English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Zendy Plus

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Zendy Tools

Zendy Open

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

Neural Learning of Online Consumer Credit Risk