We take a data-driven approach to modelling consumer default risk in unsecured e-commerce lending scenarios. By drilling down to tick-level shopping behaviour in the financing records of a large population, we profile consumer credit at a granular level. Careful deciphering allows real-time assessment of future payment risk, particularly when payments are financed without posting collateral.

A typical cycle of credit-driven online shopping consists of three stages: browsing, placing an order and applying for payment credit, and paying instalments. Modelling challenges immediately arise because these high-dimensional records are massive samples exhibiting different degrees of serial-dependency at each event stage. They also show strong heterogeneity of mutual-dependencies across stages, and manifest a wide spectrum of event frequencies, ranging from browsing events hundreds of times a day, all the way to quarterly or semi-annual frequencies when periodical instalments are due.

One direction we plan to dive into is the construction of end-to-end deep learning models which fuse separate sequential neural networks, i.e. the Long-Short Term Memory model, to encode the microscopic action data separately for each of the three stages.