We study the duopoly credit competition between two BigTech firms that leverage the user data generated in their core services to conduct credit screening and personalize loan interest rate. Screening accuracy is determined by borrower-specific data availability and the capability of the screening model. We find that lending to a user yields a positive payoff only when the screening accuracy is above a dynamic threshold. Therefore, without sufficient user data, aggressive pricing to poach the rival's loyal users is unprofitable. Accordingly, users are segmented according to their screening accuracy by the two BigTechs. Each firm has one segment wherein it becomes the de facto monopolist. In the segment where both firms have sufficient data to conduct effective screening, price competition occurs at the individual user level, and higher predictive accuracy will confer competitive advantages. Overall, the firm whose screening technology is superior earns a higher market share and profit.
Lin, Yangyin; Ye, Qiang; and Xia, Hao, "Competition in Fintech Lending by BigTechs with Personalized Interest Rates" (2023). PACIS 2023 Proceedings. 77.
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