Evaluation of borrower's credit of P2P loan based on adaptive particle swarm optimisation BP neural network

Personal credit assessment is the main method to reduce the credit risk of P2P online loans. In this paper, after the adaptive mutation operator is used to reinitialise the particles with a certain probability and the global search capability of the particle swarm optimisation algorithm is used to optimise the weights and thresholds of the BP neural network, a method which adopts adaptive mutation particle swarm combined with BP neural network model is proposed to evaluate borrower's credit of P2P network loan. Result of simulation experiment shows that AMPSO-BP neural network model has higher prediction accuracy, smaller error variation range, better fitting ability and robustness than the BP neural network model in P2P network loan borrower credit evaluation applications.