This book focuses on the development of an anti-fraud model designed to detect both individual and group fraud in the internet finance sector. It further proposes a comprehensive risk control system tailored for online credit platforms, aiming to enhance the industry's overall risk management capabilities. Centered around two core themes: credit scoring and fraud detection, the book utilizes advanced algorithms including machine learning, deep learning, and graphical models to study patterns of financial fraud online.Unlike traditional research that mainly depends on financial and operational data, which is often delayed or manipulated, this monograph leverages abundant, real-time user evaluation data from internet finance platforms. By constructing a specialized Internet finance dictionary, it identifies key evaluation themes and emotional trends, laying the foundation for a platform public opinion monitoring and early warning system to enable timely detection of high-risk platforms.Each internet finance platform is treated as a research node, contributing to the creation of an interconnected internet finance platform network. Through network analysis, complex network theory, and public opinion text mining, the book develops both correlation and measurement models. These models explore how information dissemination influences investment and lending behaviors, investigate the relationship between cross-platform information propagation and platform risk, and analyze the dynamics of risk contagion across platforms.Finally, the book proposes actionable risk mitigation strategies to address the cumulative risks observed in the industry. It offers practical insights and policy guidance for regulators and stakeholders seeking to prevent and resolve Internet financial risks.
