In the traditional Harsanyi transformation, the virtual player “Nature” assigns the type of a real player according to the historical data with a certain probability distribution. However, sometimes it is difficult to obtain historical data. Therefore, this paper proposes a FA-XGBoost model to predict the probability distribution of a real player’s type based on a large number of relevant data. In order to eliminate the increase of the computational complexity caused by the large data dimension, factor analysis is firstly utilized to decrease the data dimension, and then the XGBoost algorithm is used to learn the probability distribution of real player’s type. “Nature” finally allocates the player’s type according to the predicted probability distribution. This paper makes an empirical analysis based on the real loan data of Lending Club. The results show that the method can well guide the decision-making of P2P loan enterprises.