Competition and Distortion : A Theory of Information Bias on the Peer-to-Peer Lending Market

Despite the popular emergence of peer-to-peer (P2P) lending platforms, relevant research investigating the role of these platforms on P2P markets still lags. In this paper, we present a model to study the market incentives of P2P lending platforms' optimal information-reporting strategies when the following exist: (i) uncertainty on the return of loans and (ii) competition from entrants. We assume platforms on the market are rational players, and they seek to stay in the market as long as possible as accurate information providers. We characterize platforms' equilibrium reporting strategies under different market conditions. Surprisingly, we find that when uncertainty is significant, and the entry of threat is strong but not detrimental, the platform has incentives to bias information toward investors' biased beliefs. This result demonstrates a case where competition and uncertainty may jointly lead to information bias. However, a properly designed uncertainty-resolution mechanism could reduce the incentive. Our findings contribute to the literature on the P2P lending market by analyzing platform decisions, and offer policy implications for regulating P2P lending market