KEYWORDS: Data modeling, Statistical modeling, Internet, Data mining, Computer simulations, Social sciences, Machine learning, Information technology, Data processing, Stochastic processes
Currently there is a growing concern over the issue of peer-to-peer (P2P) lending. A key challenge for personal investors in P2P lending marketplaces is how to accurately identify the subject of loan funds and how to effectively evaluate the profit and risk of the subject in the context of lending success.In this paper, we use the nuclear regression model to evaluate the probability of successful lending, to provide effective frontier for investors, and to give the optimal combination of the recommended bids for the lenders under different risk preferences.Finally we verify the scheme with data from Paipai Lending, the largest P2P network lending website in China. Experimental results reveals that the scheme can effectively provide investors more investment options.
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