Customer mobility signatures and financial indicators as predictors in product recommendation.

ABSTRACT: The rapid growth of mobile payment and geo-aware systems as well as the resulting emergence of Big Data present opportunities to explore individual consuming patterns across space and time. Here we analyze a one-year transaction dataset of a leading commercial bank to understand to what extent customer mobility behavior and financial indicators can predict the use of a target product, namely the Individual Consumer Loan product. After data preprocessing, we generate 13 datasets covering different time intervals and feature groups, and test combinations of 3 feature selection methods and 10 classification algorithms to determine, for each dataset, the best feature selection method and the most influential features, and the best classification algorithm. We observe the importance of spatio-t

SUBMITTER: Urkup C 

PROVIDER: S-EPMC6063431 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature