Prediction of potential credit card users of bank based on deep learning

In the post epidemic era and the rapid development of science and technology finance, bank credit card marketing has been greatly impacted. This paper proposes a new deep learning model DeepAFM (Deep Attentional Factorization Machine), which is used to predict potential credit card users of bank, so as to provide an effective basis for bank precision marketing. The model uses factorization machine and embedding layer to decompose the parameter matrix into low dimensional parameter matrix; The Attentional Mechanism is introduced to learn the weight of cross features and extract important features; A fully connected depth network is introduced to realize the mining of higher-order cross features. Finally, through the comparison with other algorithms, the results show that the expression ability of DeepAFM model is better and the automatic mining of important data is more accurate.