The most common problem in the financial service industry is the risk problem. One of the most crucial risk problems is default risk. Nowadays, the users of peer-to-peer (P2P) lending companies are growing rapidly; however, the companies have not prepared an adequate system to assess them. Furthermore, the company must have a different assessment model for each other. One assessment model which is currently used by the companies is 5C Credit Analysis. Through this research, the author constructs a credit scoring model which is based on the historical data to be implemented in a P2P lending company. The data used come from a P2P lending company in Bandung, Indonesia. The author chooses the Credit Risk Scorecard Model to predict the customer’s creditworthiness assessment. The result indicates the model constructed is better than 5C Credit Analysis. It is shown by the Pearson correlation value, where the model generates higher significant value than 5C's Credit Analysis. Moreover, the model is tested using cross-validation test and resulted the optimum is occurred in the proper cut-off value of 0.235 with the accuracy rate, sensitivity rate, and misclassification rate, respectively as for 69.84%, 77.27%, and 28.24%. Moreover, the result is almost similar to the previous study which is used as the literature in this research.