  Credit investigation is critical for financial services. Whereas, traditional
methods are often restricted as the employed data hardly provide sufficient,
timely and reliable information. With the prevalence of smart mobile devices,
peoples' geographic footprints can be automatically and constantly collected
nowadays, which provides an unprecedented opportunity for credit
investigations. Inspired by the observation that locations are somehow related
to peoples' credit level, this research aims to enhance credit investigation
with users' geographic footprints. To this end, a two-stage credit
investigation framework is designed, namely CreditPrint. In the first stage,
CreditPrint explores regions' credit characteristics and learns a credit-aware
embedding for each region by considering both each region's individual
characteristics and cross-region relationships with graph convolutional
networks. In the second stage, a hierarchical attention-based credit assessment
network is proposed to aggregate the credit indications from a user's multiple
trajectories covering diverse regions. The results on real-life user mobility
datasets show that CreditPrint can increase the credit investigation accuracy
by up to 10% compared to baseline methods.
