Big data pricing in marketplace lending and price discrimination against repeat borrowers: Evidence from China

In this work, we systematically investigate the pricing mechanism change from auction to big data pricing on one of the major marketplace lending platforms in China. We find that big data pricing reduces the average interest rate while the borrowers with delinquency or default his-tories are assigned higher interest rates. However, repeat borrowers are also faced with growing interest rates, even though they have been paying their debts on time. Further analysis shows that repeat borrowers have lower income and education levels. Moreover, investor returns become less dispersed after pricing with big data, which can be a result of homogeneous loans on the market. The implications of the above findings are discussed.

Automated summary

In this study, we examined the transition from auction-based pricing to big data pricing on a major marketplace lending platform in China. Our findings indicate that big data pricing lowers the average interest rate, but borrowers with delinquency or default histories are charged higher rates. Interestingly, even repeat borrowers who have been consistently repaying their loans experience increasing interest rates. Additional analysis reveals that repeat borrowers tend to have lower income and education levels. Furthermore, investor returns become less dispersed after the implementation of big data pricing, possibly due to the presence of more homogeneous loans in the market. The implications of these findings are discussed.