Application Paths of Semantic Modeling in Financial Fraud Detection and Risk Identification
Keywords:
Semantic Modeling, Financial Fraud Detection, Risk Identification, Knowledge Graph, Semantic Rules, Artificial Intelligence, Machine LearningAbstract
Financial fraud and risk pose significant threats to economic stability and individual well-being. Traditional detection methods often struggle to keep pace with increasingly sophisticated fraudulent schemes. Semantic modeling, which focuses on understanding the meaning and relationships within data, offers a promising avenue for enhancing fraud detection and risk identification. This review paper explores the application paths of semantic modeling in this domain. We begin with a historical overview of fraud detection techniques, highlighting the limitations of traditional approaches. Subsequently, we delve into core themes, including knowledge graph-based fraud detection and semantic rule-based inference for risk assessment. We then compare and contrast different semantic modeling approaches, addressing key challenges such as data heterogeneity and scalability. Furthermore, we discuss future perspectives, focusing on the integration of semantic modeling with emerging technologies like explainable AI and federated learning. The review synthesizes findings from various studies, providing a comprehensive understanding of the current state and future directions of semantic modeling in financial fraud detection and risk identification. This review aims to guide researchers and practitioners in leveraging semantic modeling to build more robust and effective fraud detection systems. We explore current limitations and highlight opportunities for future research in this rapidly evolving field, ensuring advancements in both prevention and detection methodologies can keep pace with ever-evolving and dynamic threats.References
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