P2P lending fraud detection: a big data approach

Abstract

P2P lending directly connects borrowers and lenders without a financial institution as the intermediary. This new form of crowdfunding brings lenders more investment opportunities, but also poses unprecedented risks of default and fraud. This research-in-progress paper focuses on a specific type of fraud, loan request fraud, which may be unique to lenders on Chinese P2P lending sites due to the lack of nationwide credit rating systems in China. We propose research questions surrounding the problem of loan request fraud (its types, features, and detection methods) and present our research methodology and project plans. Specifically, we plan to develop data mining based methods and employ a big data approach to address our research questions. With the help of large volumes of data from a variety of sources, we will be able to find ways to leverage rich datasets about user behaviors and transaction histories to detect loan request fraud more effectively and efficiently.