Potential User Prediction for Financial APP Based on Random Forest Model

In recent years, mobile applications have brought great convenience to people's lives. Most financial Apps need to bind user credit cards. Thus, if historical transaction records could be effectively used to predict the potential user groups of Apps that may be tied to credit cards in the future, financial institutions can take personalized recommendation and accurate marketing promotion for this part of potential users, which helps to expand their market share of mobile applications and increase corporate profits. Therefore, this paper proposes a method for predicting potential users of financial apps based on the random forest model, and confirms that this model has better classification prediction effects and higher accuracy when dealing with the problem of predicting potential users of financial apps.