Anatomy of Peer-to-Peer (P2P) Lending Fraud: A Review with Managerial Implications

Peer-to-peer (P2P) lending platforms have transformed the financial technology sector by facilitating direct interactions between borrowers and lenders, eliminating the need for traditional financial intermediaries. While this innovation enhances financial inclusion and efficiency, it is subject to significant fraud risks such as identity theft, misrepresentation, predatory lending, and Ponzi schemes. These fraudulent activities undermine platform integrity and erode user trust. This paper presents a comprehensive systematic literature review of fraud detection strategies in P2P lending platforms, emphasizing the role of advanced analytics and machine learning (ML) models in effectively identifying and preventing fraud. The study addresses key research questions, including the definition of fraud within the P2P lending context, the human dynamics influencing fraudulent behavior, and the ML and AI techniques suited for fraud prevention. Our findings highlight the importance of real-time data analysis, continuous updating of detection models, and the integration of behavioral insights to effectively mitigate fraud risks. By synthesizing current methodologies and technologies, the paper contributes to both theoretical advancements and practical applications in fraud detection. It underscores the need for robust, adaptable frameworks that ensure a secure lending environment, enhance platform sustainability, and strengthen user confidence in P2P lending systems.