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

TL;DR

This paper introduces HUIHEN, a hierarchical neural network that leverages mobile banking user behavior data to improve credit loan overdue risk detection without complicating the application process.

Contribution

The paper proposes a novel hierarchical model combining time-aware and user-item-aware GRUs to effectively extract user intentions and habits from behavior sequences.

Findings

HUIHEN outperforms existing models on all datasets.

The model improves risk detection accuracy without increasing application complexity.

Behavior session division enhances feature extraction.

Abstract

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.