Neural Learning of Online Consumer Credit Risk
This addresses credit risk assessment for e-commerce platforms issuing unsecured credit, though it is incremental as it applies deep learning to a specific domain with new data.
The paper tackles consumer credit risk prediction for e-commerce platforms by developing the 'NeuCredit' deep learning model, which captures serial dependences and nonlinear interactions in time-series data and decomposes risk into interpretable components, showing significant performance gains over traditional methods.
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.