Default prediction in P2P lending from high-dimensional data based on machine learning

Jing Zhou, Wei Li, Jiaxin Wang, Shuai Ding and Chengyi Xia

Physica A: Statistical Mechanics and its Applications, 2019, vol. 534, issue C

Abstract: In recent years, a new Internet-based unsecured credit model, peer-to-peer (P2P) lending, is flourishing and has become a successful complement to the traditional credit business. However, credit risk remains inevitable. A key challenge is creating a default prediction model that can effectively and accurately predict the default probability of each loan for a P2P lending platform. Due to the characteristics of P2P lending credit data, such as high dimension and class imbalance, conventional statistical models and machine learning algorithms cannot effectively and accurately predict default probability. To address this issue, a decision tree model-based heterogeneous ensemble default prediction model is proposed in this paper for accurate prediction of customer default in P2P lending. Gradient boosting decision trees (GBDT), extreme gradient boosting (XGBoost) and light gradient boosting machine (LightGBM) are employed as individual classifiers to create a heterogeneous ensemble learning-based default prediction model. Learning model-based feature ranking is applied to P2P lending credit data, and individual classifiers undergo hyperparameter optimization. Finally, comparison with benchmark models shows that the prediction model can achieve desirable prediction results and thus effectively solve the challenge of predictions based on high-dimensional and imbalanced data.

Keywords: Default prediction; High-dimensional data; Imbalanced data; Machine learning; P2P lending (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (14)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0378437119313652
Full text for ScienceDirect subscribers only. Journal offers the option of making the article available online on Science direct for a fee of $3,000

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:534:y:2019:i:c:s0378437119313652

DOI: 10.1016/j.physa.2019.122370

Access Statistics for this article

Physica A: Statistical Mechanics and its Applications is currently edited by K. A. Dawson, J. O. Indekeu, H.E. Stanley and C. Tsallis

More articles in Physica A: Statistical Mechanics and its Applications from Elsevier
Bibliographic data for series maintained by Catherine Liu ().