  This paper takes a deep learning approach to understand consumer credit risk
when e-commerce platforms issue unsecured credit to finance customers'
purchase. The "NeuCredit" model can capture both serial dependences in
multi-dimensional time series data when event frequencies in each dimension
differ. It also captures nonlinear cross-sectional interactions among different
time-evolving features. Also, the predicted default probability is designed to
be interpretable such that risks can be decomposed into three components: the
subjective risk indicating the consumers' willingness to repay, the objective
risk indicating their ability to repay, and the behavioral risk indicating
consumers' behavioral differences. Using a unique dataset from one of the
largest global e-commerce platforms, we show that the inclusion of shopping
behavioral data, besides conventional payment records, requires a deep learning
approach to extract the information content of these data, which turns out
significantly enhancing forecasting performance than the traditional machine
learning methods.
