HPO-LGBM-DRI: Dynamic Recognition Interval Estimation for Imbalanced Fraud Call via HPO-LGBM

The prevention and crackdown of fraud calls have been paid more and more attention by industrial and academic societies. Most current researches based on machine learning ignore the imbalanced data distribution characteristic between normal and fraudulent call users, and the outputs neglect the probability fluctuation range of the suspected fraudulent calls. To overcome these limitations, we first construct user behavioral feature vector by a random forest method. Secondly, we propose a novel hierarchical sampling method to overcome the class imbalance problem. Thirdly, we propose a novel fraud call recognition method based on HPO-LGBM (the Bayesian hyper parameter optimization based on random forest and Light Gradient Boosting Machine). Finally, we further evaluate the method’s performance with a DRI (dynamic recognition interval) model. Experimental results on public datasets show that the proposed HPO-LGBM holds a 92.90% F1 value, a 91.90% AUC, a 92.92% G-means, and a 92.37% MCC in fraud call recognition. In addition, the proposed HPO-LGBM model can further give the dynamic recognition interval of the output result, behaving more robust than other models (i.e., LR, RF, MLP, GBDT, XGBOOST, LGBM).