Abstract
P2P online lending is an emerging economic lending model. In this marketplace, borrowers submit requests for loans, and lenders make bids on them. It has put forward new challenges to investors about how to make effective investment decisions. Bayesian network is a probabilistic graphical model that represents a set of random variables and their conditional dependencies. In the paper, we calculate the mutual information of every two variables to measure their mutual dependence and build a Bayesian network model to select loans that would pay back with high confidence. We perform abundant experiments on the data from the world’s largest P2P lending platform Prosper.com. Experimental results reveal that Bayesian network model can significantly help investors make better investment decisions than other investment models.
The work was supported National Natural Science Foundation of China (61202011 and 61272385).
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Wang, X., Zhang, D., Zeng, X., Wu, X. (2013). A Bayesian Investment Model for Online P2P Lending. In: Su, J., Zhao, B., Sun, Z., Wang, X., Wang, F., Xu, K. (eds) Frontiers in Internet Technologies. Communications in Computer and Information Science, vol 401. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-53959-6_3
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DOI: https://doi.org/10.1007/978-3-642-53959-6_3
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