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With the development of the Internet, peer-to-peer lending has become widely recognized and accepted by the public. P2P lending platforms are overgrowing around the world, which provides many conveniences to medium borrowers who have difficulty in traditional financial institutions. However, due to the qualification of P2P lending platforms and the lack of supervision, many of these platforms have no ability to disclose information to the authorities, which will increase industry risk and instability in the financial system. So, our work focuses on the missing data of P2P lending platforms from the perspective of predicting losing values. We highlight our work on dealing with missing values using Random Forest instead of traditional methods. After that, we applied the Gaussian Process Regression to explore the relationship between variables and got a conclusion that the loan balance of a particular platform can be predicted.

Probabilistic Regression and Missing Values Compensation for P2P Lending