Analysis of Internet Finance Models and Risk Response Strategies in the Big Data Era

Abstract

The rapid proliferation of big data technologies has fundamentally reshaped the landscape of internet finance, giving rise to novel business models that challenge conventional financial paradigms. This paper systematically analyzes the principal internet finance models operating in the big data era—including peer-to-peer (P2P) lending, third-party payment platforms, crowdfunding, robo-advisory services, and supply chain finance—and evaluates their intrinsic risk structures. Employing a mixed-methods framework that integrates quantitative data analysis with qualitative case studies, we examine the principal categories of risk (credit risk, liquidity risk, operational risk, and systemic risk) associated with each model. Furthermore, we propose a multi-layered risk response strategy encompassing regulatory technology adoption, credit scoring innovation through machine learning, enhanced disclosure mechanisms, and coordinated supervisory frameworks. Our findings indicate that while big data analytics substantially improves risk identification and management efficiency, significant regulatory gaps and information asymmetry challenges persist. This research contributes a structured analytical framework to the growing body of literature on internet finance governance and offers actionable recommendations for regulators, platform operators, and investors.