Develop and Refine Fraud Detection Techniques in P2P Lending: The research aims to explore and implement advanced data-driven techniques for detecting fraud in peer-to-peer lending platforms. This involves the application of machine learning algorithms and predictive models to analyze large datasets for anomalies that indicate potential fraud, thus enhancing the platforms' ability to manage risks and protect users.
Establish a Unified Definition of Fraud in P2P Lending: A key goal is to establish a clear and standardized definition of fraud specific to the P2P lending context. This effort addresses the current inconsistencies in fraud identification and research, facilitating the development of more effective detection algorithms and improving the comparability of research outcomes across different studies and platforms.
Assess the Social and Economic Impacts of Fraud Detection Systems: The study seeks to delve into the broader social and economic implications of implementing robust fraud detection systems in P2P lending. It examines how these systems affect the trustworthiness and operational resilience of lending platforms, thus contributing to a safer and more regulatory-compliant financial environment for both lenders and borrowers.