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Tricking account overdraft fee refers to the behavior of the users who utilize the overdraft limit to purchase goods but no longer recharge the account. In order to solve the problem that some mobile communication users have a tricking account overdraft fee behavior which lead to bad debts of telecom operators, a risk prediction model based on the combination of Logistic and GBDT is proposed. The fusion model uses Logistic Regression transformation to map the function value to the interval from 0 to 1, and the mapped value was used as the risk probability value for prediction; GBDT is used to discover features with multiple degrees of differentiation and combine effective features to enhance the feature dimension. The combination features were extracted from the original data, which made up for Logistic Regression's poor capture of feature combinations and improved the predictive ability of this model. The experiment based on real mobile communication user data of an operator shows that the proposed fusion model has a good prediction effect. © 2019 IEEE.
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