Abstract
P2P lending platform contains many risks of loan default. The top priority of P2P lending platform is to predict the risk of default accurately and to take corresponding measures. The paper selects public lending loan transaction data of users from Lending Club to carry on the empirical study on the risks of loan default by respectively using logistic regression model, the bat optimization algorithm of feedforward(BAPA) neural network and least squares support vector machine(LSSVM) to analyze the experimental data, and then to evaluate the applicability of three methods in predicting the risk of loan default on the P2P network platform. The experimental results show that the least squares support vector machine has a better prediction effect.
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