A Hierarchical User Intention-Habit Extract Network for Credit Loan Overdue Risk Detection
This addresses a domain-specific problem for banks by reducing economic losses from loan defaults through better risk prediction, but it is incremental as it builds on existing behavior analysis methods.
The paper tackles credit loan overdue risk detection by proposing HUIHEN, a model that uses user behavior data from mobile banking apps to supplement sparse application information, improving accuracy without complicating the application process. Experimental results show it outperforms state-of-the-art models on all datasets.
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 HUIHEN (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.