suffer a serious loss caused by default behaviors of borrowers. In this paper, we present an effective default behavior prediction model to reduce default risk in P2P lending. The proposed model uses mobile phone usage data, which are generated from widely used mobile phones. We extract features from five aspects, including consumption, social network, mobility, socioeconomic, and individual attribute. Based on these features, we propose a joint decision model, which makes a default risk judgment through combining Random Forests with Light Gradient Boosting Machine. Validated by a real-world dataset collected by a mobile carrier and a P2P lending company in China, the proposed model not only demonstrates satisfactory performance on the evaluation metrics but also outperforms the existing methods in this area. Based on these results, the proposed model implies the high feasibility and potential to be adopted in real-world P2P online lending platforms.
AB - P2P online lending platforms have become increasingly developed. However, these platforms may suffer a serious loss caused by default behaviors of borrowers. In this paper, we present an effective default behavior prediction model to reduce default risk in P2P lending. The proposed model uses mobile phone usage data, which are generated from widely used mobile phones. We extract features from five aspects, including consumption, social network, mobility, socioeconomic, and individual attribute. Based on these features, we propose a joint decision model, which makes a default risk judgment through combining Random Forests with Light Gradient Boosting Machine. Validated by a real-world dataset collected by a mobile carrier and a P2P lending company in China, the proposed model not only demonstrates satisfactory performance on the evaluation metrics but also outperforms the existing methods in this area. Based on these results, the proposed model implies the high feasibility and potential to be adopted in real-world P2P online lending platforms.
KW - Joint decision model
KW - Mobile phone usage
KW - P2P default behavior Prediction
UR - https://www.scopus.com/pages/publications/85049063317
U2 - 10.1007/978-3-319-93701-4_36
DO - 10.1007/978-3-319-93701-4_36
M3 - 会议稿件
AN - SCOPUS:85049063317
SN - 9783319937007
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 462
EP - 475
BT - Computational Science – ICCS 2018 - 18th International Conference, Proceedings
A2 - Krzhizhanovskaya, Valeria V.
A2 - Lees, Michael Harold
A2 - Sloot, Peter M.
A2 - Dongarra, Jack
A2 - Shi, Yong
A2 - Tian, Yingjie
A2 - Fu, Haohuan
PB - Springer Verlag
T2 - 18th International Conference on Computational Science, ICCS 2018
Y2 - 11 June 2018 through 13 June 2018
ER -