A Hierarchical User Intention-Habit Extract Network for Credit Loan Overdue Risk Detection [article]

Hao Guo, Xintao Ren, Rongrong Wang, Zhun Cai, Kai Shuang, Yue Sun
<span title="2020-08-18">2020</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
More personal consumer loan products are emerging in mobile banking APP. For ease of use, application process is always simple, which means that few application information is requested for user to fill when applying for a loan, which is not conducive to construct users' credit profile. Thus, the simple application process brings huge challenges to the overdue risk detection, as higher overdue rate will result in greater economic losses to the bank. In this paper, we propose a model named
more &raquo; ... (Hierarchical User Intention-Habit Extract Network) that leverages the users' behavior information in mobile banking APP. Due to the diversity of users' behaviors, we divide behavior sequences into sessions according to the time interval, and use the field-aware method to extract the intra-field information of behaviors. Then, we propose a hierarchical network composed of time-aware GRU and user-item-aware GRU to capture users' short-term intentions and users' long-term habits, which can be regarded as a supplement to user profile. The proposed model can improve the accuracy without increasing the complexity of the original online application process. Experimental results demonstrate the superiority of HUIHEN and show that HUIHEN outperforms other state-of-art models on all datasets.
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2008.07796v1">arXiv:2008.07796v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/rr4ej4u2orf7lbkzxx74j5ax2u">fatcat:rr4ej4u2orf7lbkzxx74j5ax2u</a> </span>
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