Using a unique data set obtained from a Chinese fintech lending company, this paper categorizes the information used for loan default prediction into four types: Self-reported information, central bank credit information, e-commerce credit score, and online shopping behaviors. The roles played by the two types of digital footprints, e-commerce credit score and online shopping behaviors, in predicting loan default are investigated and the underlying mechanisms through which digital footprints improve 〖JP2〗credit availability are explored. Our results show that the digital footprints can help lenders to improve the accuracy in predicting a borrower’s〖JP〗 default likelihood by about 50〖WTXT〗%〖WTBZ〗. The prediction power of the e-commerce credit score is significantly the highest among the four types of information. Nevertheless, online shopping behaviors can not only provide additionally useful information beyond the e-commerce credit score, but also perform as well as the self-reported information disclosed by borrowers. Furthermore, the preliminary evidence on the mechanisms through which digital footprints improve credit availability shows that e-commerce credit score and online shopping behaviors can be used to establish credit scores for “unbanked customers” and accurately evaluate the credit worthiness of borrowers whose credit quality is always wrongly underestimated.