Enhancing investment decisions in P2P lending: an investor composition perspective

Authors:
Chunyu Luo;Hui Xiong;Wenjun Zhou;Yanhong Guo;Guishi Deng
Affiliations:
Dalian University of Technology, Dalian, China;Rutgers University, Newark, NJ, USA;University of Tennessee, Knoxville, TN, USA;Dalian University of Technology, Dalian, China;Dalian University of Technology, Dalian, China
Venue:
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Year:
2011

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Abstract

P2P lending, as a novel economic lending model, has imposed new challenges about how to make effective investment decisions. Indeed, a key challenge along this line is how to align the right information with the right people. For a long time, people have made tremendous efforts in establishing credit records for the borrowers. However, information from investors is still under-explored for improving investment decisions in P2P lending. To that end, we propose a data driven investment decision-making framework, which exploits the investor composition of each investment for enhancing decisions making in P2P lending. Specifically, we first build investor profiles based on quantitative analysis of past performances, risk preferences, and investment experiences of investors. Then, based on investor profiles, we develop an investor composition analysis model, which can be used to select valuable investments and improve the investment decisions. To validate the proposed model, we perform extensive experiments on the real-world data from the world's largest P2P lending marketplace. Experimental results reveal that investor composition can help us evaluate the profit potential of an investment and the decision model based on investor composition can help investors make better investment decisions.