The online peer-to-peer (P2P) lending market, in which it is the practice of making unsecured microloans to other individual borrowers, is becoming more and more popular worldwide. In this market, information asymmetry between lenders and borrowers may create the adverse selection problem -- the lenders may fund sub-prime borrowers with high risk of defaulting. In this study, we propose a model to evaluate how performance – individual and group can be used as signals to reduce information asymmetry problems. We also propose these performance signals can interact with lenders risk attitude to affect funding success. Data from PaiPaiDai will be collected to evaluate our research model, along with our proposed research hypotheses.